Squiggle Semantic Search System

A Squiggle Semantic Search System is a Semantic Search System that is designed to provide both syntactic and semantic indexing and searching primitive.

• AKA: Expert Knowledge Ontology-based Semantic Search, Engineering or Environmental Knowledge Ontology-based Semantic Search.
• Context:
  • It was developed by Celino et al. (2006).
• Example(s):
  • Squiggle Ski,
  • Squiggle Med.
  • …
• Counter-Example(s):
  • BioSearch (Hu et al., 2017)
  • SemEHR (Wu et al.,2018),
  • SemSearch (Lei et al., 2006);
  • EKOSS System (Kraines et al., 2006).
  • Open Semantic Search.
• See: Squiggle SVG Browser, Semantic Web, Ontology Search System, Natural Language Processing, Search System, Semantic Search Engine, Swoogle System, SEAL System.

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References

2006

• (Celino et al., 2006) ⇒ Irene Celino, Emanuele Della Valle, Dario Cerizza, and Andrea Turati. (2006). “Squiggle: A Semantic Search Engine for Indexing and Retrieval of Multimedia Content.” In: Proceedings of the 1st International Conference on Semantic-Enhanced Multimedia Presentation Systems - Volume 228.
  • QUOTE: In essence, Squiggle is not a search engine itself, but it allows users to customize their own engine on the basis of a particular domain knowledge, as will be explained in section 5.

    Squiggle is designed to provide both syntactic and semantic indexing and searching primitives, seamlessly combining the speed of syntactic search tools with improved recall and precision, based on the ability to assign alternative designations and wordings in multiple languages to their meaning. Among the constituents of Squiggle, Sesame 12 is used to store and query semantic information constituting the knowledge base, described in RDF/OWL with regard to the SKOS model, whereas the syntactic search engine Lucene 13 is used, among other things, to quickly perform text searches in literals, which is something that semantic search tools typically cannot do well. Therefore the described architecture lends itself well both to overcome the limitations of purely syntactic approaches and to improve the performance of semantic engines.

    The main components of Squiggle are sketched in figure 1. In the following, we provide a brief description of Squiggle’s Conceptual Indexing (§4.1) and Semantic Search (§4.2) capabilities. Moreover, in §4.3, we illustrate how to add plug-in extensions to customize Squiggle and strengthen its potentialities.

    

    Fig. 1. The architecture of Squiggle framework

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