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Ian Horrocks
Researcher at University of Oxford
Publications - 488
Citations - 40046
Ian Horrocks is an academic researcher from University of Oxford. The author has contributed to research in topics: Ontology (information science) & Description logic. The author has an hindex of 87, co-authored 472 publications receiving 38785 citations. Previous affiliations of Ian Horrocks include The Turing Institute & National and Kapodistrian University of Athens.
Papers
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Proceedings Article
Towards a UMLS-based silver standard for matching biomedical ontologies
TL;DR: A silver standard based on the UMLS Metathesaurus to align NCI, FMA and SNOMED CT is proposed and aims at being exploited within the OAEI and SEALS Campaigns.
Proceedings Article
KeywDB: A System for Keyword−Driven Ontology−to−RDB Mapping Construction
Dmitriy Zheleznyakov,Evgeny Kharlamov,Vidar Norstein Klungre,Martin G. Skjæveland,Dag Hovland,Martin Giese,Ian Horrocks,Arild Waaler +7 more
TL;DR: This demo presents the system KeywDB, a system that facilitates construction of ontology-to-RDB mappings in an interactive fashion that relies on techniques for keyword query answering over RDBs.
Journal Article
OptiqueVQS: visual query formulation for OBDA
Ahmet Soylu,Evgeny Kharlamov,Dmitriy Zheleznyakov,Ernesto Jiménez-Ruiz,Martin Giese,Ian Horrocks +5 more
TL;DR: Ontology Based Data Access (OBDA) is a recently proposed prominent approach that aims at providing domain experts with a direct access to available enterprise data sources without IT-experts being involved.
Journal ArticleDOI
The delay and window size problems in rule-based stream reasoning
TL;DR: In this paper , a suite of decision problems can be exploited by stream reasoning algorithms to tackle the aforementioned challenges, and provide tight complexity bounds for a core temporal extension of Datalog.
Posted Content
The Window Validity Problem in Rule-Based Stream Reasoning.
TL;DR: In this article, a recursive fragment of temporal Datalog with tractable data complexity is proposed to minimize the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time.