Abstract
Semantic Web data seems like a promising source of information for improving search. While there is some literature about how semantic data should be used to enhance search, there are no positive conclusions about the best approach. This paper surveys existing approaches to semantic web search, describes adapting a TREC benchmark for evaluation, and proposes a learned representation algorithm for using semantic web data in search.
All research is funded by Los Alamos National Labs.
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Keywords
- Loss Function
- Mean Average Precision
- Vector Space Model
- Citation Relationship
- Learn Representation Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Gronski, J. (2009). Semantic Web for Search. In: Bernstein, A., et al. The Semantic Web - ISWC 2009. ISWC 2009. Lecture Notes in Computer Science, vol 5823. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04930-9_61
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DOI: https://doi.org/10.1007/978-3-642-04930-9_61
Publisher Name: Springer, Berlin, Heidelberg
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