You can find a list of completed Phd theses here.
2021 |
Mousavinezhad, Najmeh Knowledge Extraction Methods for the Analysis of Contractual Agreements PhD Thesis Rheinische Friedrich-Wilhelms-Universität Bonn, 2021. @phdthesis{, title = {Knowledge Extraction Methods for the Analysis of Contractual Agreements}, author = {Najmeh Mousavinezhad}, year = {2021}, date = {2021-05-31}, school = {Rheinische Friedrich-Wilhelms-Universität Bonn}, abstract = {The ubiquitous availability of the Internet results in a massive number of apps, software, and online services with accompanying contractual agreements in the form of end-user license agreement' andprivacy policy’. Often the textual documents describing rights, policies, and conditions comprise many pages and can not be reasonably assumed to be read and understood by humans. Although everyone is exposed to such consent forms, the majority tend to ignore them due to their length and complexity. However, the cost of ignoring terms and conditions is not always negligible, and occasionally people have to pay (money or other means) as a result of their oversight. In this thesis, we focus on the interpretation of contractual agreements for the benefit of end-users. Contractual agreements encompass both the privacy policies and the general terms and conditions related to software and services. The main characteristics of such agreements are their use of legal terminologies and limited vocabulary. This feature has pros and cons. On one hand, the clear structure and legal language facilitate the mapping between the human-readable agreements and machine-processable concepts. On the other hand, the legal terminologies make the contractual agreement complex, subjective, and, therefore, open to interpretation. This thesis addresses the problem of contractual agreement analysis from both perspectives. In order to provide a structured presentation of contractual agreements, we apply text mining and semantic technologies to develop approaches that extract important information from the agreements and retrieve helpful links and resources for better comprehension. Our approaches are based on ontology-based information extraction, machine learning, and semantic similarity and aim to deliver tedious consent forms in a user friendly and visualized format. The ontology-based information extraction approach processes the human-readable license agreement guided by a domain ontology to extract deontic modalities and presents a summarized output to the end-user. In the extraction phase, we focus on three key rights and conditions: permission, prohibition, duty and cluster the extracted excerpts according to their similarities. The clustering is based on semantic similarity employing a distributional semantics approach on large word embeddings database. The machine learning method employs deep neural networks to classify a privacy policy’s paragraphs into pre-defined categories. Since the prediction results of the trained model are promising, we further use the predicted classes to assign five risk colors (Green, Yellow, Red) to five privacy icons (Expected Use, Expected Collection, Precise Location, Data Retention and Children Privacy). Furthermore, given that any contractual agreement must comply with the relevant legislation, we utilize text semantic similarity to map an agreement’s content to regulatory documents. The semantic similarity-based approach finds candidate sentences in an agreement that are potentially related to specific articles in the regulation. Then, for each candidate sentence, the relevant article and provision is found according to their semantic similarity. The achieved results from our proposed approaches allow us to conclude that although semi-automatic approaches lead to information loss, they save time and effort by producing instant results and facilitate the end-users understanding of legal texts.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } The ubiquitous availability of the Internet results in a massive number of apps, software, and online services with accompanying contractual agreements in the form of end-user license agreement' andprivacy policy’. Often the textual documents describing rights, policies, and conditions comprise many pages and can not be reasonably assumed to be read and understood by humans. Although everyone is exposed to such consent forms, the majority tend to ignore them due to their length and complexity. However, the cost of ignoring terms and conditions is not always negligible, and occasionally people have to pay (money or other means) as a result of their oversight. In this thesis, we focus on the interpretation of contractual agreements for the benefit of end-users. Contractual agreements encompass both the privacy policies and the general terms and conditions related to software and services. The main characteristics of such agreements are their use of legal terminologies and limited vocabulary. This feature has pros and cons. On one hand, the clear structure and legal language facilitate the mapping between the human-readable agreements and machine-processable concepts. On the other hand, the legal terminologies make the contractual agreement complex, subjective, and, therefore, open to interpretation. This thesis addresses the problem of contractual agreement analysis from both perspectives. In order to provide a structured presentation of contractual agreements, we apply text mining and semantic technologies to develop approaches that extract important information from the agreements and retrieve helpful links and resources for better comprehension. Our approaches are based on ontology-based information extraction, machine learning, and semantic similarity and aim to deliver tedious consent forms in a user friendly and visualized format. The ontology-based information extraction approach processes the human-readable license agreement guided by a domain ontology to extract deontic modalities and presents a summarized output to the end-user. In the extraction phase, we focus on three key rights and conditions: permission, prohibition, duty and cluster the extracted excerpts according to their similarities. The clustering is based on semantic similarity employing a distributional semantics approach on large word embeddings database. The machine learning method employs deep neural networks to classify a privacy policy’s paragraphs into pre-defined categories. Since the prediction results of the trained model are promising, we further use the predicted classes to assign five risk colors (Green, Yellow, Red) to five privacy icons (Expected Use, Expected Collection, Precise Location, Data Retention and Children Privacy). Furthermore, given that any contractual agreement must comply with the relevant legislation, we utilize text semantic similarity to map an agreement’s content to regulatory documents. The semantic similarity-based approach finds candidate sentences in an agreement that are potentially related to specific articles in the regulation. Then, for each candidate sentence, the relevant article and provision is found according to their semantic similarity. The achieved results from our proposed approaches allow us to conclude that although semi-automatic approaches lead to information loss, they save time and effort by producing instant results and facilitate the end-users understanding of legal texts. |
Dubey, Mohnish Towards Complex Question Answering over Knowledge Graphs PhD Thesis University of Bonn , 2021. @phdthesis{mohnishthesis, title = {Towards Complex Question Answering over Knowledge Graphs}, author = {Mohnish Dubey}, url = {https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/9122}, year = {2021}, date = {2021-01-19}, school = {University of Bonn }, abstract = {Over the past decade, Knowledge Graphs (KG) have emerged as a prominent repository for storing facts about the world in a linked data architecture. Providing machines with the capability of exploring such Knowledge Graphs and answering natural language questions over them, has been an active area of research. The purpose of this work, is to delve further into the research of retrieving information stored in KGs, based on the natural language questions posed by the user. Knowledge Graph Question Answering (KGQA) aims to produce a concise answer to a user question, such that the user is exempt from using KG vocabulary and overheads of learning a formal query language. Existing KGQA systems have achieved excellent results over Simple Questions, where the information required is limited to a single triple and a single formal query pattern. Our motivation is to improve the performance of KGQA over Complex Questions, where formal query patterns significantly vary, and a single triple is not confining for all the required information. Complex KGQA provides several challenges such as understanding semantics and syntactic structure of questions, Entity Linking, Relation Linking and Answer Representation. Lack of suitable datasets for complex question answering further adds to research gaps. Hence, in this thesis, we focus the research objective of laying the foundations for the advancement of the state-of-the-art for Complex Question Answering over Knowledge Graphs, by providing techniques to solve various challenges and provide resources to fill the research gaps. First, we propose Normalized Query Structure (NQS), which is a linguistic analyzer module that helps the QA system to detect inputs and intents and the relation between them in the users’ question. NQS acts like an intermediate language between natural language questions and formal expressions to ease the process of query formulation for complex questions. We then developed a framework named LC-QuAD to generate large scale question answering dataset by reversing the process of question answering, thereby translating natural language questions from the formal query using intermediate templates. Our goal is to use this framework for high variations in the query patterns and create a large size dataset with minimum human effort. The first version of the dataset consists of 5,000 complex questions. By extending the LC-QuAD framework to support Reified KGs and crowd-sourcing, we published the second version of the dataset as LC-QuAD 2.0, consisting of 30,000 questions with their paraphrases and has higher complexity and new variations in the questions. To overcome the problem of Entity Linking and Relation Linking in KGQA, we develop EARL, a module performing these two tasks as a single joint task for complex question answering. We develop approaches for this module, first by formalizing the task as an instance of the Generalized Traveling Salesman Problem (GTSP) and the second approach uses machine learning to exploit the connection density between nodes in the Knowledge Graph. Lastly, we create another large scale dataset to answer verbalization and provide results for multiple baseline systems on it. The Verbalization dataset is introduced to make the system’s response more human-like. The NQS based KGQA system was next to the best system in terms of accuracy on the QALD-5 dataset. We empirically prove that NQS is robust to tackle paraphrases of the questions. EARL achieves the state of the art results in Entity Linking and Relation Linking for question answering on several KGQA datasets. The dataset curated in this thesis has helped the research community to move forward in the direction of improving the accuracy of complex question answering as a task as other researchers too developed several KGQA systems and modules around these published datasets. With the large-scale datasets, we have encouraged the use of large scale machine learning, deep learning and emergence of new techniques to advance the state-of-the-art in complex question answering over knowledge graphs. We further developed core components for the KGQA pipeline to overcome the challenges of Question Understanding, Entity-Relation Linking and Answer Verbalization and thus achieve our research objective. All the proposed approaches mentioned in this thesis and the published resources are available at https://github.com/AskNowQA and are released under the umbrella project AskNow.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } Over the past decade, Knowledge Graphs (KG) have emerged as a prominent repository for storing facts about the world in a linked data architecture. Providing machines with the capability of exploring such Knowledge Graphs and answering natural language questions over them, has been an active area of research. The purpose of this work, is to delve further into the research of retrieving information stored in KGs, based on the natural language questions posed by the user. Knowledge Graph Question Answering (KGQA) aims to produce a concise answer to a user question, such that the user is exempt from using KG vocabulary and overheads of learning a formal query language. Existing KGQA systems have achieved excellent results over Simple Questions, where the information required is limited to a single triple and a single formal query pattern. Our motivation is to improve the performance of KGQA over Complex Questions, where formal query patterns significantly vary, and a single triple is not confining for all the required information. Complex KGQA provides several challenges such as understanding semantics and syntactic structure of questions, Entity Linking, Relation Linking and Answer Representation. Lack of suitable datasets for complex question answering further adds to research gaps. Hence, in this thesis, we focus the research objective of laying the foundations for the advancement of the state-of-the-art for Complex Question Answering over Knowledge Graphs, by providing techniques to solve various challenges and provide resources to fill the research gaps. First, we propose Normalized Query Structure (NQS), which is a linguistic analyzer module that helps the QA system to detect inputs and intents and the relation between them in the users’ question. NQS acts like an intermediate language between natural language questions and formal expressions to ease the process of query formulation for complex questions. We then developed a framework named LC-QuAD to generate large scale question answering dataset by reversing the process of question answering, thereby translating natural language questions from the formal query using intermediate templates. Our goal is to use this framework for high variations in the query patterns and create a large size dataset with minimum human effort. The first version of the dataset consists of 5,000 complex questions. By extending the LC-QuAD framework to support Reified KGs and crowd-sourcing, we published the second version of the dataset as LC-QuAD 2.0, consisting of 30,000 questions with their paraphrases and has higher complexity and new variations in the questions. To overcome the problem of Entity Linking and Relation Linking in KGQA, we develop EARL, a module performing these two tasks as a single joint task for complex question answering. We develop approaches for this module, first by formalizing the task as an instance of the Generalized Traveling Salesman Problem (GTSP) and the second approach uses machine learning to exploit the connection density between nodes in the Knowledge Graph. Lastly, we create another large scale dataset to answer verbalization and provide results for multiple baseline systems on it. The Verbalization dataset is introduced to make the system’s response more human-like. The NQS based KGQA system was next to the best system in terms of accuracy on the QALD-5 dataset. We empirically prove that NQS is robust to tackle paraphrases of the questions. EARL achieves the state of the art results in Entity Linking and Relation Linking for question answering on several KGQA datasets. The dataset curated in this thesis has helped the research community to move forward in the direction of improving the accuracy of complex question answering as a task as other researchers too developed several KGQA systems and modules around these published datasets. With the large-scale datasets, we have encouraged the use of large scale machine learning, deep learning and emergence of new techniques to advance the state-of-the-art in complex question answering over knowledge graphs. We further developed core components for the KGQA pipeline to overcome the challenges of Question Understanding, Entity-Relation Linking and Answer Verbalization and thus achieve our research objective. All the proposed approaches mentioned in this thesis and the published resources are available at https://github.com/AskNowQA and are released under the umbrella project AskNow. |
Oliveira, Italo Lopes; Fileto, Renato; Speck, René; 'i, Lu; Moussallem, Diego; Lehmann, Jens Towards holistic Entity Linking: Survey and directions Journal Article Inf. Syst., 95 , pp. 101624, 2021. @article{DBLP:journals/is/OliveiraFSGML21, title = {Towards holistic Entity Linking: Survey and directions}, author = {Italo Lopes Oliveira and Renato Fileto and René Speck and Lu{'i}s Paulo F. Garcia and Diego Moussallem and Jens Lehmann}, url = {https://doi.org/10.1016/j.is.2020.101624}, doi = {10.1016/j.is.2020.101624}, year = {2021}, date = {2021-01-01}, journal = {Inf. Syst.}, volume = {95}, pages = {101624}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Mami, Mohamed Nadjib Strategies for a Semantified Uniform Access to Large and Heterogeneous Data Sources PhD Thesis Rheinische Friedrich-Wilhelms-Universität Bonn, 2021. @phdthesis{handle:20.500.11811/8925, title = {Strategies for a Semantified Uniform Access to Large and Heterogeneous Data Sources}, author = {Mohamed Nadjib Mami}, url = {http://hdl.handle.net/20.500.11811/8925}, year = {2021}, date = {2021-01-01}, school = {Rheinische Friedrich-Wilhelms-Universität Bonn}, abstract = {The remarkable advances achieved in both research and development of Data Management as well as the prevalence of high-speed Internet and technology in the last few decades have caused unprecedented data avalanche. Large volumes of data manifested in a multitude of types and formats are being generated and becoming the new norm. In this context, it is crucial to both leverage existing approaches and propose novel ones to overcome this data size and complexity, and thus facilitate data exploitation. In this thesis, we investigate two major approaches to addressing this challenge: Physical Data Integration and Logical Data Integration. The specific problem tackled is to enable querying large and heterogeneous data sources in an ad hoc manner. In the Physical Data Integration, data is physically and wholly transformed into a canonical unique format, which can then be directly and uniformly queried. In the Logical Data Integration, data remains in its original format and form and a middleware is posed above the data allowing to map various schemata elements to a high-level unifying formal model. The latter enables the querying of the underlying original data in an ad hoc and uniform way, a framework which we call Semantic Data Lake, SDL. Both approaches have their advantages and disadvantages. For example, in the former, a significant effort and cost are devoted to pre-processing and transforming the data to the unified canonical format. In the latter, the cost is shifted to the query processing phases, e.g., query analysis, relevant source detection and results reconciliation. In this thesis we investigate both directions and study their strengths and weaknesses. For each direction, we propose a set of approaches and demonstrate their feasibility via a proposed implementation. In both directions, we appeal to Semantic Web technologies, which provide a set of time-proven techniques and standards that are dedicated to Data Integration. In the Physical Integration, we suggest an end-to-end blueprint for the semantification of large and heterogeneous data sources, i.e., physically transforming the data to the Semantic Web data standard RDF (Resource Description Framework). A unified data representation, storage and query interface over the data are suggested. In the Logical Integration, we provide a description of the SDL architecture, which allows querying data sources right on their original form and format without requiring a prior transformation and centralization. For a number of reasons that we detail, we put more emphasis on the virtual approach. We present the effort behind an extensible implementation of the SDL, called Squerall, which leverages state-of-the-art Semantic and Big Data technologies, e.g., RML (RDF Mapping Language) mappings, FnO (Function Ontology) ontology, and Apache Spark. A series of evaluation is conducted to evaluate the implementation along with various metrics and input data scales. In particular, we describe an industrial real-world use case using our SDL implementation. In a preparation phase, we conduct a survey for the Query Translation methods in order to back some of our design choices.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } The remarkable advances achieved in both research and development of Data Management as well as the prevalence of high-speed Internet and technology in the last few decades have caused unprecedented data avalanche. Large volumes of data manifested in a multitude of types and formats are being generated and becoming the new norm. In this context, it is crucial to both leverage existing approaches and propose novel ones to overcome this data size and complexity, and thus facilitate data exploitation. In this thesis, we investigate two major approaches to addressing this challenge: Physical Data Integration and Logical Data Integration. The specific problem tackled is to enable querying large and heterogeneous data sources in an ad hoc manner. In the Physical Data Integration, data is physically and wholly transformed into a canonical unique format, which can then be directly and uniformly queried. In the Logical Data Integration, data remains in its original format and form and a middleware is posed above the data allowing to map various schemata elements to a high-level unifying formal model. The latter enables the querying of the underlying original data in an ad hoc and uniform way, a framework which we call Semantic Data Lake, SDL. Both approaches have their advantages and disadvantages. For example, in the former, a significant effort and cost are devoted to pre-processing and transforming the data to the unified canonical format. In the latter, the cost is shifted to the query processing phases, e.g., query analysis, relevant source detection and results reconciliation. In this thesis we investigate both directions and study their strengths and weaknesses. For each direction, we propose a set of approaches and demonstrate their feasibility via a proposed implementation. In both directions, we appeal to Semantic Web technologies, which provide a set of time-proven techniques and standards that are dedicated to Data Integration. In the Physical Integration, we suggest an end-to-end blueprint for the semantification of large and heterogeneous data sources, i.e., physically transforming the data to the Semantic Web data standard RDF (Resource Description Framework). A unified data representation, storage and query interface over the data are suggested. In the Logical Integration, we provide a description of the SDL architecture, which allows querying data sources right on their original form and format without requiring a prior transformation and centralization. For a number of reasons that we detail, we put more emphasis on the virtual approach. We present the effort behind an extensible implementation of the SDL, called Squerall, which leverages state-of-the-art Semantic and Big Data technologies, e.g., RML (RDF Mapping Language) mappings, FnO (Function Ontology) ontology, and Apache Spark. A series of evaluation is conducted to evaluate the implementation along with various metrics and input data scales. In particular, we describe an industrial real-world use case using our SDL implementation. In a preparation phase, we conduct a survey for the Query Translation methods in order to back some of our design choices. |
Thakker, Harsh Vrajeshkumar On Supporting Interoperability between RDF and Property Graph Databases PhD Thesis Rheinische Friedrich-Wilhelms-Universität Bonn, 2021. @phdthesis{handle:20.500.11811/9083, title = {On Supporting Interoperability between RDF and Property Graph Databases}, author = {Harsh Vrajeshkumar Thakker}, url = {http://hdl.handle.net/20.500.11811/9083}, year = {2021}, date = {2021-01-01}, school = {Rheinische Friedrich-Wilhelms-Universität Bonn}, abstract = {Over the last few years, the amount and availability of machine-readable Open, Linked, and Big data on the web has increased. Simultaneously, several data management systems have emerged to deal with the increased amounts of this structured data. RDF and Graph databases are two popular approaches for data management based on modeling, storing, and querying graph-like data. RDF database systems are based on the W3C standard RDF data model and use the W3C standard SPARQL as their defacto query language. Most graph database systems are based on the Property Graph (PG) data model and use the Gremlin language as their query language due to its popularity amongst vendors. Given that both of these approaches have distinct and complementary characteristics – RDF is suited for distributed data integration with built-in world-wide unique identifiers and vocabularies; PGs, on the other hand, support horizontally scalable storage and querying, and are widely used for modern data analytics applications, – it becomes necessary to support interoperability amongst them. The main objective of this dissertation is to study and address this interoperability issue. We identified three research challenges that are concerned with the data interoperability, query interoperability, and benchmarking of these databases. First, we tackle the data interoperability problem. We propose three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database. We show that the proposed mappings satisfy the desired properties of semantics preservation and information preservation. Based on our analysis (both formal and empirical), we argue that any RDF database can be transformed into a PG database using our approach. Second, we propose a novel approach for querying PG databases using SPARQL using Gremlin traversals – GREMLINATOR to tackle the query interoperability problem. In doing so, we first formalize the declarative constructs of Gremlin language using a consolidated graph relational algebra and define mappings to translate SPARQL queries into Gremlin traversals. GREMLINATOR has been officially integrated as a plugin for the Apache TinkerPop graph computing framework (as sparql-gremlin), which enables users to execute SPARQL queries over a wide variety of OLTP graph databases and OLAP graph processing frameworks. Finally, we tackle the third, benchmarking (performance evaluation), problem. We propose a novel framework – LITMUS Benchmark Suite that allows a choke-point driven performance comparison and analysis of various databases (PG and RDF-based) using various third-party real and synthetic datasets and queries. We also studied a variety of intrinsic and extrinsic factors – data and system-specific metrics and Key Performance Indicators (KPIs) that influence a given system’s performance. LITMUS incorporates various memory, processor, data quality, indexing, query typology, and data-based metrics for providing a fine-grained evaluation of the benchmark. In conclusion, by filling the research gaps, addressed by this dissertation, we have laid a solid formal and practical foundation for supporting interoperability between the RDF and Property graph database technology stacks. The artifacts produced during the term of this dissertation have been integrated into various academic and industrial projects.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } Over the last few years, the amount and availability of machine-readable Open, Linked, and Big data on the web has increased. Simultaneously, several data management systems have emerged to deal with the increased amounts of this structured data. RDF and Graph databases are two popular approaches for data management based on modeling, storing, and querying graph-like data. RDF database systems are based on the W3C standard RDF data model and use the W3C standard SPARQL as their defacto query language. Most graph database systems are based on the Property Graph (PG) data model and use the Gremlin language as their query language due to its popularity amongst vendors. Given that both of these approaches have distinct and complementary characteristics – RDF is suited for distributed data integration with built-in world-wide unique identifiers and vocabularies; PGs, on the other hand, support horizontally scalable storage and querying, and are widely used for modern data analytics applications, – it becomes necessary to support interoperability amongst them. The main objective of this dissertation is to study and address this interoperability issue. We identified three research challenges that are concerned with the data interoperability, query interoperability, and benchmarking of these databases. First, we tackle the data interoperability problem. We propose three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database. We show that the proposed mappings satisfy the desired properties of semantics preservation and information preservation. Based on our analysis (both formal and empirical), we argue that any RDF database can be transformed into a PG database using our approach. Second, we propose a novel approach for querying PG databases using SPARQL using Gremlin traversals – GREMLINATOR to tackle the query interoperability problem. In doing so, we first formalize the declarative constructs of Gremlin language using a consolidated graph relational algebra and define mappings to translate SPARQL queries into Gremlin traversals. GREMLINATOR has been officially integrated as a plugin for the Apache TinkerPop graph computing framework (as sparql-gremlin), which enables users to execute SPARQL queries over a wide variety of OLTP graph databases and OLAP graph processing frameworks. Finally, we tackle the third, benchmarking (performance evaluation), problem. We propose a novel framework – LITMUS Benchmark Suite that allows a choke-point driven performance comparison and analysis of various databases (PG and RDF-based) using various third-party real and synthetic datasets and queries. We also studied a variety of intrinsic and extrinsic factors – data and system-specific metrics and Key Performance Indicators (KPIs) that influence a given system’s performance. LITMUS incorporates various memory, processor, data quality, indexing, query typology, and data-based metrics for providing a fine-grained evaluation of the benchmark. In conclusion, by filling the research gaps, addressed by this dissertation, we have laid a solid formal and practical foundation for supporting interoperability between the RDF and Property graph database technology stacks. The artifacts produced during the term of this dissertation have been integrated into various academic and industrial projects. |
2020 |
Sejdiu, Gezim Efficient Distributed In-Memory Processing of RDF Datasets PhD Thesis Rheinische Friedrich-Wilhelms-Universität Bonn, 2020. @phdthesis{gezim-sejdiu-2020-phd-thesis, title = {Efficient Distributed In-Memory Processing of RDF Datasets}, author = {Gezim Sejdiu}, url = {http://hdl.handle.net/20.500.11811/8735}, year = {2020}, date = {2020-01-01}, school = {Rheinische Friedrich-Wilhelms-Universität Bonn}, abstract = {Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. Today, we count more than 10,000 datasets made available online following Semantic Web standards. A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. Thus, we introduce a distributed approach of quality assessment of large RDF datasets. It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. As a result, it has become difficult to efficiently process these large RDF datasets. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. All the proposed approaches during this thesis are integrated into the larger SANSA framework.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies. Today, we count more than 10,000 datasets made available online following Semantic Web standards. A major and yet unsolved challenge that research faces today is to perform scalable analysis of large-scale knowledge graphs in order to facilitate applications in various domains including life sciences, publishing, and the internet of things. The main objective of this thesis is to lay foundations for efficient algorithms performing analytics, i.e. exploration, quality assessment, and querying over semantic knowledge graphs at a scale that has not been possible before. First, we propose a novel approach for statistical calculations of large RDF datasets, which scales out to clusters of machines. In particular, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. Many applications such as data integration, search, and interlinking, may take full advantage of the data when having a priori statistical information about its internal structure and coverage. However, such applications may suffer from low quality and not being able to leverage the full advantage of the data when the size of data goes beyond the capacity of the resources available. Thus, we introduce a distributed approach of quality assessment of large RDF datasets. It is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. Based on the knowledge of the internal statistics of a dataset and its quality, users typically want to query and retrieve large amounts of information. As a result, it has become difficult to efficiently process these large RDF datasets. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. Therefore, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets by translating SPARQL queries into Spark executable code. We conducted several empirical evaluations to assess the scalability, effectiveness, and efficiency of our proposed approaches. More importantly, various use cases i.e. Ethereum analysis, Mining Big Data Logs, and Scalable Integration of POIs, have been developed and leverages by our approach. The empirical evaluations and concrete applications provide evidence that our methodology and techniques proposed during this thesis help to effectively analyze and process large-scale RDF datasets. All the proposed approaches during this thesis are integrated into the larger SANSA framework. |
Zafartavanaelmi, Hamid Semantic Question Answering Over Knowledge Graphs: Pitfalls and Pearls PhD Thesis University of Bonn, 2020. @phdthesis{ZafarThesis, title = {Semantic Question Answering Over Knowledge Graphs: Pitfalls and Pearls}, author = {Hamid Zafartavanaelmi}, url = {https://bonndoc.ulb.uni-bonn.de/xmlui/bitstream/handle/20.500.11811/9125/6154.pdf?sequence=1&isAllowed=y}, year = {2020}, date = {2020-01-01}, school = {University of Bonn}, abstract = {Nowadays, the Web provides an infrastructure to share all kinds of information which are easily accessible to humans around the world. Furthermore, the amount of information is growing rapidly and requires computing machines to process, comprehend, and extract useful information tailored for the end-users. The Semantic Web and semantic technologies play a prominent role to enable knowledge representation and reasoning for these computational processes. Semantic technologies such as ontologies and knowledge graphs are being used in various application domains, including data governance, knowledge management, chatbots, biology, etc., which aim at providing proper infrastructure to analyze the knowledge and reasoning for the computers. Semantic Question Answering systems are among the most desired platforms in recent years that facilitate access to information in knowledge graphs. They provide a natural language interface that permits the users to ask their questions posed in a natural language, without any understanding of the underlying technologies. We thus study question answering systems over knowledge graphs which aim to map an input question in natural language into a formal query, intending to retrieve a concise answer from the knowledge graph. This is a highly challenging task due to the intrinsic complexity of the natural language, such that the resulting query does not always accurately subserve the user intent, particularly, for more complex and less common questions.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } Nowadays, the Web provides an infrastructure to share all kinds of information which are easily accessible to humans around the world. Furthermore, the amount of information is growing rapidly and requires computing machines to process, comprehend, and extract useful information tailored for the end-users. The Semantic Web and semantic technologies play a prominent role to enable knowledge representation and reasoning for these computational processes. Semantic technologies such as ontologies and knowledge graphs are being used in various application domains, including data governance, knowledge management, chatbots, biology, etc., which aim at providing proper infrastructure to analyze the knowledge and reasoning for the computers. Semantic Question Answering systems are among the most desired platforms in recent years that facilitate access to information in knowledge graphs. They provide a natural language interface that permits the users to ask their questions posed in a natural language, without any understanding of the underlying technologies. We thus study question answering systems over knowledge graphs which aim to map an input question in natural language into a formal query, intending to retrieve a concise answer from the knowledge graph. This is a highly challenging task due to the intrinsic complexity of the natural language, such that the resulting query does not always accurately subserve the user intent, particularly, for more complex and less common questions. |
Höffner, Konrad Question Answering on RDF Data Cubes PhD Thesis University of Leipzig, 2020. @phdthesis{HoeffnerThesis, title = {Question Answering on RDF Data Cubes}, author = {Konrad Höffner}, url = {https://svn.aksw.org/papers/2016/Thesis_Konrad/public.pdf}, year = {2020}, date = {2020-01-01}, school = {University of Leipzig}, abstract = {The Semantic Web, a Web of Data, is an extension of the World Wide Web (WWW), a Web of Documents. A large amount of such data is freely available as Linked Open Data (LOD) for many areas of knowledge, forming the LOD Cloud. While this data conforms to the Resource Description Framework (RDF) and can thus be processed by machines, users need to master a formal query language and learn a specific vocabulary. Semantic Question Answering (SQA) systems remove those access barriers by letting the user ask natural language questions that the systems translate into formal queries. Thus, the research area of SQA plays an important role for the acceptance and benefit of the Semantic Web. The original contributions of this thesis to SQA are: First, we survey the current state of the art of SQA. We complement existing surveys by systematically identifying SQA publications in the chosen timeframe. 72 publications describing 62 different systems are systematically and manually selected using predefined inclusion and exclusion criteria out of 1960 candidates from the end of 2010 to July 2015. The survey identifies common challenges, structured solutions, and recommendations on research opportunities for future systems. From that point on, we focus on multidimensional numerical data, which is immensely valuable as it influences decisions in health care, policy and finance, among others. With the growth of the open data movement, more and more of it is becoming freely available. A large amount of such data is included in the LOD cloud using the RDF Data Cube (RDC) vocabulary. However, consuming multidimensional numerical data requires experts and specialized tools. Traditional SQA systems cannot process RDCs because their meta-structure is opaque to applications that expect facts to be encoded in single triples, This motivates our second contribution, the design and implementation of the first SQA algorithm on RDF Data Cubes. We kick-start this new research subfield by creating a user question corpus and a benchmark over multiple data sets. The evaluation of our system on the benchmark, which is included in the public Question Answering over Linked Data (QALD) challenge of 2016, shows the feasibility of the approach, but also highlights challenges, which we discuss in detail as a starting point for future work in the field. The benchmark is based on our final contribution, the addition of 955 financial government spending data sets to the LOD cloud by transforming data sets of the OpenSpending project to RDF Data Cubes. Open spending data has the power to reduce corruption by increasing accountability and strengthens democracy because voters can make better informed decisions. An informed and trusting public also strengthens the government itself because it is more likely to commit to large projects. OpenSpending.org is an open platform that provides public finance data from governments around the world. The transformation result, called Linked Spending, consists of more than five million planned and carried out financial transactions in 955 data sets from all over the world as Linked Open Data and is freely available and openly licensed.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } The Semantic Web, a Web of Data, is an extension of the World Wide Web (WWW), a Web of Documents. A large amount of such data is freely available as Linked Open Data (LOD) for many areas of knowledge, forming the LOD Cloud. While this data conforms to the Resource Description Framework (RDF) and can thus be processed by machines, users need to master a formal query language and learn a specific vocabulary. Semantic Question Answering (SQA) systems remove those access barriers by letting the user ask natural language questions that the systems translate into formal queries. Thus, the research area of SQA plays an important role for the acceptance and benefit of the Semantic Web. The original contributions of this thesis to SQA are: First, we survey the current state of the art of SQA. We complement existing surveys by systematically identifying SQA publications in the chosen timeframe. 72 publications describing 62 different systems are systematically and manually selected using predefined inclusion and exclusion criteria out of 1960 candidates from the end of 2010 to July 2015. The survey identifies common challenges, structured solutions, and recommendations on research opportunities for future systems. From that point on, we focus on multidimensional numerical data, which is immensely valuable as it influences decisions in health care, policy and finance, among others. With the growth of the open data movement, more and more of it is becoming freely available. A large amount of such data is included in the LOD cloud using the RDF Data Cube (RDC) vocabulary. However, consuming multidimensional numerical data requires experts and specialized tools. Traditional SQA systems cannot process RDCs because their meta-structure is opaque to applications that expect facts to be encoded in single triples, This motivates our second contribution, the design and implementation of the first SQA algorithm on RDF Data Cubes. We kick-start this new research subfield by creating a user question corpus and a benchmark over multiple data sets. The evaluation of our system on the benchmark, which is included in the public Question Answering over Linked Data (QALD) challenge of 2016, shows the feasibility of the approach, but also highlights challenges, which we discuss in detail as a starting point for future work in the field. The benchmark is based on our final contribution, the addition of 955 financial government spending data sets to the LOD cloud by transforming data sets of the OpenSpending project to RDF Data Cubes. Open spending data has the power to reduce corruption by increasing accountability and strengthens democracy because voters can make better informed decisions. An informed and trusting public also strengthens the government itself because it is more likely to commit to large projects. OpenSpending.org is an open platform that provides public finance data from governments around the world. The transformation result, called Linked Spending, consists of more than five million planned and carried out financial transactions in 955 data sets from all over the world as Linked Open Data and is freely available and openly licensed. |
Fathalla, Said; Auer, Sören; Lange, Christoph Towards the semantic formalization of science Inproceedings SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30 - April 3, 2020, pp. 2057–2059, ACM, 2020. @inproceedings{DBLP:conf/sac/FathallaA020, title = {Towards the semantic formalization of science}, author = {Said Fathalla and Sören Auer and Christoph Lange}, url = {https://doi.org/10.1145/3341105.3374132}, doi = {10.1145/3341105.3374132}, year = {2020}, date = {2020-01-01}, booktitle = {SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30 - April 3, 2020}, pages = {2057--2059}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Jabeen, Hajira; Haziiev, Eskender; Sejdiu, Gezim; Lehmann, Jens DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data Inproceedings IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 400–407, IEEE, 2020. @inproceedings{DBLP:conf/semco/JabeenHS020, title = {DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data}, author = {Hajira Jabeen and Eskender Haziiev and Gezim Sejdiu and Jens Lehmann}, url = {https://doi.org/10.1109/ICSC.2020.00079}, doi = {10.1109/ICSC.2020.00079}, year = {2020}, date = {2020-01-01}, booktitle = {IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020}, pages = {400--407}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Khiat, Abderrahmane; Halilaj, Lavdim; Hemid, Ahmad; Lohmann, Steffen VoColReg: A Registry for Supporting Distributed Ontology Development using Version Control Systems Inproceedings IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 393–399, IEEE, 2020. @inproceedings{DBLP:conf/semco/KhiatHHL20, title = {VoColReg: A Registry for Supporting Distributed Ontology Development using Version Control Systems}, author = {Abderrahmane Khiat and Lavdim Halilaj and Ahmad Hemid and Steffen Lohmann}, url = {https://doi.org/10.1109/ICSC.2020.00078}, doi = {10.1109/ICSC.2020.00078}, year = {2020}, date = {2020-01-01}, booktitle = {IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020}, pages = {393--399}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Thakkar, Harsh; Angles, Renzo; Rodriguez, Marko; Mallette, Stephen; Lehmann, Jens Let's build Bridges, not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin Inproceedings IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020, pp. 408–415, IEEE, 2020. @inproceedings{DBLP:conf/semco/ThakkarARM020, title = {Let's build Bridges, not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin}, author = {Harsh Thakkar and Renzo Angles and Marko Rodriguez and Stephen Mallette and Jens Lehmann}, url = {https://doi.org/10.1109/ICSC.2020.00080}, doi = {10.1109/ICSC.2020.00080}, year = {2020}, date = {2020-01-01}, booktitle = {IEEE 14th International Conference on Semantic Computing, ICSC 2020, San Diego, CA, USA, February 3-5, 2020}, pages = {408--415}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Grubenmann, Tobias; Cheng, Reynold C K; Lakshmanan, Laks V S TSA: A Truthful Mechanism for Social Advertising Inproceedings WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, pp. 214–222, ACM, 2020. @inproceedings{DBLP:conf/wsdm/GrubenmannCL20, title = {TSA: A Truthful Mechanism for Social Advertising}, author = {Tobias Grubenmann and Reynold C. K. Cheng and Laks V. S. Lakshmanan}, url = {https://doi.org/10.1145/3336191.3371809}, doi = {10.1145/3336191.3371809}, year = {2020}, date = {2020-01-01}, booktitle = {WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020}, pages = {214--222}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Musyaffa, Fathoni A; Vidal, Maria-Esther; Orlandi, Fabrizio; Lehmann, Jens; Jabeen, Hajira IOTA: Interlinking of heterogeneous multilingual open fiscal DaTA Journal Article Expert Syst. Appl., 147 , pp. 113135, 2020. @article{DBLP:journals/eswa/MusyaffaVOLJ20, title = {IOTA: Interlinking of heterogeneous multilingual open fiscal DaTA}, author = {Fathoni A. Musyaffa and Maria-Esther Vidal and Fabrizio Orlandi and Jens Lehmann and Hajira Jabeen}, url = {https://doi.org/10.1016/j.eswa.2019.113135}, doi = {10.1016/j.eswa.2019.113135}, year = {2020}, date = {2020-01-01}, journal = {Expert Syst. Appl.}, volume = {147}, pages = {113135}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Fathalla, Said; Vahdati, Sahar; Lange, Christoph; Auer, Sören Scholarly event characteristics in four fields of science: a metrics-based analysis Journal Article Scientometrics, 123 (2), pp. 677–705, 2020. @article{DBLP:journals/scientometrics/FathallaVLA20, title = {Scholarly event characteristics in four fields of science: a metrics-based analysis}, author = {Said Fathalla and Sahar Vahdati and Christoph Lange and Sören Auer}, url = {https://doi.org/10.1007/s11192-020-03391-y}, doi = {10.1007/s11192-020-03391-y}, year = {2020}, date = {2020-01-01}, journal = {Scientometrics}, volume = {123}, number = {2}, pages = {677--705}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Bader, Sebastian R; Grangel-González, Irlán; Nanjappa, Priyanka; Vidal, Maria-Esther; Maleshkova, Maria A Knowledge Graph for Industry 4.0 Inproceedings The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings, pp. 465–480, Springer, 2020. @inproceedings{DBLP:conf/esws/BaderGNVM20, title = {A Knowledge Graph for Industry 4.0}, author = {Sebastian R. Bader and Irlán Grangel-González and Priyanka Nanjappa and Maria-Esther Vidal and Maria Maleshkova}, url = {https://doi.org/10.1007/978-3-030-49461-2_27}, doi = {10.1007/978-3-030-49461-2_27}, year = {2020}, date = {2020-01-01}, booktitle = {The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings}, volume = {12123}, pages = {465--480}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Kacupaj, Endri; Zafar, Hamid; Lehmann, Jens; Maleshkova, Maria VQuAnDa: Verbalization QUestion ANswering DAtaset Inproceedings The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings, pp. 531–547, Springer, 2020. @inproceedings{DBLP:conf/esws/KacupajZ0M20, title = {VQuAnDa: Verbalization QUestion ANswering DAtaset}, author = {Endri Kacupaj and Hamid Zafar and Jens Lehmann and Maria Maleshkova}, url = {https://doi.org/10.1007/978-3-030-49461-2_31}, doi = {10.1007/978-3-030-49461-2_31}, year = {2020}, date = {2020-01-01}, booktitle = {The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings}, volume = {12123}, pages = {531--547}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Nayyeri, Mojtaba; Vahdati, Sahar; Zhou, Xiaotian; Yazdi, Hamed Shariat; Lehmann, Jens Embedding-Based Recommendations on Scholarly Knowledge Graphs Inproceedings The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings, pp. 255–270, Springer, 2020. @inproceedings{DBLP:conf/esws/NayyeriVZY020, title = {Embedding-Based Recommendations on Scholarly Knowledge Graphs}, author = {Mojtaba Nayyeri and Sahar Vahdati and Xiaotian Zhou and Hamed Shariat Yazdi and Jens Lehmann}, url = {https://doi.org/10.1007/978-3-030-49461-2_15}, doi = {10.1007/978-3-030-49461-2_15}, year = {2020}, date = {2020-01-01}, booktitle = {The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings}, volume = {12123}, pages = {255--270}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Nedelchev, Rostislav; Usbeck, Ricardo; Lehmann, Jens Treating Dialogue Quality Evaluation as an Anomaly Detection Problem Inproceedings Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020, pp. 508–512, European Language Resources Association, 2020. @inproceedings{DBLP:conf/lrec/NedelchevUL20, title = {Treating Dialogue Quality Evaluation as an Anomaly Detection Problem}, author = {Rostislav Nedelchev and Ricardo Usbeck and Jens Lehmann}, url = {https://www.aclweb.org/anthology/2020.lrec-1.64/}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020}, pages = {508--512}, publisher = {European Language Resources Association}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Rula, Anisa; Zaveri, Amrapali; Simperl, Elena; Demidova, Elena Editorial: Special Issue on Quality Assessment of Knowledge Graphs Dedicated to the Memory of Amrapali Zaveri Journal Article J. Data and Information Quality, 12 (2), pp. 7:1–7:4, 2020. @article{DBLP:journals/jdiq/RulaZSD20, title = {Editorial: Special Issue on Quality Assessment of Knowledge Graphs Dedicated to the Memory of Amrapali Zaveri}, author = {Anisa Rula and Amrapali Zaveri and Elena Simperl and Elena Demidova}, url = {https://doi.org/10.1145/3388748}, doi = {10.1145/3388748}, year = {2020}, date = {2020-01-01}, journal = {J. Data and Information Quality}, volume = {12}, number = {2}, pages = {7:1--7:4}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Angles, Renzo; Thakkar, Harsh; Tomaszuk, Dominik Mapping RDF Databases to Property Graph Databases Journal Article IEEE Access, 8 , pp. 86091–86110, 2020. @article{DBLP:journals/access/AnglesTT20, title = {Mapping RDF Databases to Property Graph Databases}, author = {Renzo Angles and Harsh Thakkar and Dominik Tomaszuk}, url = {https://doi.org/10.1109/ACCESS.2020.2993117}, doi = {10.1109/ACCESS.2020.2993117}, year = {2020}, date = {2020-01-01}, journal = {IEEE Access}, volume = {8}, pages = {86091--86110}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Han, Xiaolin; Grubenmann, Tobias; Cheng, Reynold; Wong, Sze Chun; Li, Xiaodong; Sun, Wenya Traffic Incident Detection: A Trajectory-based Approach Inproceedings 36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, April 20-24, 2020, pp. 1866–1869, IEEE, 2020. @inproceedings{DBLP:conf/icde/HanGCWLS20, title = {Traffic Incident Detection: A Trajectory-based Approach}, author = {Xiaolin Han and Tobias Grubenmann and Reynold Cheng and Sze Chun Wong and Xiaodong Li and Wenya Sun}, url = {https://doi.org/10.1109/ICDE48307.2020.00190}, doi = {10.1109/ICDE48307.2020.00190}, year = {2020}, date = {2020-01-01}, booktitle = {36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, April 20-24, 2020}, pages = {1866--1869}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Jahn, Franziska; Bindel, Michelle; Höffner, Konrad; Ghalandari, Maryam; Schneider, Birgit; Stäubert, Sebastian; Dornauer, Verena; Karopka, Thomas; Ammenwerth, Elske; Winter, Alfred Towards Precise Descriptions of Medical Free/Libre and Open Source Software Inproceedings Digital Personalized Health and Medicine - Proceedings of MIE 2020, Medical Informatics Europe, Geneva, Switzerland, April 28 - May 1, 2020 [conference cancelled because of Covid-19], pp. 463–468, IOS Press, 2020. @inproceedings{DBLP:conf/mie/JahnBHGSSDKAW20, title = {Towards Precise Descriptions of Medical Free/Libre and Open Source Software}, author = {Franziska Jahn and Michelle Bindel and Konrad Höffner and Maryam Ghalandari and Birgit Schneider and Sebastian Stäubert and Verena Dornauer and Thomas Karopka and Elske Ammenwerth and Alfred Winter}, url = {https://doi.org/10.3233/SHTI200203}, doi = {10.3233/SHTI200203}, year = {2020}, date = {2020-01-01}, booktitle = {Digital Personalized Health and Medicine - Proceedings of MIE 2020, Medical Informatics Europe, Geneva, Switzerland, April 28 - May 1, 2020 [conference cancelled because of Covid-19]}, volume = {270}, pages = {463--468}, publisher = {IOS Press}, series = {Studies in Health Technology and Informatics}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Elahi, Mehdi; Hosseini, Reza; Rimaz, Mohammad Hossein; Moghaddam, Farshad Bakhshandegan; Trattner, Christoph Visually-Aware Video Recommendation in the Cold Start Inproceedings HT '20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, USA, July 13-15, 2020, pp. 225–229, ACM, 2020. @inproceedings{DBLP:conf/ht/ElahiHRMT20, title = {Visually-Aware Video Recommendation in the Cold Start}, author = {Mehdi Elahi and Reza Hosseini and Mohammad Hossein Rimaz and Farshad Bakhshandegan Moghaddam and Christoph Trattner}, url = {https://doi.org/10.1145/3372923.3404778}, doi = {10.1145/3372923.3404778}, year = {2020}, date = {2020-01-01}, booktitle = {HT '20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, USA, July 13-15, 2020}, pages = {225--229}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Elias, Mirette; Ruckhaus, Edna; Draffan, E A; James, Abi; Suárez-Figueroa, Mari Carmen; Lohmann, Steffen; Khiat, Abderrahmane; Auer, Sören Accessibility and Personalization in OpenCourseWare : An Inclusive Development Approach Inproceedings 20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020, Tartu, Estonia, July 6-9, 2020, pp. 279–283, IEEE, 2020. @inproceedings{DBLP:conf/icalt/EliasRDJSLKA20, title = {Accessibility and Personalization in OpenCourseWare : An Inclusive Development Approach}, author = {Mirette Elias and Edna Ruckhaus and E. A. Draffan and Abi James and Mari Carmen Suárez-Figueroa and Steffen Lohmann and Abderrahmane Khiat and Sören Auer}, url = {https://doi.org/10.1109/ICALT49669.2020.00091}, doi = {10.1109/ICALT49669.2020.00091}, year = {2020}, date = {2020-01-01}, booktitle = {20th IEEE International Conference on Advanced Learning Technologies, ICALT 2020, Tartu, Estonia, July 6-9, 2020}, pages = {279--283}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Reimann, Lars; Kniesel-Wünsche, Günter Achieving Guidance in Applied Machine Learning through Software Engineering Techniques Inproceedings Programming'20: 4th International Conference on the Art, Science, and Engineering of Programming, Porto, Portugal, March 23-26, 2020, pp. 7–12, ACM, 2020. @inproceedings{DBLP:conf/programming/ReimannK20, title = {Achieving Guidance in Applied Machine Learning through Software Engineering Techniques}, author = {Lars Reimann and Günter Kniesel-Wünsche}, url = {https://doi.org/10.1145/3397537.3397552}, doi = {10.1145/3397537.3397552}, year = {2020}, date = {2020-01-01}, booktitle = {Programming'20: 4th International Conference on the Art, Science, and Engineering of Programming, Porto, Portugal, March 23-26, 2020}, pages = {7--12}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Tomaszuk, Dominik; Angles, Renzo; Thakkar, Harsh PGO: Describing Property Graphs in RDF Journal Article IEEE Access, 8 , pp. 118355–118369, 2020. @article{DBLP:journals/access/TomaszukAT20, title = {PGO: Describing Property Graphs in RDF}, author = {Dominik Tomaszuk and Renzo Angles and Harsh Thakkar}, url = {https://doi.org/10.1109/ACCESS.2020.3002018}, doi = {10.1109/ACCESS.2020.3002018}, year = {2020}, date = {2020-01-01}, journal = {IEEE Access}, volume = {8}, pages = {118355--118369}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ali, Mehdi; Berrendorf, Max; Hoyt, Charles Tapley; Vermue, Laurent; Galkin, Mikhail; Sharifzadeh, Sahand; Fischer, Asja; Tresp, Volker; Lehmann, Jens Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework Miscellaneous 2020. @misc{DBLP:journals/corr/abs-2006-13365, title = {Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework}, author = {Mehdi Ali and Max Berrendorf and Charles Tapley Hoyt and Laurent Vermue and Mikhail Galkin and Sahand Sharifzadeh and Asja Fischer and Volker Tresp and Jens Lehmann}, url = {https://arxiv.org/abs/2006.13365}, year = {2020}, date = {2020-01-01}, journal = {CoRR}, volume = {abs/2006.13365}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Bader, Sebastian R; Maleshkova, Maria SOLIOT - Decentralized Data Control and Interactions for IoT Journal Article Future Internet, 12 (6), pp. 105, 2020. @article{DBLP:journals/fi/BaderM20, title = {SOLIOT - Decentralized Data Control and Interactions for IoT}, author = {Sebastian R. Bader and Maria Maleshkova}, url = {https://doi.org/10.3390/fi12060105}, doi = {10.3390/fi12060105}, year = {2020}, date = {2020-01-01}, journal = {Future Internet}, volume = {12}, number = {6}, pages = {105}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Jabeen, Hajira Big Data Outlook, Tools, and Architectures Incollection Knowledge Graphs and Big Data Processing, 12072 , pp. 35–55, Springer, 2020. @incollection{DBLP:series/lncs/Jabeen20, title = {Big Data Outlook, Tools, and Architectures}, author = {Hajira Jabeen}, url = {https://doi.org/10.1007/978-3-030-53199-7_3}, doi = {10.1007/978-3-030-53199-7_3}, year = {2020}, date = {2020-01-01}, booktitle = {Knowledge Graphs and Big Data Processing}, volume = {12072}, pages = {35--55}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Jabeen, Hajira; Graux, Damien; Sejdiu, Gezim Scalable Knowledge Graph Processing Using SANSA Incollection Knowledge Graphs and Big Data Processing, 12072 , pp. 105–121, Springer, 2020. @incollection{DBLP:series/lncs/JabeenGS20, title = {Scalable Knowledge Graph Processing Using SANSA}, author = {Hajira Jabeen and Damien Graux and Gezim Sejdiu}, url = {https://doi.org/10.1007/978-3-030-53199-7_7}, doi = {10.1007/978-3-030-53199-7_7}, year = {2020}, date = {2020-01-01}, booktitle = {Knowledge Graphs and Big Data Processing}, volume = {12072}, pages = {105--121}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Tasnim, Mayesha; Collarana, Diego; Graux, Damien; Vidal, Maria-Esther Context-Based Entity Matching for Big Data Incollection Knowledge Graphs and Big Data Processing, 12072 , pp. 122–146, Springer, 2020. @incollection{DBLP:series/lncs/TasnimCGV20, title = {Context-Based Entity Matching for Big Data}, author = {Mayesha Tasnim and Diego Collarana and Damien Graux and Maria-Esther Vidal}, url = {https://doi.org/10.1007/978-3-030-53199-7_8}, doi = {10.1007/978-3-030-53199-7_8}, year = {2020}, date = {2020-01-01}, booktitle = {Knowledge Graphs and Big Data Processing}, volume = {12072}, pages = {122--146}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Jabeen, Hajira; Weinz, Jonas; Lehmann, Jens AutoChef: Automated Generation of Cooking Recipes Inproceedings IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, United Kingdom, July 19-24, 2020, pp. 1–7, IEEE, 2020. @inproceedings{DBLP:conf/cec/JabeenWL20, title = {AutoChef: Automated Generation of Cooking Recipes}, author = {Hajira Jabeen and Jonas Weinz and Jens Lehmann}, url = {https://doi.org/10.1109/CEC48606.2020.9185605}, doi = {10.1109/CEC48606.2020.9185605}, year = {2020}, date = {2020-01-01}, booktitle = {IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, United Kingdom, July 19-24, 2020}, pages = {1--7}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Elias, Mirette; Oelen, Allard; Tavakoli, MohammadReza; Kismihók, Gábor; Auer, Sören Quality Evaluation of Open Educational Resources Inproceedings Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Germany, September 14-18, 2020, Proceedings, pp. 410–415, Springer, 2020. @inproceedings{DBLP:conf/ectel/EliasOTKA20, title = {Quality Evaluation of Open Educational Resources}, author = {Mirette Elias and Allard Oelen and MohammadReza Tavakoli and Gábor Kismihók and Sören Auer}, url = {https://doi.org/10.1007/978-3-030-57717-9_36}, doi = {10.1007/978-3-030-57717-9_36}, year = {2020}, date = {2020-01-01}, booktitle = {Addressing Global Challenges and Quality Education - 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Germany, September 14-18, 2020, Proceedings}, volume = {12315}, pages = {410--415}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören Ontology Design for Pharmaceutical Research Outcomes Inproceedings Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings, pp. 119–132, Springer, 2020. @inproceedings{DBLP:conf/ercimdl/SayFV0A20, title = {Ontology Design for Pharmaceutical Research Outcomes}, author = {Zeynep Say and Said Fathalla and Sahar Vahdati and Jens Lehmann and Sören Auer}, url = {https://doi.org/10.1007/978-3-030-54956-5_9}, doi = {10.1007/978-3-030-54956-5_9}, year = {2020}, date = {2020-01-01}, booktitle = {Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings}, volume = {12246}, pages = {119--132}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Cremaschi, Marco; Paoli, Flavio De; Rula, Anisa; Spahiu, Blerina A fully automated approach to a complete Semantic Table Interpretation Journal Article Future Gener. Comput. Syst., 112 , pp. 478–500, 2020. @article{DBLP:journals/fgcs/CremaschiPRS20, title = {A fully automated approach to a complete Semantic Table Interpretation}, author = {Marco Cremaschi and Flavio De Paoli and Anisa Rula and Blerina Spahiu}, url = {https://doi.org/10.1016/j.future.2020.05.019}, doi = {10.1016/j.future.2020.05.019}, year = {2020}, date = {2020-01-01}, journal = {Future Gener. Comput. Syst.}, volume = {112}, pages = {478--500}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Sibarani, Elisa Margareth; Scerri, Simon SCODIS: Job Advert-Derived Time Series for High-Demand Skillset Discovery and Prediction Inproceedings Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Bratislava, Slovakia, September 14-17, 2020, Proceedings, Part II, pp. 366–381, Springer, 2020. @inproceedings{DBLP:conf/dexa/SibaraniS20, title = {SCODIS: Job Advert-Derived Time Series for High-Demand Skillset Discovery and Prediction}, author = {Elisa Margareth Sibarani and Simon Scerri}, url = {https://doi.org/10.1007/978-3-030-59051-2_25}, doi = {10.1007/978-3-030-59051-2_25}, year = {2020}, date = {2020-01-01}, booktitle = {Database and Expert Systems Applications - 31st International Conference, DEXA 2020, Bratislava, Slovakia, September 14-17, 2020, Proceedings, Part II}, volume = {12392}, pages = {366--381}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Sadeghi, Afshin; Graux, Damien; Yazdi, Hamed Shariat; Lehmann, Jens MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs Inproceedings ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020), pp. 1427–1434, IOS Press, 2020. @inproceedings{DBLP:conf/ecai/SadeghiGY020, title = {MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs}, author = {Afshin Sadeghi and Damien Graux and Hamed Shariat Yazdi and Jens Lehmann}, url = {https://doi.org/10.3233/FAIA200248}, doi = {10.3233/FAIA200248}, year = {2020}, date = {2020-01-01}, booktitle = {ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020)}, volume = {325}, pages = {1427--1434}, publisher = {IOS Press}, series = {Frontiers in Artificial Intelligence and Applications}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Zafar, Hamid; Tavakol, Maryam; Lehmann, Jens Distantly Supervised Question Parsing Inproceedings ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020), pp. 2290–2297, IOS Press, 2020. @inproceedings{DBLP:conf/ecai/ZafarT020, title = {Distantly Supervised Question Parsing}, author = {Hamid Zafar and Maryam Tavakol and Jens Lehmann}, url = {https://doi.org/10.3233/FAIA200357}, doi = {10.3233/FAIA200357}, year = {2020}, date = {2020-01-01}, booktitle = {ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020)}, volume = {325}, pages = {2290--2297}, publisher = {IOS Press}, series = {Frontiers in Artificial Intelligence and Applications}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Nejad, Najmeh Mousavi; Jabat, Pablo; Nedelchev, Rostislav; Scerri, Simon; Graux, Damien Establishing a Strong Baseline for Privacy Policy Classification Inproceedings ICT Systems Security and Privacy Protection - 35th IFIP TC 11 International Conference, SEC 2020, Maribor, Slovenia, September 21-23, 2020, Proceedings, pp. 370–383, Springer, 2020. @inproceedings{DBLP:conf/sec/NejadJNSG20, title = {Establishing a Strong Baseline for Privacy Policy Classification}, author = {Najmeh Mousavi Nejad and Pablo Jabat and Rostislav Nedelchev and Simon Scerri and Damien Graux}, url = {https://doi.org/10.1007/978-3-030-58201-2_25}, doi = {10.1007/978-3-030-58201-2_25}, year = {2020}, date = {2020-01-01}, booktitle = {ICT Systems Security and Privacy Protection - 35th IFIP TC 11 International Conference, SEC 2020, Maribor, Slovenia, September 21-23, 2020, Proceedings}, volume = {580}, pages = {370--383}, publisher = {Springer}, series = {IFIP Advances in Information and Communication Technology}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Elias, Mirette; Tavakoli, MohammadReza; Lohmann, Steffen; Kismihók, Gábor; Auer, Sören An OER Recommender System Supporting Accessibility Requirements Inproceedings ASSETS '20: The 22nd International ACM SIGACCESS Conference on Computers and Accessibility, Virtual Event, Greece, October 26-28, 2020, pp. 57:1–57:4, ACM, 2020. @inproceedings{DBLP:conf/assets/EliasTLKA20, title = {An OER Recommender System Supporting Accessibility Requirements}, author = {Mirette Elias and MohammadReza Tavakoli and Steffen Lohmann and Gábor Kismihók and Sören Auer}, url = {https://doi.org/10.1145/3373625.3418021}, doi = {10.1145/3373625.3418021}, year = {2020}, date = {2020-01-01}, booktitle = {ASSETS '20: The 22nd International ACM SIGACCESS Conference on Computers and Accessibility, Virtual Event, Greece, October 26-28, 2020}, pages = {57:1--57:4}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Chau, Minh Triet; Esteves, Diego; Lehmann, Jens A Neural-based model to Predict the Future Natural Gas Market Price through Open-domain Event Extraction Inproceedings Proceedings of the 1st International Workshop on Cross-lingual Event-centric Open Analytics co-located with the 17th Extended Semantic Web Conference (ESWC 2020), Heraklion, Crete, Greece, June 3, 2020 (online event due to COVID-19 outbreak), pp. 17–31, CEUR-WS.org, 2020. @inproceedings{DBLP:conf/esws/ChauE020, title = {A Neural-based model to Predict the Future Natural Gas Market Price through Open-domain Event Extraction}, author = {Minh Triet Chau and Diego Esteves and Jens Lehmann}, url = {http://ceur-ws.org/Vol-2611/paper2.pdf}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of the 1st International Workshop on Cross-lingual Event-centric Open Analytics co-located with the 17th Extended Semantic Web Conference (ESWC 2020), Heraklion, Crete, Greece, June 3, 2020 (online event due to COVID-19 outbreak)}, volume = {2611}, pages = {17--31}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Graux, Damien; Sejdiu, Gezim; Stadler, Claus; Napolitano, Giulio; Lehmann, Jens MINDS: A Translator to Embed Mathematical Expressions Inside SPARQL Queries Inproceedings Semantic Systems. In the Era of Knowledge Graphs - 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7-10, 2020, Proceedings, pp. 104–117, Springer, 2020. @inproceedings{DBLP:conf/i-semantics/GrauxSSN020, title = {MINDS: A Translator to Embed Mathematical Expressions Inside SPARQL Queries}, author = {Damien Graux and Gezim Sejdiu and Claus Stadler and Giulio Napolitano and Jens Lehmann}, url = {https://doi.org/10.1007/978-3-030-59833-4_7}, doi = {10.1007/978-3-030-59833-4_7}, year = {2020}, date = {2020-01-01}, booktitle = {Semantic Systems. In the Era of Knowledge Graphs - 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7-10, 2020, Proceedings}, volume = {12378}, pages = {104--117}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Musyaffa, Fathoni A; Lehmann, Jens; Jabeen, Hajira Cross-administration comparative analysis of open fiscal data Inproceedings ICEGOV 2020: 13th International Conference on Theory and Practice of Electronic Governance, Athens, Greece, 23-25 September, 2020, pp. 459–468, ACM, 2020. @inproceedings{DBLP:conf/icegov/Musyaffa0J20, title = {Cross-administration comparative analysis of open fiscal data}, author = {Fathoni A. Musyaffa and Jens Lehmann and Hajira Jabeen}, url = {https://doi.org/10.1145/3428502.3428573}, doi = {10.1145/3428502.3428573}, year = {2020}, date = {2020-01-01}, booktitle = {ICEGOV 2020: 13th International Conference on Theory and Practice of Electronic Governance, Athens, Greece, 23-25 September, 2020}, pages = {459--468}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Payrosangari, Samin; Sadeghi, Afshin; Graux, Damien; Lehmann, Jens Meta-hyperband: Hyperparameter Optimization with Meta-learning and Coarse-to-Fine Inproceedings Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4-6, 2020, Proceedings, Part II, pp. 335–347, Springer, 2020. @inproceedings{DBLP:conf/ideal/PayrosangariSG020, title = {Meta-hyperband: Hyperparameter Optimization with Meta-learning and Coarse-to-Fine}, author = {Samin Payrosangari and Afshin Sadeghi and Damien Graux and Jens Lehmann}, url = {https://doi.org/10.1007/978-3-030-62365-4_32}, doi = {10.1007/978-3-030-62365-4_32}, year = {2020}, date = {2020-01-01}, booktitle = {Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4-6, 2020, Proceedings, Part II}, volume = {12490}, pages = {335--347}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Nayyeri, Mojtaba; Zhou, Xiaotian; Vahdati, Sahar; Izanloo, Reza; Yazdi, Hamed Shariat; Lehmann, Jens Let the Margin SlidE for Knowledge Graph Embeddings via a Correntropy Objective Function Inproceedings 2020 International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, United Kingdom, July 19-24, 2020, pp. 1–9, IEEE, 2020. @inproceedings{DBLP:conf/ijcnn/NayyeriZVIY020, title = {Let the Margin SlidE for Knowledge Graph Embeddings via a Correntropy Objective Function}, author = {Mojtaba Nayyeri and Xiaotian Zhou and Sahar Vahdati and Reza Izanloo and Hamed Shariat Yazdi and Jens Lehmann}, url = {https://doi.org/10.1109/IJCNN48605.2020.9207513}, doi = {10.1109/IJCNN48605.2020.9207513}, year = {2020}, date = {2020-01-01}, booktitle = {2020 International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, United Kingdom, July 19-24, 2020}, pages = {1--9}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Bader, Sebastian R; Pullmann, Jaroslav; Mader, Christian; Tramp, Sebastian; Quix, Christoph; Müller, Andreas W; Akyürek, Haydar; Böckmann, Matthias; Imbusch, Benedikt T; Lipp, Johannes; Geisler, Sandra; Lange, Christoph The International Data Spaces Information Model - An Ontology for Sovereign Exchange of Digital Content Inproceedings The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part II, pp. 176–192, Springer, 2020. @inproceedings{DBLP:conf/semweb/BaderPMTQMABILG20, title = {The International Data Spaces Information Model - An Ontology for Sovereign Exchange of Digital Content}, author = {Sebastian R. Bader and Jaroslav Pullmann and Christian Mader and Sebastian Tramp and Christoph Quix and Andreas W. Müller and Haydar Akyürek and Matthias Böckmann and Benedikt T. Imbusch and Johannes Lipp and Sandra Geisler and Christoph Lange}, url = {https://doi.org/10.1007/978-3-030-62466-8_12}, doi = {10.1007/978-3-030-62466-8_12}, year = {2020}, date = {2020-01-01}, booktitle = {The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part II}, volume = {12507}, pages = {176--192}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Nayyeri, Mojtaba; Xu, Chengjin; Vahdati, Sahar; Vassilyeva, Nadezhda; Sallinger, Emanuel; Yazdi, Hamed Shariat; Lehmann, Jens Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them Inproceedings The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I, pp. 438–455, Springer, 2020. @inproceedings{DBLP:conf/semweb/NayyeriXVVSYL20, title = {Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them}, author = {Mojtaba Nayyeri and Chengjin Xu and Sahar Vahdati and Nadezhda Vassilyeva and Emanuel Sallinger and Hamed Shariat Yazdi and Jens Lehmann}, url = {https://doi.org/10.1007/978-3-030-62419-4_25}, doi = {10.1007/978-3-030-62419-4_25}, year = {2020}, date = {2020-01-01}, booktitle = {The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I}, volume = {12506}, pages = {438--455}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Rose, Jewgeni; Lehmann, Jens CASQAD - A New Dataset for Context-Aware Spatial Question Answering Inproceedings The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part II, pp. 3–17, Springer, 2020. @inproceedings{DBLP:conf/semweb/RoseL20, title = {CASQAD - A New Dataset for Context-Aware Spatial Question Answering}, author = {Jewgeni Rose and Jens Lehmann}, url = {https://doi.org/10.1007/978-3-030-62466-8_1}, doi = {10.1007/978-3-030-62466-8_1}, year = {2020}, date = {2020-01-01}, booktitle = {The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part II}, volume = {12507}, pages = {3--17}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Xu, Chenjin; Nayyeri, Mojtaba; Alkhoury, Fouad; Yazdi, Hamed Shariat; Lehmann, Jens Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding Inproceedings The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I, pp. 654–671, Springer, 2020. @inproceedings{DBLP:conf/semweb/XuNAYL20, title = {Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding}, author = {Chenjin Xu and Mojtaba Nayyeri and Fouad Alkhoury and Hamed Shariat Yazdi and Jens Lehmann}, url = {https://doi.org/10.1007/978-3-030-62419-4_37}, doi = {10.1007/978-3-030-62419-4_37}, year = {2020}, date = {2020-01-01}, booktitle = {The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I}, volume = {12506}, pages = {654--671}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |