Paper Accepted at ACM SAC

We are very pleased to announce that our group got a paper accepted for presentation at ACM SAC. The ACM Symposium on Applied Computing (SAC) has been a primary and international forum for applied computer scientists, computer engineers and application developers to gather, interact and present their work.

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

Towards the Semantic Formalization of Science” by Said Fathalla, Soren Auer, Christoph Lange.
Abstract:The past decades have witnessed a huge growth in scholarly information published on the Web, mostly in unstructured or semi-structured formats, which hampers scientific literature exploration and scientometric studies. Past studies on ontologies for structuring scholarly information focused on describing scholarly articles’ components, such as document structure, metadata and bibliographies, rather than the scientific work itself. Over the past four years, we have been developing the Science Knowledge Graph Ontologies (SKGO), a set of ontologies for modeling the research findings in various fields of modern science resulting in a knowledge graph. Here, we introduce this ontology suite and discuss the design considerations taken into account during its development. We deem that within the next few years, a science knowledge graph is likely to become a crucial component for organizing and exploring scientific work.

Papers Accepted at ECAI

Logo ECAI2020

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.



Quantum Natural Language Processing (QNLP) in Oxford

From 5 to 6 December, a conference on QNLP took place at St. Aldate’s Church in Oxford. This event was organized by the Quantum Group at the Department of Computer Science of the University of Oxford with support from the companies Cambridge Quantum Computing (CQC) and IBM. The two members Cedric Möller and Daniel Steinigen of the SDA team in Dresden participated in the conference. This was also the first conference ever about this combination of NLP with quantum computing.

Quantum Artificial Intelligence (QAI) has become increasingly interesting for research activities in the recent years. Noisy intermediate-scale quantum (NISQ) computers already provide the ability to perform algorithms and to find possible advantages for NLP. Since mathematical foundations of quantum theory are very similar to those of compositional NLP with applied category theory, quantum computers should provide a natural setting for compositional NLP tasks [1].

[1] Zeng, Coecke – Quantum Algorithms for Compositional Natural Language Processing https://arxiv.org/abs/1608.01406