Abstract: The need for representing knowledge is ubiquitous in applications: travel apps need to know about transport links and accommodation; music apps need to know about artists and albums; (social) networking apps need to know about user profiles and preferences; and ""virtual assistants"" need to answer questions such as ""what is the height of the Eiffel Tower"". Although real-world knowledge can be complex, a great deal can be achieved even with simple representations such as knowledge graphs; Google's knowledge graph, for example, contains more than 50 billion facts, and enables Google to provide direct answers to many questions.
Maintaining and extending such a knowledge graph is, however, extremely challenging. This problem can be mitigated to some extent by using structural rules, often called an ontology, to reduce the need for and identify obvious errors in explicitly stored facts. However, answering queries over ontology augmented knowledge graphs requires more complex reasoning, and scaling this up to large graphs is also challenging.
In this talk I will introduce basic knowledge graphs, illustrate the benefits of augmenting them with ontologies, explore some inherent challenges, and compare various techniques for query answering over ontology augmented large-scale knowledge graphs.
Bio: Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Visiting Professor in the Department of Informatics at the University of Oslo. He is a Fellow of the Royal Society, a member of Academia Europaea, an ECCAI Fellow, and a Fellow of the British Computer Society. His research interests include logic-based knowledge representation and reasoning and semantic technologies, with a particular focus on ontology languages and applications. He was an author of the OIL, DAML+OIL, and OWL ontology language standards, chaired the W3C working group that standardized OWL 2, and developed many of the algorithms, optimization techniques, and reasoning systems that underpin OWL applications. His recent work includes query answering over ontologies and very large data sets, and applications in domains such as engineering, oil and gas, finance and medicine.