Dr. Mohamed Sherif – Smart Data Analytics

Dr. Mohamed Sherif left SDA. The profile below reflects the status at the point of his departure and is no longer updated.

Senior Researcher
Agile Knowledge Engineering and Semantic Web (AKSW)
University of Leipzig

Profiles: LinkedIn, Google Scholar, DBLP, GitHub, Google+

Room P905
Augustusplatz 10, 04109 Leipzig

sherif@informatik.uni-leipzig.de

Short CV

Dr. Mohamed Sherif is a Senior Researcher at the University of Leipzig. Mohamed’s research interests are in the area of RDF Workflows, Link Discovery and Fusion.

Research Interests

Machine Learning
RDF Workflows
Link Discovery and
Fusion

Publications

2021

Ahmed, Abdullah Fathi; Sherif, Mohamed Ahmed; Moussallem, Diego; Ngomo, Axel-Cyrille Ngonga

Multilingual Verbalization and Summarization for Explainable Link Discovery Journal Article

In: Data Knowl. Eng., 133 , pp. 101874, 2021.

Links | BibTeX

Zahera, Hamada M.; Jalota, Rricha; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

I-AID: Identifying Actionable Information From Disaster-Related Tweets Journal Article

In: IEEE Access, 9 , pp. 118861–118870, 2021.

Links | BibTeX

2020

Röder, Michael; Sherif, Mohamed Ahmed; Saleem, Muhammad; Conrads, Felix; Ngomo, Axel-Cyrille Ngonga

Benchmarking the Lifecycle of Knowledge Graphs Incollection

In: Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges, 47 , pp. 73–97, IOS Press, 2020.

Links | BibTeX

Bigerl, Alexander; Conrads, Felix; Behning, Charlotte; Sherif, Mohamed Ahmed; Saleem, Muhammad; Ngomo, Axel-Cyrille Ngonga

Tentris - A Tensor-Based Triple Store Inproceedings

In: The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2-6, 2020, Proceedings, Part I, pp. 56–73, Springer, 2020.

Links | BibTeX

Georgala, Kleanthi; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

LIGER - link discovery with partial recall Inproceedings

In: Proceedings of the 15th International Workshop on Ontology Matching co-located with the 19th International Semantic Web Conference (ISWC 2020), Virtual conference (originally planned to be in Athens, Greece), November 2, 2020, pp. 87–91, CEUR-WS.org, 2020.

Links | BibTeX

Georgala, Kleanthi; Sherif, Mohamed Ahmed; Röder, Michael; Ngomo, Axel-Cyrille Ngonga

Applying edge-counting semantic similarities to link discovery: scalability and accuracy Inproceedings

Links | BibTeX

Sherif, Mohamed Ahmed; Dreßler, Kevin; Ngomo, Axel-Cyrille Ngonga

LIGON - link discovery with noisy oracles Inproceedings

Links | BibTeX

Röder, Michael; Sherif, Mohamed Ahmed; Saleem, Muhammad; Conrads, Felix; Ngomo, Axel-Cyrille Ngonga

Benchmarking Knowledge Graphs on the Web Journal Article

In: CoRR, abs/2002.06039 , 2020.

Links | BibTeX

Zahera, Hamada M.; Sherif, Mohamed Ahmed; Ngonga, Axel

Semantic-based End-to-End Learning for Typhoon Intensity Prediction Journal Article

In: CoRR, abs/2003.13779 , 2020.

Links | BibTeX

2019

Athanasiou, Spiros; Giannopoulos, Giorgos; Graux, Damien; Karagiannakis, Nikos; Lehmann, Jens; Ngomo, Axel-Cyrille Ngonga; Patroumpas, Kostas; Sherif, Mohamed Ahmed; Skoutas, Dimitrios

Big POI data integration with Linked Data technologies Inproceedings

In: Advances in Database Technology - 22nd International Conference on Extending Database Technology, EDBT 2019, Lisbon, Portugal, March 26-29, 2019, pp. 477–488, OpenProceedings.org, 2019.

Links | BibTeX

Ahmed, Abdullah Fathi; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

Do your Resources Sound Similar?: On the Impact of Using Phonetic Similarity in Link Discovery Inproceedings

In: Proceedings of the 10th International Conference on Knowledge Capture, K-CAP 2019, Marina Del Rey, CA, USA, November 19-21, 2019, pp. 53–60, ACM, 2019.

Links | BibTeX

Zahera, Hamada M.; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction Inproceedings

In: Proceedings of the 10th International Conference on Knowledge Capture, K-CAP 2019, Marina Del Rey, CA, USA, November 19-21, 2019, pp. 231–234, ACM, 2019.

Links | BibTeX

Ahmed, Abdullah Fathi; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

LSVS: Link Specification Verbalization and Summarization Inproceedings

In: Natural Language Processing and Information Systems - 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, Salford, UK, June 26-28, 2019, Proceedings, pp. 66–78, Springer, 2019.

Links | BibTeX

Sherif, Mohamed Ahmed; Svetlana, Pestryakova; Dreßler, Kevin; Ngomo, Axel-Cyrille Ngonga

LimesWebUI - Link Discovery Made Simple Inproceedings

In: Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 26-30, 2019, pp. 205–208, CEUR-WS.org, 2019.

Links | BibTeX

Zahera, Hamada M.; Elgendy, Ibrahim A.; Jalota, Rricha; Sherif, Mohamed Ahmed

Fine-tuned BERT Model for Multi-Label Tweets Classification Inproceedings

In: Proceedings of the Twenty-Eighth Text REtrieval Conference, TREC 2019, Gaithersburg, Maryland, USA, November 13-15, 2019, National Institute of Standards and Technology (NIST), 2019.

Links | BibTeX

2018

Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

A systematic survey of point set distance measures for link discovery Journal Article

In: Semantic Web, 9 (5), pp. 589–604, 2018.

Abstract | Links | BibTeX

Wauer, Matthias; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

Towards a Semantic Message-driven Microservice Platform for Geospatial and Sensor Data Inproceedings

In: Proceedings of the 3rd International Workshop on Geospatial Linked Data and the 2nd Workshop on Querying the Web of Data co-located with 15th Extended Semantic Web Conference (ESWC 2018), Heraklion, Greece, June 3, 2018, pp. 47–58, CEUR-WS.org, 2018.

Links | BibTeX

Ahmed, Abdullah Fathi; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

On the Effect of Geometries Simplification on Geo-spatial Link Discovery Inproceedings

In: Proceedings of the 14th International Conference on Semantic Systems, SEMANTICS 2018, Vienna, Austria, September 10-13, 2018, pp. 139–150, Elsevier, 2018.

Links | BibTeX

Moussallem, Diego; Sherif, Mohamed Ahmed; Esteves, Diego; Zampieri, Marcos; Ngomo, Axel-Cyrille Ngonga

LIdioms: A Multilingual Linked Idioms Data Set Inproceedings

In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7-12, 2018, European Language Resources Association (ELRA), 2018.

Links | BibTeX

Ahmed, Abdullah Fathi; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

RADON2 - a buffered-intersection matrix computing approach to accelerate link discovery over geo-spatial RDF knowledge bases: OAEI2018 results Inproceedings

In: Proceedings of the 13th International Workshop on Ontology Matching co-located with the 17th International Semantic Web Conference, OM@ISWC 2018, Monterey, CA, USA, October 8, 2018, pp. 197–204, CEUR-WS.org, 2018.

Links | BibTeX

Jiménez-Ruiz, Ernesto; Saveta, Tzanina; Zamazal, Ondrej; Hertling, Sven; Röder, Michael; Fundulaki, Irini; Ngomo, Axel-Cyrille Ngonga; Sherif, Mohamed Ahmed; Annane, Amina; Bellahsene, Zohra; Yahia, Sadok Ben; Diallo, Gayo; Faria, Daniel; Kachroudi, Marouen; Khiat, Abderrahmane; Lambrix, Patrick; Li, Huanyu; Mackeprang, Maximilian; Mohammadi, Majid; Rybinski, Maciej; Balasubramani, Booma Sowkarthiga; Trojahn, Cássia

Introducing the HOBBIT platform into the ontology alignment evaluation campaign Inproceedings

Links | BibTeX

2017

Sherif, Mohamed Ahmed; Dreßler, Kevin; Smeros, Panayiotis; Ngomo, Axel-Cyrille Ngonga

Radon - Rapid Discovery of Topological Relations Inproceedings

In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pp. 175–181, AAAI Press, 2017.

Abstract | Links | BibTeX

Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga; Lehmann, Jens

Wombat - A Generalization Approach for Automatic Link Discovery Inproceedings

In: The Semantic Web - 14th International Conference, ESWC 2017, Portorov z, Slovenia, May 28 - June 1, 2017, Proceedings, Part I, pp. 103–119, 2017.

Links | BibTeX

Dreßler, Kevin; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

Radon results for OAEI 2017 Inproceedings

In: Proceedings of the 12th International Workshop on Ontology Matching co-located with the 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 21, 2017, pp. 178–184, CEUR-WS.org, 2017.

Links | BibTeX

2016

Sherif, Mohamed Ahmed

Automating Geospatial RDF Dataset Integration and Enrichment PhD Thesis

University of Leipzig, 2016.

Abstract | Links | BibTeX

@phdthesis{Sherif-thesis-2016,

title = {Automating Geospatial RDF Dataset Integration and Enrichment},

author = {Mohamed Ahmed Sherif},

url = {https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa-215708},

year = {2016},

date = {2016-12-01},

address = {Leipzig, Germany},

school = {University of Leipzig},

abstract = {Over the last years, the Linked Open Data (LOD) has evolved from a mere 12 to more than 10, 000 knowledge bases. These knowledge bases come from diverse domains including (but not limited to) publications, life sciences, social networking, government, media, linguistics. Moreover, the LOD cloud also contains a large number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge bases are commonly managed in a decentralized fashion and contain partly overlapping information. This architectural choice has led to knowledge pertaining to the same domain being published by independent entities in the LOD cloud. For example, information on drugs can be found in Diseasome as well as DBpedia and Drugbank. Furthermore, certain knowledge bases such as DBLP have been published by several bodies, which in turn has lead to duplicated content in the LOD. In addition, large amounts of geo-spatial information have been made available with the growth of heterogeneous Web of Data. The concurrent publication of knowledge bases containing related information promises to become a phenomenon of increasing importance with the growth of the number of independent data providers. Enabling the joint use of the knowledge bases published by these providers for tasks such as federated queries, cross-ontology question answering and data integration is most commonly tackled by creating links between the resources described within these knowledge bases. Within this thesis, we spur the transition from isolated knowledge bases to enriched Linked Data sets where information can be easily integrated and processed. To achieve this goal, we provide concepts, approaches and use cases that facilitate the integration and enrichment of information with other data types that are already present on the Linked Data Web with a focus on geo-spatial data. The first challenge that motivates our work is the lack of measures that use the geographic data for linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources being described by the means of vector geometry. In particular, discrepancies in granularity and error measurements across knowledge bases render the selection of appropriate distance measures for geo-spatial resources difficult. We address this challenge by evaluating existing literature for pointset measures that can be used to measure the similarity of vector geometries. Then, we present and evaluate the ten measures that we derived from the literature on samples of three real knowledge bases. The second challenge we address in this thesis is the lack of automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge bases with missing and erroneous data. To this end,we present Colibri, an unsupervised approach that allows discovering links between knowledge bases while improving the quality of the instance data in these knowledge bases. A Colibri iteration begins by generating links between knowledge bases. Then, the approach makes use of these links to detect resources with probably erroneous or missing information. This erroneous or missing infor- mation detected by the approach is finally corrected or added. The third challenge we address is the lack of scalable LD approaches for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm Optimization (DPSO), a novel load balancing technique for LD on parallel hardware based on particle-swarm optimization. We combine this approach with the Orchid algorithm for geo-spatial linking and evaluate it on real and artificial data sets. The lack of approaches for automatic updating of links of an evolving knowledge base is our fourth challenge. This challenge is addressed in this thesis by the Wombat algorithm. Wombat is a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of Link Specifications (LS). We study the theoretical characteristics of Wombat and evaluate it on different benchmark data sets. The last challenge addressed herein is the lack of automatic approaches for geo-spatial knowledge base enrichment. Thus, we propose Deer, a supervised learning approach based on a refinement operator for enriching Resource Description Framework (RDF) data sets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples. Each of the proposed approaches is implemented and evaluated against state-of-the-art approaches on real and/or artificial data sets. Moreover, all approaches are peer-reviewed and published in a con- ference or a journal paper. Throughout this thesis, we detail the ideas, implementation and the evaluation of each of the approaches. Moreover, we discuss each approach and present lessons learned. Finally, we conclude this thesis by presenting a set of possible future extensions and use cases for each of the proposed approaches.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Over the last years, the Linked Open Data (LOD) has evolved from a mere 12 to more than 10, 000 knowledge bases. These knowledge bases come from diverse domains including (but not limited to) publications, life sciences, social networking, government, media, linguistics. Moreover, the LOD cloud also contains a large number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge bases are commonly managed in a decentralized fashion and contain partly overlapping information. This architectural choice has led to knowledge pertaining to the same domain being published by independent entities in the LOD cloud. For example, information on drugs can be found in Diseasome as well as DBpedia and Drugbank. Furthermore, certain knowledge bases such as DBLP have been published by several bodies, which in turn has lead to duplicated content in the LOD. In addition, large amounts of geo-spatial information have been made available with the growth of heterogeneous Web of Data. The concurrent publication of knowledge bases containing related information promises to become a phenomenon of increasing importance with the growth of the number of independent data providers. Enabling the joint use of the knowledge bases published by these providers for tasks such as federated queries, cross-ontology question answering and data integration is most commonly tackled by creating links between the resources described within these knowledge bases. Within this thesis, we spur the transition from isolated knowledge bases to enriched Linked Data sets where information can be easily integrated and processed. To achieve this goal, we provide concepts, approaches and use cases that facilitate the integration and enrichment of information with other data types that are already present on the Linked Data Web with a focus on geo-spatial data. The first challenge that motivates our work is the lack of measures that use the geographic data for linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources being described by the means of vector geometry. In particular, discrepancies in granularity and error measurements across knowledge bases render the selection of appropriate distance measures for geo-spatial resources difficult. We address this challenge by evaluating existing literature for pointset measures that can be used to measure the similarity of vector geometries. Then, we present and evaluate the ten measures that we derived from the literature on samples of three real knowledge bases. The second challenge we address in this thesis is the lack of automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge bases with missing and erroneous data. To this end,we present Colibri, an unsupervised approach that allows discovering links between knowledge bases while improving the quality of the instance data in these knowledge bases. A Colibri iteration begins by generating links between knowledge bases. Then, the approach makes use of these links to detect resources with probably erroneous or missing information. This erroneous or missing infor- mation detected by the approach is finally corrected or added. The third challenge we address is the lack of scalable LD approaches for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm Optimization (DPSO), a novel load balancing technique for LD on parallel hardware based on particle-swarm optimization. We combine this approach with the Orchid algorithm for geo-spatial linking and evaluate it on real and artificial data sets. The lack of approaches for automatic updating of links of an evolving knowledge base is our fourth challenge. This challenge is addressed in this thesis by the Wombat algorithm. Wombat is a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of Link Specifications (LS). We study the theoretical characteristics of Wombat and evaluate it on different benchmark data sets. The last challenge addressed herein is the lack of automatic approaches for geo-spatial knowledge base enrichment. Thus, we propose Deer, a supervised learning approach based on a refinement operator for enriching Resource Description Framework (RDF) data sets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples. Each of the proposed approaches is implemented and evaluated against state-of-the-art approaches on real and/or artificial data sets. Moreover, all approaches are peer-reviewed and published in a con- ference or a journal paper. Throughout this thesis, we detail the ideas, implementation and the evaluation of each of the approaches. Moreover, we discuss each approach and present lessons learned. Finally, we conclude this thesis by presenting a set of possible future extensions and use cases for each of the proposed approaches.

Georgala, Kleanthi; Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

An Efficient Approach for the Generation of Allen Relations Inproceedings

In: ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016), pp. 948–956, IOS Press, 2016.

Links | BibTeX

Sherif, Mohamed Ahmed; Hassan, Mofeed M.; Soru, Tommaso; Ngomo, Axel-Cyrille Ngonga; Lehmann, Jens

Lion's Den: feeding the LinkLion Inproceedings

In: Proceedings of the 11th International Workshop on Ontology Matching co-located with the 15th International Semantic Web Conference (ISWC 2016), Kobe, Japan, October 18, 2016, pp. 235–236, CEUR-WS.org, 2016.

Links | BibTeX

Sherif, Mohamed Ahmed; Dreßler, Kevin; Smeros, Panayiotis; Ngomo, Axel-Cyrille Ngonga

Annex: Radon - Rapid Discovery of Topological Relations Journal Article

In: CoRR, abs/1611.06128 , 2016.

Links | BibTeX

2015

Lehmann, Jens; Athanasiou, Spiros; Both, Andreas; Buehmann, Lorenz; Garcia-Rojas, Alejandra; Giannopoulos, Giorgos; Hladky, Daniel; Hoeffner, Konrad; Grange, Jon Jay Le; Ngomo, Axel-Cyrille Ngonga; Pietzsch, Rene; Isele, Robert; Sherif, Mohamed Ahmed; Stadler, Claus; Wauer, Matthias; Westphal, Patrick

The GeoKnow Handbook Technical Report

2015.

Links | BibTeX

Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga; Lehmann, Jens

Automating RDF Dataset Transformation and Enrichment Inproceedings

In: The Semantic Web. Latest Advances and New Domains - 12th European Semantic Web Conference, ESWC 2015, Portoroz, Slovenia, May 31 - June 4, 2015. Proceedings, pp. 371–387, Springer, 2015.

Abstract | Links | BibTeX

Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

An optimization approach for load balancing in parallel link discovery Inproceedings

In: Proceedings of the 11th International Conference on Semantic Systems, SEMANTICS 2015, Vienna, Austria, September 15-17, 2015, pp. 161–168, ACM, 2015.

Links | BibTeX

Stadler, Claus; Unbehauen, Jörg; Westphal, Patrick; Sherif, Mohamed Ahmed; Lehmann, Jens

Simplified RDB2RDF Mapping Inproceedings

In: Proceedings of the Workshop on Linked Data on the Web, LDOW 2015, co-located with the 24th International World Wide Web Conference (WWW 2015), Florence, Italy, May 19th, 2015, CEUR-WS.org, 2015.

Abstract | Links | BibTeX

Lehmann, Jens; Athanasiou, Spiros; Both, Andreas; Garc'ia-Rojas, Alejandra; Giannopoulos, Giorgos; Hladky, Daniel; Grange, Jon Jay Le; Ngomo, Axel-Cyrille Ngonga; Sherif, Mohamed Ahmed; Stadler, Claus; Wauer, Matthias; Westphal, Patrick; Zaslawski, Vadim

Managing Geospatial Linked Data in the GeoKnow Project Incollection

In: The Semantic Web in Earth and Space Science. Current Status and Future Directions, 20 , pp. 51–78, IOS Press, 2015.

Links | BibTeX

Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

Semantic Quran Journal Article

In: Semantic Web, 6 (4), pp. 339–345, 2015.

Links | BibTeX

2014

Grange, Jon Jay Le; Lehmann, Jens; Athanasiou, Spiros; Rojas, Alejandra Garcia; Giannopoulos, Giorgos; Hladky, Daniel; Isele, Robert; Ngomo, Axel-Cyrille Ngonga; Sherif, Mohamed Ahmed; Stadler, Claus; Wauer, Matthias

The GeoKnow Generator: Managing Geospatial Data in the Linked Data Web Inproceedings

In: Proceedings of the Linking Geospatial Data Workshop, 2014.

Links | BibTeX

Sherif, Mohamed Ahmed; Ngomo, Axel-Cyrille Ngonga

Semantic Quran: A Multilingual Resource for Natural-Language Processing Journal Article

In: Semantic Web Journal, XXX , pp. 1-5, 2014.

Abstract | Links | BibTeX

Ngomo, Axel-Cyrille Ngonga; Sherif, Mohamed Ahmed; Lyko, Klaus

Unsupervised Link Discovery through Knowledge Base Repair Inproceedings

In: The Semantic Web: Trends and Challenges - 11th International Conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014. Proceedings, pp. 380–394, Springer, 2014.

Links | BibTeX

Sherif, Mohamed Ahmed; Coelho, Sandro; Usbeck, Ricardo; Hellmann, Sebastian; Lehmann, Jens; Brümmer, Martin; Both, Andreas

NIF4OGGD - NLP Interchange Format for Open German Governmental Data Inproceedings

In: Proceedings of the Ninth International Conference on Language Resources and Evaluation, LREC 2014, Reykjavik, Iceland, May 26-31, 2014, pp. 3524–3528, European Language Resources Association (ELRA), 2014.

Links | BibTeX

Pokharel, Suresh; Sherif, Mohamed Ahmed; Lehmann, Jens

Ontology Based Data Access and Integration for Improving the Effectiveness of Farming in Nepal Inproceedings

In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, August 11-14, 2014 - Volume I, pp. 319–326, IEEE Computer Society, 2014.

Links | BibTeX

2013

Zaveri, Amrapali; Kontokostas, Dimitris; Sherif, Mohamed Ahmed; Bühmann, Lorenz; Morsey, Mohamed; Auer, Sören; Lehmann, Jens

User-driven quality evaluation of DBpedia Inproceedings

In: I-SEMANTICS 2013 - 9th International Conference on Semantic Systems, ISEM '13, Graz, Austria, September 4-6, 2013, pp. 97–104, ACM, 2013.

Abstract | Links | BibTeX

@inproceedings{DBLP:conf/i-semantics/ZaveriKSBMAL13,

title = {User-driven quality evaluation of DBpedia},

author = {Amrapali Zaveri and 

Dimitris Kontokostas and 

Mohamed Ahmed Sherif and 

Lorenz Bühmann and 

Mohamed Morsey and 

Sören Auer and 

Jens Lehmann},

url = {http://svn.aksw.org/papers/2013/ISemantics_DBpediaDQ/public.pdf},

doi = {10.1145/2506182.2506195},

year  = {2013},

date = {2013-01-01},

booktitle = {I-SEMANTICS 2013 - 9th International Conference on Semantic Systems, 

ISEM '13, Graz, Austria, September 4-6, 2013},

pages = {97--104},

publisher = {ACM},

abstract = {Linked Open Data (LOD) comprises of an unprecedented volume of structured datasets on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowdsourced and even extracted data of relatively low quality. We present a methodology for assessing the quality of linked data resources, which comprises of a manual and a semi-automatic process. The first phase includes the detection of common quality problems and their representation in a quality problem taxonomy. In the manual process, the second phase comprises of the evaluation of a large number of individual resources, according to the quality problem taxonomy via crowdsourcing. This process is accompanied by a tool wherein a user assesses an individual resource and evaluates each fact for correctness. The semi-automatic process involves the generation and verification of schema axioms. We report the results obtained by applying this methodology to DBpedia. We identified 17 data quality problem types and 58 users assessed a total of 521 resources. Overall, 11.93% of the evaluated DBpedia triples were identified to have some quality issues. Applying the semi-automatic component yielded a total of 222,982 triples that have a high probability to be incorrect. In particular, we found that problems such as object values being incorrectly extracted, irrelevant extraction of information and broken links were the most recurring quality problems. With this study, we not only aim to assess the quality of this sample of DBpedia resources but also adopt an agile methodology to improve the quality in future versions by regularly providing feedback to the DBpedia maintainers.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}