Invited Talk

Franz Baader

Full Professor, TU Dresden, Germany

Franz Baader is full professor for Theoretical Computer Science at TU Dresden (Germany) since 2002 and director of the Institute for Theoretical Computer Science at TU Dresden since 2005. He has received his doctoral degree (Dr.-Ing.) in Computer Science from the University of Erlangen-Nürnberg in 1989, and from 1989 to 1993 was senior scientist and project leader at the Germany Research Center for Artificial Intelligence (DFKI) in Kaiserslautern and Saarbrücken. From 1993 to 2002 he was professor for Theoretical Computer Science at RWTH Aachen.

His main research areas are Logic in Computer Science and Artificial Intelligence, and there in particular Automated Deduction and Knowledge Representation. He and his research group have worked on Description Logics for 30 years, and have laid the logical and algorithmic foundations for the Description Logics underlying OWL and the OWL 2 profile OWL 2 EL. They have also developed algorithmic tools that support building and maintaining large ontologies. Franz Baader is a fellow of the European Association for Artificial Intelligence (EurAI) since 2004 and a member of the Academia Europaea since 2011. In 2020 he received the Herbrand Award for Distinguished Contributions to Automated Reasoning.

Title

Optimal Repairs in the Description Logic EL Revisited

Abstract

Ontologies based on Description Logics (DLs) may contain errors, which are usually detected when reasoning produces consequences that follow from the ontology, but do not hold in the application domain modeled by the ontology. In previous work, we have investigated repair approaches for ontologies written in the inexpressive DL EL, which is, e.g., used in biomedical ontologies. In particular, we have developed approaches producing repairs that are optimal in the sense that they preserve a maximal set of consequences. In this talk, I will explain the advantages that optimal repairs have over previously introduced notions of ontology repair, and then give a gentle introduction into the approaches we have developed for computing them. I will also sketch new results that address the problems that optimal repairs may become very large or need not even exist in some cases. Basically, one can deal with these problems by introducing concise representations of optimal repairs.

Yudai Nakamoto

SCSK Corporation

Yudai Nakamoto joined SCSK Corporation in 2020. From 2021, he has been working on a method by knowledge graph to handle terminology and background knowledge at low cost in the Japanese model of BERT, a large-scale language model. Currently, he is working on the conceptual verification of each use case and problem-solving for the use of generative AI in business applications.

Title

Generative AI in Business and Expectations for Knowledge Graphs

Abstract

Generative AI, including ChatGPT, is spreading rapidly, and various organizations are using it on a trial basis or introducing it into their products. SCSK Corporation is also actively working on the use of generative AI, including the construction of a question-and-answer system based on ChatGPT. In this presentation, I will talk about a question-and-answering system utilizing a vector database that handle unique data, and discuss the use of knowledge graphs as a solution to the system’s issues.