HYKE LogoSemantic Search


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Introduction
State of the art
An overview of SemSearch
The Google-like query interface
Making sense of the user query
Translating the user query into formal queries
Implementation and experimental evaluation
Conclusions and future work
1st Workshop on Semantic Search
2nd Workshop on Semantic Search
3rd Workshop on Semantic Search
4th Workshop on Semantic Search
 

Conclusions and future work


The core observation that underlies this paper is that, in the case of semantic
search that promises to produce precise answers to user queries, it is important
to ensure that it is easy to use and effective for ordinary end users who are not
necessarily familiar with domain specific semantic data, ontologies, or SQL-like
query languages. Our survey of state-of-art semantic search tools however reveals
that current tools provide little support for end users.
In contrast with these efforts, our semantic search engine, SemSearch, pro-
vides several means to address this issue. First, SemSearch overcomes the prob-
lem of knowledge overhead by supporting a Google-like query interface. As de-
scribed in Section 4, the proposed query interface provides a simple but powerful
way of specifying queries. Second, SemSearch is able to produce precise answers
for user queries by providing comprehensive means to make sense of user queries
and to translate them into formal queries. In particular, as described in Section
6, the produced answers on the one hand satisfy user queries and on the other
hand are self-explanatory and understandable by end users. Finally, SemSearch
provides means (i.e., search refinement forms) to support end users in refor-
mulating better queries. A prototype has been implemented. The experimental
study indicates a encouraging results.
Future work will focus on i) carrying out a more thorough evaluation which
will help us to investigate the problems of each main component of the search en-
gine and to improve their performance accordingly; ii) developing comprehensive
means to perform semantic matching between keywords and semantic entities
without compromising the performance of the search engine in terms of time;
iii) developing more fine grained rules which on the one hand will help us to sig-
nificantly reduce the number of keywords combinations and on the other hand
will help us to identify and keep useful information in the reduction process; and
iv) developing a powerful ranking mechanism, which guarantees the best results
always staying on the top.