Purpose	To examine techniques for using ontologies to perform logical reasoning

Ontologies, especially those expressed using languages such as OWL, are logical theories. This track examines how one can use ontologies for logical reasoning. = Session 1, Track C: Ontologies and Reasoning

• Meeting Page

Agenda

• 12:30pm – 12:40pm
  • Introduction to the Reasoning and Ontologies Track
  • Donna Fritzsche and Ram D. Sriram
  • Slides
• 12:40pm – 1:10pm
  • Reasoning with Ontologies in Ontohub Slides
  • Abstract: Reasoning with ontologies can be limited by the reasoners' resources, in particular time limits. These are often reached by reasoning problems inside large knowledge bases. The performance, however, can be increased significantly by omitting irrelevant premises when invoking the reasoner because often, only a fraction of the premises are needed for a proof. In my presentation I will show the web platform Ontohub that primarily serves as an ontology repository, but also allows to reason while utilising automated premise selection.
  • Bio: Eugen Kuksa is a PhD Student at the Faculty of Computer Science at the University of Magdeburg, Germany. He has a Master's degree in Computer Science and a Bachelor's degree in Mathematics from the University of Bremen, Germany. His research is on the automatic selection for automated reasoning to improve reasoning time and capability. He is developing the web platform Ontohub which is a repository for ontologies.
  • See also: https://ontohub.org/
• 1:10pm – 1:40 pm
  • On the Roles of Logical Axiomatizations for Ontologies Slides
  • Abstract: Expressing ontologies by means of logical axioms serves multiple purposes. This includes inferential reasoning under the open world semantics, but is not restricted to this. Logical axioms can also be viewed as integrity constraints on the data and used for closed-world reasoning. They can serve for quality assurance purposes for ontology design, for data curation, or for generating implicit knowledge. At the same time, they also serve for disambiguation of the meaning of ontologies. In this talk, we will discuss the different roles logical axiomatizations can play and shed light on the state of the art regarding these aspects. 
  • Bio: Pascal Hitzler is endowed NCR Distinguished Professor and Director of Data Science at the Department of Computer Science and Engineering at Wright State University in Dayton, Ohio, U.S.A. His research record lists over 350 publications in such diverse areas as semantic web, neural-symbolic integration, knowledge representation and reasoning, machine learning, denotational semantics, and set-theoretic topology. He is Editor-in-chief of the Semantic Web journal by IOS Press, and of the IOS Press book series Studies on the Semantic Web. He is co-author of the W3C Recommendation OWL 2 Primer, and of the book Foundations of Semantic Web Technologies by CRC Press, 2010 which was named as one out of seven Outstanding Academic Titles 2010 in Information and Computer Science by the American Library Association's Choice Magazine, and has translations into German and Chinese. He is on the editorial board of several journals and book series and is a founding steering committee member of the Neural-Symbolic Learning and Reasoning (NeSy) workshop series, and of the Association for Ontology Design and Patterns (ODPA). He also frequently acts as conference chair in various functions.
  • For more information, see http://www.pascal-hitzler.de
• 1:40pm – 2:10pm
  • Cognitive Probability Graphs need an Ontology Slides
  • Abstract: At Franz we are marketing a new concept called "the cognitive probability graph". Recently we have been involved in several semantic graph database projects where we dealt with similarity reasoning, uncertainty, probabilistic events, correlations and predictions. In this presentation, I will discuss examples from e-commerce and healthcare. I'll show how we take the results of statistics and machine learning and put it back in our semantic graph database and then visually explore the results. We believe that a more formal ontology is needed to express concepts like similarity and uncertainty. We hope that the discussions during the summit will provide additional insights into how to represent the output of various types of analytics and machine learning.
  • Bio: Jans Aasman started his career as an experimental and cognitive psychologist, earning his PhD in cognitive science with a detailed model of car driver behavior using Lisp and Soar. He has spent most of his professional life in telecommunications research, specializing in intelligent user interfaces and applied artificial intelligence projects. From 1995 to 2004, he was also a part-time professor in the Industrial Design department of the Technical University of Delft. Jans is currently the CEO of Franz Inc., the leading supplier of commercial, persistent, and scalable Graph Database products that provide the storage layer for powerful reasoning and ontology modeling capabilities for Cognitive Computing applications.
• 2:10pm – 2:30pm
  • Discussion