Skip to main content
SpringerLink
Log in

Semantic Search

  • Reference work entry
  • First Online:
Encyclopedia of Big Data Technologies

  • 73 Accesses

Definitions

Semantic Search regroups a set of techniques designed to improve traditional document or knowledge base search. Semantic Search aims at better grasping the context and the semantics of the user query and/or of the indexed content by leveraging natural language processing, Semantic Web, and machine learning techniques to retrieve more relevant results from a search engine.

Overview

Semantic Search is an umbrella term regrouping various techniques for retrieving more relevant content from a search engine. Traditional search techniques focus on ranking documents based on a set of keywords appearing both in the user’s query and in the indexed content. Semantic Search, instead, attempts to better grasp the semantics (i.e., meaning) and the context of the user query and/or of the indexed content in order to retrieve more meaningful results.

Semantic Search techniques can be broadly categorized into two main groups depending on the target content:

  • techniques improving the...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 849.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 999.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Bast H, Buchhold B, Haussmann E (2016) Semantic search on text and knowledge bases. Found Trends Inform Retr 10(2–3):119–271. http://dx.doi.org/10.1561/1500000032

    Article  Google Scholar 

  • Bizer C, Lehmann J, Kobilarov G, Auer S, Becker C, Cyganiak R, Hellmann S (2009) Dbpedia – a crystallization point for the web of data. J Web Sem 7(3):154–165. https://doi.org/10.1016/j.websem.2009.07.002

    Article  Google Scholar 

  • d’Aquin M, Motta E (2011) Watson, more than a semantic web search engine. Semant Web 2(1):55–63. http://dl.acm.org/citation.cfm?id=2019470.2019476

    Google Scholar 

  • Ding L, Finin T, Joshi A, Pan R, Cost RS, Peng Y, Reddivari P, Doshi V, Sachs J (2004) Swoogle: a search and metadata engine for the semantic web. In: Proceedings of the thirteenth ACM international conference on information and knowledge management, CIKM’04. ACM, New York, pp 652–659. http://doi.acm.org/10.1145/1031171.1031289

    Chapter  Google Scholar 

  • Grbovic M, Djuric N, Radosavljevic V, Silvestri F, Bhamidipati N (2015) Context- and content-aware embeddings for query rewriting in sponsored search. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, SIGIR’15. ACM, New York, pp 383–392. http://doi.acm.org/10.1145/2766462.2767709

    Google Scholar 

  • Guha R, McCool R, Miller E (2003) Semantic search. In: Proceedings of the 12th international conference on world wide web, WWW’03. ACM, New York, pp 700–709. http://doi.acm.org/10.1145/775152.775250

    Google Scholar 

  • Hasibi F, Balog K, Garigliotti D, Zhang S (2017) Nordlys: a toolkit for entity-oriented and semantic search. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, SIGIR’17. ACM, New York, pp 1289–1292. http://doi.acm.org/10.1145/3077136.3084149

    Chapter  Google Scholar 

  • Heflin J, Hendler J (2000) Searching the web with shoe. In: AAAI workshop on artificial intelligence for web search, pp 35–40

    Book  Google Scholar 

  • Hogan A, Harth A, Umbrich J, Kinsella S, Polleres A, Decker S (2011) Searching and browsing linked data with swse: the semantic web search engine. Web Sem Sci Serv Agents World Wide Web 9(4):365–401. http://www.sciencedirect.com/science/article/pii/S1570826811000473. jWS special issue on Semantic Search

    Article  Google Scholar 

  • Horrocks I, Tessaris S (2002) Querying the semantic web: a formal approach. In: Horrocks I, Hendler J (eds) The semantic web—ISWC 2002. Springer, Berlin/Heidelberg, pp 177–191

    Chapter  MATH  Google Scholar 

  • Hua W, Wang Z, Wang H, Zheng K, Zhou X (2015) Short text understanding through lexical-semantic analysis. In: 2015 IEEE 31st international conference on data engineering, pp 495–506. https://doi.org/10.1109/ICDE.2015.7113309

  • Kaptein R, Serdyukov P, de Vries AP, Kamps J (2010) Entity ranking using wikipedia as a pivot. In: Proceedings of the 19th ACM conference on information and knowledge management, CIKM 2010, Toronto, 26–30 Oct, pp 69–78. http://doi.acm.org/10.1145/1871437.1871451

  • Lei Y, Uren VS, Motta E (2006) Semsearch: a search engine for the semantic web. In: Proceedings of the 15th international conference on managing knowledge in a world of networks, EKAW 2006, Podebrady, 2–6 Oct 2006, pp 238–245. https://doi.org/10.1007/11891 451_22

    Google Scholar 

  • Lund K, Burgess C (1996) Producing high-dimensional semantic spaces from lexical co-occurrence. Behav Res Methods Instrum Comput 28(2):203–208. https://doi.org/10.3758/BF03204766

    Article  Google Scholar 

  • Madhu G, Govardhan A, Rajinikanth TV (2011) Intelligent semantic web search engines: a brief survey. CoRR abs/1102.0831. http://arxiv.org/abs/1102.0831, 1102.0831

    Article  Google Scholar 

  • Maedche A, Motik B, Stojanovic L, Studer R, Volz R (2003) An infrastructure for searching, reusing and evolving distributed ontologies. In: Proceedings of the 12th international conference on world wide web, WWW’03. ACM, New York, pp 439–448. http://doi.acm.org/10.1145/775152.775215

    Google Scholar 

  • Mäkelä E (2005) Survey of semantic search research. https://seco.cs.aalto.fi/publications/2005/makela-semantic-search-2005.pdf

  • Mangold C (2007) A survey and classification of semantic search approaches. Int J Metadata Semant Ontologies 2(1):23–34. https://doi.org/10.1504/IJMSO.2007.015073

    Article  Google Scholar 

  • Manning CD (2011) Part-of-speech tagging from 97% to 100%: Is it time for some linguistics? In: Gelbukh AF (ed) Computational linguistics and intelligent text processing. Springer, Berlin/Heidelberg, pp 171–189

    Chapter  Google Scholar 

  • Mika P (2015) On schema.org and why it matters for the web. IEEE Int Comput 19(4):52–55. https://doi.org/10.1109/MIC.2015.81

    Article  Google Scholar 

  • Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. CoRR abs/1301.3781. http://arxiv.org/abs/1301.3781, 1301.3781

  • Nalisnick E, Mitra B, Craswell N, Caruana R (2016) Improving document ranking with dual word embeddings. In: Proceedings of the 25th international conference companion on world wide web, International world wide web conferences steering committee, WWW’16 Companion. Republic and Canton of Geneva, Switzerland, pp 83–84. https://doi.org/10.1145/2872518.2889361

    Chapter  Google Scholar 

  • Oren E, Delbru R, Catasta M, Cyganiak R, Stenzhorn H, Tummarello G (2008) Sindice.com: a document-oriented lookup index for open linked data. IJMSO 3(1):37–52. https://doi.org/10.1504/IJMSO.2008.021204

    Article  Google Scholar 

  • Pehcevski J, Vercoustre AM, Thom JA (2008) Exploiting locality of wikipedia links in entity ranking. In: Macdonald C, Ounis I, Plachouras V, Ruthven I, White RW (eds) Advances in information retrieval. Springer, Berlin/Heidelberg, pp 258–269

    Chapter  Google Scholar 

  • Pound J, Mika P, Zaragoza H (2010) Ad-hoc object retrieval in the web of data. In: Proceedings of the 19th international conference on world wide web, WWW’10. ACM, New York, pp 771–780. http://doi.acm.org/10.1145/1772690.1772769

    Chapter  Google Scholar 

  • Prokofyev R, Tonon A, Luggen M, Vouilloz L, Difallah DE, Cudré-Mauroux P (2015) Sanaphor: ontology-based coreference resolution. In: Proceedings of the 14th international conference on the semantic web, ISWC 2015, vol 9366. Springer, Berlin/Heidelberg, pp 458–473. https://doi.org/10.1007/978-3-319-25007-6_27

    Chapter  Google Scholar 

  • Rebele T, Suchanek FM, Hoffart J, Biega J, Kuzey E, Weikum G (2016) YAGO: a multilingual knowledge base from wikipedia, wordnet, and geonames. In: Proceedings Part II 15th international semantic web conference of the semantic web, ISWC 2016, Kobe, 17–21 Oct, pp 177–185. https://doi.org/10.1007/978-3-319-46547-0_19

    Google Scholar 

  • Ristoski P, Paulheim H (2016) Rdf2vec: Rdf graph embeddings for data mining. In: Groth P, Simperl E, Gray A, Sabou M, Krötzsch M, Lecue F, Flöck F, Gil Y (eds) The semantic web – ISWC 2016. Springer International Publishing, Cham, pp 498–514

    Chapter  Google Scholar 

  • Rocha C, Schwabe D, Aragao MP (2004) A hybrid approach for searching in the semantic web. In: Proceedings of the 13th international conference on world wide web, WWW’04. ACM, New York, pp 374–383. http://doi.acm.org/10.1145/988672.988723

    Chapter  Google Scholar 

  • Schuhmacher M, Ponzetto SP (2013) Exploiting dbpedia for web search results clustering. In: Proceedings of the 2013 workshop on automated knowledge base construction, AKBC’13. ACM, New York, pp 91–96. http://doi.acm.org/10.1145/2509558.2509574

    Chapter  Google Scholar 

  • Schuhmacher M, Dietz L, Paolo Ponzetto S (2015) Ranking entities for web queries through text and knowledge. In: Proceedings of the 24th ACM international on conference on information and knowledge management, CIKM’15. ACM, New York, pp 1461–1470. http://doi.acm.org/10.1145/2806416.2806480

    Google Scholar 

  • Sheth A, Bertram C, Avant D, Hammond B, Kochut K, Warke Y (2002) Managing semantic content for the web. IEEE Int Comput 6(4):80–87. https://doi.org/10.1109/MIC.2002.1020330

    Article  Google Scholar 

  • Siencnik SK (2015) Adapting word2vec to named entity recognition. In: Proceedings of the 20th Nordic conference of computational linguistics, NODALIDA 2015, 11–13 May. Institute of the Lithuanian Language, Vilnius, pp 239–243. http://aclweb.org/anthology/W/W15/W15-1830.pdf

  • Stojanovic N, Studer R, Stojanovic L (2003) An approach for the ranking of query results in the semantic web. In: Fensel D, Sycara K, Mylopoulos J (eds) The semantic web – ISWC 2003. Springer, Berlin/Heidelberg, pp 500–516

    Chapter  Google Scholar 

  • Tonon A, Demartini G, Cudré-Mauroux P (2012) Combining inverted indices and structured search for ad-hoc object retrieval. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, SIGIR’12. ACM, New York, pp 125–134. http://doi.acm.org/10.1145/2348283.2348304

    Google Scholar 

  • Tran T, Cimiano P, Rudolph S, Studer R (2007) Ontology-based interpretation of keywords for semantic search. In: Proceedings of the 6th international the semantic web and 2nd asian conference on asian semantic web conference, ISWC’07/ASWC’07. Springer, Berlin/Heidelberg, pp 523–536. http://dl.acm.org/citation.cfm?id=1785162.1785201

    Google Scholar 

  • Voorhees EM (1993) Using wordnet to disambiguate word senses for text retrieval. In: Proceedings of the 16th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR’93. ACM, New York, pp 171–180. http://doi.acm.org/10.1145/160688.160715

    Google Scholar 

  • Vrandecic D, Krötzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78–85. http://doi.acm.org/10.1145/2629489

    Article  Google Scholar 

  • Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724–2743. https://doi.org/10.1109/TKDE.2017.2754499

    Article  Google Scholar 

  • Zhang L, Yu Y, Zhou J, Lin C, Yang Y (2005) An enhanced model for searching in semantic portals. In: Proceedings of the 14th international conference on world wide web, WWW’05. ACM, New York, pp 453–462. http://doi.acm.org/10.1145/1060745.1060812

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. eXascale Infolab, University of Fribourg, Fribourg, Switzerland

    Philippe Cudre-Mauroux

Authors
  1. Philippe Cudre-Mauroux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philippe Cudre-Mauroux .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Cudre-Mauroux, P. (2019). Semantic Search. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_231

Download citation

Publish with us

Policies and ethics

Search

Navigation