Papers Accepted at ECAI

We are very pleased to announce that our group got two papers accepted for presentation at ECAI2020 (European Conference on Artificial Intelligence), Europe’s premier AI Research venue. Under the motto “Paving the way towards Human-Centric AI” ECAI provides an opportunity for researchers to present and discuss about the best AI research, developments, applications and results.

Here are the pre-prints of the accepted papers with their abstracts:

  • Distantly Supervised Question Parsing” by Hamid Zafar, Maryam Tavakol, Jens Lehmann.
    Abstract: The emergence of structured databases for Question Answering (QA) systems has led to developing methods, in which the problem of learning the correct answer efficiently is based on a linking task between the constituents of the question and the corresponding entries in the database. As a result, parsing the questions in order to determine their main elements, which are required for answer retrieval, becomes crucial. However, most datasets for question answering systems lack gold annotations for parsing, i.e., labels are only available in the form of (question, formal-query, answer). In this paper, we propose a distantly supervised learning framework based on reinforcement learning to learn the mentions of entities and relations in questions. We leverage the provided formal queries to characterize delayed rewards for optimizing a policy gradient objective for the parsing model. An empirical evaluation of our approach shows a significant improvement in the performance of entity and relation linking compared to the state of the art. We also demonstrate that a more accurate parsing component enhances the overall performance of QA systems.
  • “MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs by Afshin Sadeghi, Damien Graux, Hamed Shariat Yazdi, and Jens Lehmann. 
    Abstract: Over the past decade, knowledge graphs became popular for capturing structureddomain knowledge. Relational learning models enable the prediction of miss-ing links inside knowledge graphs. More specifically, latent distance approachesmodel the relationships among entities via a distance between latent representa-tions. Translating embedding models (e.g., TransE) are among the most popularlatent distance approaches which use one distance functionto learn multiple re-lation patterns. However, they are not capable of capturingsymmetric relations.They also force relations with reflexive patterns to become symmetric and tran-sitive. In order to improve distance based embedding, we propose multi-distanceembeddings (MDE). Our solution is based on the idea that by learning indepen-dent embedding vectors for each entity and relation one can aggregate contrastingdistance functions. Benefiting from MDE, we also develop supplementary dis-tances resolving the above-mentioned limitations of TransE. We further proposean extended loss function for distance based embeddings andshow that MDE andTransE are fully expressive using this loss function. Furthermore, we obtain abound on the size of their embeddings for full expressivity.Our empirical resultsshow that MDE significantly improves the translating embeddings and outper-forms several state-of-the-art embedding models on benchmark datasets.