KMi Seminars
Scalable Query Answering over Linked Ontological Data
This event took place on Wednesday 15 July 2009 at 11:30

 
DR. Jeff Pan University of Aberdeen, UK

Scalable query answering over ontologies is one of the most useful and important services to support Semantic Web applications. For example, more and more ontological vocabulary used in linked data. Approximation has been identified as a potential way to reduce the complexity of query answering over OWL DL ontologies. Existing approaches are mainly based on syntactic approximation of ontological axioms and queries. In this talk, I will firstly give an overview of description logics in general, which are the underpinning of the OWL DL standard, and query answering over DL-based ontologies in particular. Then I propose to recast the idea of knowledge compilation into semantically approximating OWL DL ontologies with DL-Lite ontologies, against which query answering has only LogSpace data complexity. We identify a useful category of queries for which our approach guarantees also completeness. If time allows, I will also report on the implementation of our approach in the TrOWL system and preliminary, but encouraging, benchmark results which compare TrOWL's response times on queries in a well known ontology benchmark with those of existing ontology reasoning systems. I will conclude the talk with discussions on some future steps.

 
KMi Seminars Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.

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