SemSearch

A SemSearch is a Semantic Search System developed by Lei et al. (2006) that uses a Google-like query interface.

• Context:
  • It can be designed to solve the problem of knowledge overhead.
  • It is composed by the following system-layers:
    • Query Interface Layer;
    • Text Search Layer ;
    • Query Construction Layer;
    • Querying Layer;
    • Semantic Data Layer.
• Example(s):
  • …
• Counter-Example(s):
  • BioSearch (Hu et al., 2017)
  • SemEHR (Wu et al.,2018),
  • Squiggle (Celino et al., 2006)
  • Open Semantic Search,
  • EKOSS System.
• See: Semantic Web, Ontology Search System, Natural Language Processing, Search System.

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References

2009a

• (KMi Open, 2019) ⇒ http://kmi.open.ac.uk/technologies/name/semsearch Retrieved: 2019-05-05.
  • QUOTE: SemSearch is a semantic search engine, which is designed for naïve users, i.e., ordinary end users who are not necessarily familiar with domain specific semantic data, ontologies, or SQL-like query Languages. It hides the complexity of semantic search from end users by supporting a Google-like query interface and by providing comprehensive means to translate user queries into formal queries.

2019b

• (KMi Open, 2019) ⇒ http://technologies.kmi.open.ac.uk/semsearch/ Retrieved: 2019-05-05.
  • QUOTE: SemSearch is a keyword-based semantic search technology, which aims to wrap up the complexity of specifying semantic queries and make it suitable for ordinary users who are not necessarily familiar with the problem domain or with the specified query language. The following figure shows its five-layered architecture. P
    
    • The Query Interface Layer offers a straightforward way of specifying queries using multiple keywords.
    • The Text Search Layer exploits state-of-art text search technologies to locate the semantics of keywords in the given domain.
    • The Query Construction Layer computes the most relevant formal queries from informal queries issued by end users that are often ambiguous.
    • The Querying Layer executes the derived formal queries and ranks the search results.
    • The Semantic Data Layer sets data environments that the search engine works. This layer is made up of ontologies, semantic metadata repositories and the associated web resources.

2008

• (Uren et al., 2008) ⇒ Victoria Uren, Yuangui Lei, and Enrico Motta. (2008). “Semsearch: Refining Semantic Search.” In: Proceedings of European Semantic Web Conference. . ISBN:978-3-540-68233-2 doi:10.1007/978-3-540-68234-9_76
  • QUOTE: SemSearch is a search engine for RDF knowledge bases . The driving factor in its design is to make the formulation of semantic queries straightforward for users who may not know the details of the ontology underlying the knowledge base. To achieve this, it has a query translation engine which takes keyword input and translates it into formal semantic queries.

2006

• (Lei et al., 2006) ⇒ Yuangui Lei, Victoria Uren, and Enrico Motta. (2006). “SemSearch: A Search Engine for the Semantic Web/.” In: Proceedings of the 15th International Conference on Knowledge Engineering and Knowledge Management Managing Knowledge in a World of Networks (EKAW 2006). doi:10.1007/11891451_22
  • QUOTE: The semantic search engine we present here, SemSearch, provides several means to address this issue.
    • SemSearch tackles the problem of knowledge overhead by supporting a Google-like query interface. As will be described in Section 4, the proposed query interface provides a simple but powerful way of specifying queries.
    • SemSearch addresses the problem of existing semantic-based keyword search engines by supporting complex queries. It provides comprehensive means to make sense of user queries and to translate them into formal queries.
    • SemSearch takes the focus of user queries into consideration when generating formal queries, thus being able to produce precise results that on the one hand satisfy user queries and on the other hand are self-explanatory and understandable by end users.

Thus, SemSearch makes it possible for ordinary end users to harvest the benefits of semantic search and other semantic web technologies without having to know the underlying semantic data or to learn a SQL-like query language. A prototype of the search engine has been implemented and applied in the semantic web portal of our lab . An initial evaluation shows promising results.

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