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Fig. 6.7 Feedback-driven
semantic search
Representation is the internal data model used to represent the documents, either
vector-space models or language models. The feedback used to expand the query is
given by Feedback with the kind of relevance feedback algorithm used to expand
the query given by Algorithm , which for relevance models are directly built into the
representation. The ranking function (cross-entropy for language models, or some
variation of cosine for vector-space models) is given by Ranking . The final results
for each query are presented to the user in PresentResults .
We can compare both Semantic Web data and hypertext documents by consider-
ing both to be 'bags of words' and using relevance modelling techniques to expand
the queries (Lavrenko and Croft 2001). We consider both to be 'bags of words.'
Semantic Web data can be flattened, and URIs can be reduced to 'words' by the
following steps:
Reduce to the rightmost hierarchical component.
If the rightmost component contains a fragment identifier (#), consider all
characters right of the fragment identifier the rightmost hierarchical component.
Tokenize the rightmost component on space, capitalization, and underscore.
So, http://www.example.org/hasArchitect would be reduced to two tokens, 'has'
and 'architect.' Using this system, we evaluated both the vector-space and language
models described in Sect. 6.3 on queries selected in Sect. 6.2.2 with relevance
judgments on these queries selected in Sect. 6.2.3 .
6.5
Feedback Evaluation
In this section we evaluate algorithms and parameters using relevance feedback
against the same system without relevance feedback. In Sect. 6.8 we evaluate against
deployed systems such as FALCON-S and Yahoo! Web Search. To preview our final
results in Sect. 6.8 , relevance feedback from the Semantic Web shows an impressive
25% gain in average precision over Yahoo! Web Search with a 16% gain in precision
over FALCON-S without relevance feedback.
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