hide
Free keywords:
-
Abstract:
The envisioned Semantic Web aims to provide richly annotated and
explicitly structured Web pages in XML, RDF, or description logics, based upon
underlying ontologies and thesauri. Ideally, this should enable a wealth of
query processing and semantic reasoning capabilities using XQuery and logical
inference
engines. However, we believe that the diversity and uncertainty of
terminologies and schema-like annotations will make precise querying on a Web
scale extremely elusive if not hopeless, and the same argument holds for
large-scale
dynamic federations of Deep Web sources. Therefore, ontology-based reasoning
and querying needs to be enhanced by statistical means, leading to
relevanceranked lists as query results.
This paper presents steps towards such a "statistically semantic" Web and
outlines technical challenges. We discuss how statistically quantified
ontological relations can be exploited in XML retrieval, how statistics can
help in making Web-scale
search efficient, and how statistical information extracted from users’ query
logs and click streams can be leveraged for better search result ranking. We
believe these are decisive issues for improving the quality of next-generation
search engines
for intranets, digital libraries, and the Web, and they are crucial also for
peer-to-peer collaborative Web search.
