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We are very pleased to announce that our group got a paper accepted for presentation at SEMANTICS21. SEMANTiCS conference is the leading European conference on Semantic Technologies and AI. Researchers, industry experts and business leaders can develop a thorough understanding of trends and application scenarios in the fields of Machine Learning, Data Science, Linked Data and Natural Language Processing.

Here is the abstract and the link to the paper:

We are very pleased to announce that our group got two papers accepted for presentation at ECML21. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases is the premier European machine learning and data mining conference and builds upon over 19 years of successful events and conferences held across Europe.

Here are the abstract and the links to the paper:

• Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities
  By Afshin Sadeghi, Diego Collarana, Damien Graux and Jens Lehmann.
  Abstract

  Knowledge graphs embeddings (KGE) are lately at the center of many artificial intelligence studies due to their applicability for solving downstream tasks, including link prediction and node classification. However, most Knowledge Graph embedding models encode, into the vector space, only the local graph structure of an entity, i.e., information of the 1-hop neighborhood. Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs. This work proposes a novel KGE method named Graph Feature Attentive Neural Network (GFA-NN) that computes graphical features of entities. As a consequence, the resulting embeddings are attentive to two types of global network features. First, nodes’ relative centrality is based on the observation that some of the entities are more “prominent” than the others. Second, the relative position of entities in the graph. GFA-NN computes several centrality values per entity, generates a random set of reference nodes’ entities, and computes a given entity’s shortest path to each entity in the reference set. It then learns this information through optimization of objectives specified on each of these features. We investigate GFA-NN on several link prediction benchmarks in the inductive and transductive setting and show that GFA-NN achieves on-par or better results than state-of-the-art KGE solutions.
• VOGUE: Answer Verbalization through Multi-Task Learning
  By Endri Kacupaj, Shyamnath Premnadh, Kuldeep Singh , Jens Lehmann and Maria Maleshkova.
  Abstract

  In recent years, there have been significant developments in Question Answering over Knowledge Graphs (KGQA). Despite all the notable advancements, current KGQA systems only focus on answer generation techniques and not on answer verbalization. However, in real-world scenarios (e.g., voice assistants such as Alexa, Siri, etc.), users prefer verbalized answers instead of a generated response. This paper addresses the task of answer verbalization for (complex) question answering over knowledge graphs. In this context, we propose a multi-task-based answer verbalization framework: VOGUE (Verbalization thrOuGh mUlti-task lEarning). The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm. Our framework can generate results based on using questions and queries as inputs concurrently. VOGUE comprises four modules that are trained simultaneously through multi-task learning. We evaluate our framework on existing datasets for answer verbalization, and it outperforms all current baselines on both BLEU and METEOR scores.

We are happy to announce that our paper “Trans4E: Link Prediction on Scholarly Knowledge Graph” has been published in Neurocomputing. Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

Here is the abstract and the link to the paper:

We are very pleased to announce that our group got four papers accepted for presentation at ESWC2021. The ESWC is a major venue for discussing the latest scientific results and technology innovations around semantic technologies. Building on its past success, ESWC is seeking to broaden its focus to span other relevant related research areas in which Web semantics plays an important role. The goal of the Semantic Web is to create a Web of knowledge and services in which the semantics of content is made explicit and content is linked to both other content and services allowing novel applications to combine content from heterogeneous sites in unforeseen ways and support enhanced matching between users needs and content. This network of knowledge-based functionality will weave together a large network of human knowledge, and make this knowledge machine-processable to support intelligent behaviour by machines. Creating such an interlinked Web of knowledge which spans unstructured text, structured data (e.g. RDF) as well as multimedia content and services requires the collaboration of many disciplines, including but not limited to: Artificial Intelligence, Natural Language Processing, Databases and Information Systems, Information Retrieval, Machine Learning, Multimedia, Distributed Systems, Social Networks, Web Engineering, and Web Science.

Here are the abstracts and the links to the papers:

• Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
  By Debanjan Chaudhuri, Md Rashad Al Hasan Rony, and Jens Lehmann.
  Abstract

  Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge. Knowledge Graphs (KG) can be viewed as an abstraction of the real world, which can potentially facilitate a dialogue system to produce knowledge grounded responses. However, integrating KGs into the dialogue generation process in an end-to-end manner is a non-trivial task. This paper proposes a novel architecture for integrating KGs into the response generation process by training a BERT model that learns to answer using the elements of the KG (entities and relations) in a multi-task, end-to-end setting. The k-hop subgraph of the KG is incorporated into the model during training and inference using Graph Laplacian. Empirical evaluation suggests that the model achieves better knowledge groundedness (measured via Entity F1 score) compared to other state-of-the-art models for both goal and non-goal oriented dialogues.
• Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs
  By Joan Plepi,, Endri Kacupaj, Kuldeep Singh, Harsh Thakkar, and Jens Lehmann.
  Abstract

  Neural semantic parsing approaches have been widely used for Question Answering (QA) systems over knowledge graphs. Such methods provide the flexibility to handle QA datasets with complex queries and a large number of entities. In this work, we propose a novel framework named CARTON, which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph. Our framework consists of a stack of pointer networks as an extension of a context transformer model for parsing the input question and the dialog history. The framework generates a sequence of actions that can be executed on the knowledge graph. We evaluate CARTON on a standard dataset for complex sequential question answering on which CARTON outperforms all baselines. Specifically, we observe performance improvements in F1-score on eight out of ten question types compared to the previous state of the art. For logical reasoning questions, an improvement of 11 absolute points is reached.
• ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation
  By Endri Kacupaj, Barshana Banerjee, Kuldeep Singh, and Jens Lehmann.
  Abstract

  This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG). The dataset was created using a semi-automated framework for generating diverse paraphrasing of the answers using techniques such as back-translation. The existing datasets for conversational question answering over KGs (single-turn/multi-turn) focus on question paraphrasing and provide only up to one answer verbalization. However, ParaQA contains 5000 question-answer pairs with a minimum of two and a maximum of eight unique paraphrased responses for each question. We complement the dataset with baseline models and illustrate the advantage of having multiple paraphrased answers through commonly used metrics such as BLEU and METEOR. The ParaQA dataset is publicly available on a persistent URI for broader usage and adaptation in the research community.
• A Virtual Knowledge Graph for Enabling DefectTraceability and Customer Service Analytics
  By Nico Wilhelm, Diego Collarana, and Jens Lehmann.
  Abstract

  In this paper, we showcase the implementation of a semantic information model and a virtual knowledge graph at ZF Friedrichshafen AG company, with two main goals in mind: 1) integration of heterogeneous data sources following a pay-as-you-go approach; and the 2) combination core domain concepts from ZF’s production line with meta-data of its internal data sources. We employ the developed semantic information model in two use cases, defect traceability and customer service, demonstrating and discussing the benefits and opportuni-ties provided by following an agile semantic virtual integration approach.

We are very pleased to announce that our group got a paper accepted for presentation at ECIR 2021. The ECIR conference is the premier European forum for the presentation of new research results in the broadly conceived area of Information Retrieval (IR), and has a strong focus on the active participation of early-career researchers. The General Chairs of ECIR 2021 invite researchers, academics, students, and industry leaders working in the field to join us online for a rich program featuring full-paper and poster presentations, system demonstrations, tutorials, workshops, an industry-oriented event, and great social events.

Here is the abstract and the link to the paper:

We are very pleased to announce that our group got a paper accepted for presentation at NAACL21.The North American Chapter of the Association for Computational Linguistics (NAACL) provides a regional focus for members of the Association for Computational Linguistics (ACL) in North America as well as in Central and South America, organizes annual conferences, promotes cooperation and information exchange among related scientific and professional societies, encourages and facilitates ACL membership by people and institutions in the Americas, and provides a source of information on regional activities for the ACL Executive Committee.

Here is the abstract and the link to the paper:

We are very pleased to announce that our group got a paper accepted for presentation at IDA2021. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.

Here is the abstract and the link to the paper:

We are very pleased to announce that our group got a paper accepted for presentation at IJCNN 2021. The annual International Joint Conference on Neural Networks (IJCNN) is the flagship conference of the IEEE Computational Intelligence Society and the International Neural Network Society. It covers a wide range of topics in the field of neural networks, from biological neural network modeling to artificial neural computation.

Here is the abstract and the link to the paper:

We are happy to announce that we got a paper accepted for presentation at ACL 2021 (Association for Computational Linguistics). ACL is a premier Natural Language Processing conference. In the paper, we investigate the efficient integration of knowledge graphs into Transformer-based decoder architectures. The approach allows to integrate knowledge graphs into large-scale language models like GPT-2 or GPT-3, which leads to more comprehensive and interesting dialogues with such models.

Here is the pre-print of the accepted paper with its abstract: