Semantic Search System

A Semantic Search System is a Search System that based on the semantic analysis of the search query.

• Context:
  • It can solve a Semantic Search Task by implementing Semantic Search Algorithms.
• Example(s):
  • SemEHR (Wu et al.,2018),
  • BioSearch (Hu et al., 2017),
  • a Semantic Web Search System such as:
    • SemSearch (Lei et al., 2006);
    • Squiggle (Celino et al., 2006)
    • Open Semantic Search;
  • a Semantic Ontology Search System such as:
    • EKOSS System (Kraines et al., 2006).
  • …
• Counter-Example(s):
  • a Keyword Search System,
  • a Lexical Search System,
  • a Concept Search System,
• See: Semantic Web, Ontology Search System, Natural Language Processing,----

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References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Semantic_search Retrieved:2019-4-21.
  • Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.

2018

• (Wu et al., 2018) ⇒ Honghan Wu, Giulia Toti, Katherine I Morley, Zina M Ibrahim, Amos Folarin, Richard Jackson, Ismail Kartoglu, Asha Agrawal, Clive Stringer, Darren Gale, Genevieve Gorrell, Angus Roberts, Matthew Broadbent, Robert Stewart, and Richard JB Dobson. (2018). “SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment, and Clinical Research.” In: Journal of the American Medical Informatics Association, 25(5).

2017a

• (Hu et al., 2017) ⇒ Wei Hu, Honglei Qiu, Jiacheng Huang, and Michel Dumontier. (2017). “BioSearch: A Semantic Search Engine for Bio2RDF.” In: Database (Oxford) Journal, 2017. doi:10.1093/database/bax059

2017b

• (Kaewboonma et al., 2017) ⇒ Nattapong Kaewboonma, Jirapong Panawong, Ekkawit Pianhanuruk, and Marut Buranarach. (2017). “Development of Intelligent Semantic Search System for Rubber Research Data in Thailand.” In: Proceedings of The 2nd International Conference on Applied Science and Technology 2017 (ICAST €™17). doi:10.1063/1.5005406

2016

• (Hua et al., 2016) ⇒ Yu Hua, Hong Jiang, and Dan Feng. (2016). “Real-Time Semantic Search Using Approximate Methodology for Large-Scale Storage Systems.” In: IEEE Transactions on Parallel and Distributed Systems Journal, 27(4). doi:10.1109/TPDS.2015.2425399

2015

• (Jiang et al., 2015) ⇒ Lu Jiang, Shoou-I Yu, Deyu Meng, Teruko Mitamura, and Alexander G. Hauptmann. (2015). “Bridging the Ultimate Semantic Gap: A Semantic Search Engine for Internet Videos.” In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. ISBN:978-1-4503-3274-3 doi:10.1145/2671188.2749399

2014

• (Fatima et al., 2014) ⇒ Arooj Fatima, Cristina Luca, and George Wilson. (2014). “User Experience and Efficiency for Semantic Search Engine.” In: Proceedings of 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). doi:https://doi.org/10.1109/OPTIM.2014.6851023

2011a

• (Umamaheswari et al., 2011) ⇒ E. Umamaheswari, T. V. Geetha, Ranjani Parthasarathi, and Madhan Karky. (2011). "A Multilevel UNL Concept based Searching and Ranking". In WEBIST (pp. 282-289).
  • QUOTE: Concept based search can also be based on the use of knowledge structures. One such search engine is Engineering or Environmental Knowledge Ontology-based Semantic Search(EKOSS) (Kraines et al., 2006). It is an ontology based semantic search engine which uses a fully functional ontology for representing the knowledge base. It provides a collaborative knowledge sharing environment and helps knowledge experts to share their knowledge such as research papers, database, computer simulated model and even curriculum vitae. The EKOSS system is used to construct computer-interpretable semantically rich statements of the knowledge resource. When a user request is posted, this system converts the user request into a computer readable knowledge description based on description logic and associated rules.

2011b

• (Mukhopadhyay et al., 2011) ⇒ Debajyoti Mukhopadhyay, Aritra Banik, Sreemoyee Mukherjee, Jhilik Bhattacharya, and Young-Chon Kim. (2011). “A Domain Specific Ontology Based Semantic Web Search Engine.” In: Proceedings of 7th International Workshop on MSPT (MSPT 2007).
  • QUOTE: A search engine is a document retrieval system designed to help find information stored in a computer system, such as on the World Wide Web, inside a corporate or proprietary network, or in a personal computer.

    The search engine allows one to ask for content meeting specific criteria (typically those containing a given word or phrase) and retrieves a list of items that match those criteria.

2007

• (Tran et al., 2007) ⇒ Thanh Tran, Philipp Cimiano, Sebastian Rudolph, and Rudi Studer. (2007). “Ontology-based Interpretation of Keywords for Semantic Search.” In: Proceedings of the 6th International Semantic Web Conference and the 2nd Asian Semantic Web Conference (ISWC 2007 + ASWC 2007). doi:10.1007/978-3-540-76298-0_38
  • … Ontology-based approaches allow for sophisticated semantic search but impose a query syntax more difficult to handle.

2006a

• (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
  • … This paper presents SemSearch, a search engine, which pays special attention to this issue by providing several means to hide the complexity of semantic search from end users and thus make it easy to use and effective.

2006b

• (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.

2003

• (Guha et al., 2003) ⇒ Ramanathan V. Guha, Rob McCool, and Eric Miller. (2003). “Semantic Search.” In: Proceedings of the 12th International Conference on World Wide Web.
  • QUOTE: The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. It is the idea of having data on the Web defined and linked in a way that it can be used for more effective discovery, automation, integration, and reuse across various applications. In particular, the Semantic Web will contain resources corresponding not just to media objects (such as Web pages, images, audio clips, etc.) as the current Web does, but also to objects such as people, places, organizations and events. Further, the Semantic Web will contain not just a single kind of relation (the hyperlink) between resources, but many different kinds of relations between the different types of resources mentioned above.

    The general ideas presented in this paper assume that the Semantic Web will contain resources with relations amoung each other. More concretely, we assume that the data on the Semantic Web is modeled as a directed labeled graph, wherein each node corresponds to a resource and each arc is labeled with a property type (also a resource). While there are several different XML based proposals for representing resources and their inter-relations on the Semantic Web, a particular system needs to make a commitment to one or more interchange formats and protocols in order to exchange this information. The system described in this paper uses the W3C's Resource Description Framework with the schema vocabulary provided by RDFS as a means for describing resources and their inter-relations. SOAP is used as the protocol for querying and exchanging this RDF instance data between machines.

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