Towards a Semantic Web data publishing and search tool
Anila Sahar Butt (ANU)
ARTIFICIAL INTELLIGENCE SEMINAR PhD monitoringDATE: 2013-10-02
TIME: 12:00:00 - 12:30:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU
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ABSTRACT:
The amount of automatically generated machine-readable data on the Web has significantly increased in recent years. This is in part due to the advent of Linked Data and its publishing tools that allowed the mapping of relational data to RDF. However, the amount of semantic Web data is still many orders of magnitude smaller than the World-Wide-Web, and this limits semantic Web applications. One of the barriers for semantic Web novices to create machine-readable data is the lack of easy-to-use Web publishing tools that separate the schema modeling from the data creation.
ActiveRaUL is a Web service that operates on an RDF template RaUL ontology model that describes the structure and data model of a Web form. It implements a read-write Linked Data architecture that manages the mapping of a Web form record to an RDF graph, its field names to RDF property types and the user input value to instances of RDF properties. We have further extended this Web service to automatically generate Web forms from any arbitrary input ontology. To do that we map the graph-structured input ontology to a tree-structured Web form while still allowing the user to create RDF data typed according to the input ontology.
The Web form based interface in ActiveRaUL also provides the facility to link newly generated data with existing data or update existing semantic web data through a lookup query for the exiting instances. However, the selection of an appropriate instance becomes problematic for the user due to the large amount of available semantic data. Now our focus is to providing meaningful and actionable information by ranking Semantic Web data according to its relevance/importance to a user query. This in turn will lead to better information representations of the Semantic Web. We are looking to exploit the inherent nature of Semantic Web data and/or machine learning techniques e.g. learning to rank to bridge the gap between automatically computed ranks and hand-crafted ranking on semantic Web data.
