Structuring Linked Data Search Results Using Probabilistic Soft Logic: ISWC 2016: The Semantic Web – ISWC 2016

On-the-y generation of integrated representations of Linked
Data (LD) search results is challenging because it requires successfully
automating a number of complex subtasks, such as structure inference
and matching of both instances and concepts, each of which gives rise to
uncertain outcomes. Such uncertainty is unavoidable given the semantically
heterogeneous nature of web sources, including LD ones. This paper
approaches the problem of structuring LD search results as an evidencebased
one. In particular, the paper shows how one formalism (viz., probabilistic
soft logic (PSL)) can be exploited to assimilate different sources
of evidence in a principled way and to beneficial effect for users. The
paper considers syntactic evidence derived from matching algorithms,
semantic evidence derived from LD vocabularies, and user evidence, in
the form of feedback. The main contributions are: sets of PSL rules that
model the uniform assimilation of diverse kinds of evidence, an empirical
evaluation of how the resulting PSL programs perform in terms of their
ability to infer structure for integrating LD search results, and, finally,
a concrete example of how populating such inferred structures for presentation
to the end user is benefcial, besides enabling the collection of
feedback whose assimilation further improves search result presentation.