EPSRC logo

Details of Grant 

EPSRC Reference: EP/J008346/1
Title: PrOQAW: Probabilistic Ontological Query Answering on the Web
Principal Investigator: Lukasiewicz, Professor T
Other Investigators:
Olteanu, Professor DA Gottlob, Professor G Benedikt, Professor M
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: University of Oxford
Scheme: Standard Research
Starts: 30 April 2012 Ends: 29 April 2016 Value (£): 813,812
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Sep 2011 EPSRC ICT Responsive Mode - Sep 2011 Announced
Summary on Grant Application Form
The next revolution in Web search as one of the key technologies of the Web has just started with the incorporation of ideas from the Semantic Web, aiming at transforming current Web search into some form of semantic search and query answering on the Web, by adding meaning to Web contents and queries in the form of an underlying ontology. This also allows for more complex queries, and for evaluating queries by combining knowledge that is distributed over many Web pages, i.e., by reasoning over the Web.

Realizing such semantic search and query answering on the Web by adding ontological meaning to the current Web conceptually means annotating Web pages and their contents relative to that ontology, i.e., relating Web pages and their contents to and thus also via that ontology. From a practical perspective, one of the most promising ways of realizing this is to perform data extraction from the current Web relative to the underlying ontology, store the extracted data in a knowledge base, and realize semantic search and query answering on this knowledge base. There are recently many strong research activities in this direction.

A major unsolved problem in the above context is the principled handling of uncertainty: In addition to natural uncertainty as an inherent part of Web data, one also has to deal with uncertainty resulting from automatically processing Web data. The former also includes uncertainty due to incompleteness and inconsistency in the case of missing and over-specified information, respectively. The latter includes uncertainty due to, e.g., the automatic annotation of Web pages and their contents, the automatic extraction of knowledge from the Web, matching between different related ontologies, and the integration of distributed Web data sources.

The central goal of the proposed research is to develop a family of probabilistic data models for knowledge bases extracted from the Web relative to an underlying ontology, along with scalable query answering algorithms, which may serve as the backbone for next-generation technologies for semantic search and query answering on the Web. We believe that such probabilistic data models and query answering algorithms can be developed by integrating ontology languages, database technologies, and formalisms for managing probabilistic uncertainty in the context of the Web. The objectives include developing probabilistic data models, developing algorithms for ranking and query answering, identifying useful scalable fragments, and practically evaluating our results.
Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.ox.ac.uk