Ontologies provide a means to store knowledge in a machine-readable format. Ontology population is the task of updating an ontology with new facts from an input knowledge resource. These facts are represented in a structured format and integrated thereafter into the existing knowledge in the ontology. Textual resources are the dominant online knowledge resources that contain a large number of facts expressed either explicitly or implicitly. Hence, the automatic processing of the extensive knowledge available in these resources has recently gained increasing interest. This study discusses the major components of ontology population process and the different design aspects to be considered when building ontology population systems. In addition, this research explains the different approaches and techniques adopted to carry out the task of ontology population. The possible choices of the design aspects and the related issues are identified and analysed using a set of representative ontology population systems. This study concludes by describing the remaining open issues that should be further explored in ontology population.
MaedcheAStaabS.Ontology learning for the semantic web. IEEE Intell Syst2001; 16: 72–79.
2.
NoahSAAliDAAlhadiACet al. Going beyond the surrounding text to semantically annotate and search digital images. In: Proceedings of the second international conference on intelligent information and database, Hue City, Vietnam, 24–26 March 2010, pp. 169–179. Berlin: Springer.
3.
NoahSAAliDA. The role of lexical ontology in expanding the semantic textual content of on-line news images. In: Asia information retrieval symposium, Taipei, Taiwan, 1–3 December 2010, pp. 193–202. Berlin; Heidelberg: Springer.
4.
KhalidYIANoahSA. Ontology modelling of multimodality image retrieval system for sport news domain. In: International conference on knowledge engineering and ontology development (KEOD), Paris, 26–29 October 2011, pp. 400–406. Setúbal: SciTePress.
5.
HaghighiPDBursteinFZaslavskyAet al. Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decis Support Syst2013; 54: 1192–1204.
6.
AmailefKLuJ.Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services. Decis Support Syst2013; 55: 79–97.
7.
RavichandranDHovyE. Learning surface text patterns for a question answering system. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, Philadelphia, PA, 7–12 July 2002, pp. 41–47. Stroudsburg, PA: ACL.
WimalasuriyaDCDouD.Ontology-based information extraction: an introduction and a survey of current approaches. J Inform Sci2010; 36: 306–323.
10.
PetasisGKarkaletsisVPaliourasGet al. Ontology population and enrichment: state of the art. In: PaliourasGSpyropoulosCDTsatsaronisG (eds) Knowledge-driven multimedia information extraction and ontology evolution. Berlin: Springer, 2011, pp. 134–166.
11.
ShamsfardMBarforoushAA.The state of the art in ontology learning: a framework for comparison. Knowl Eng Rev2003; 18: 293–316.
12.
HearstMA. Automatic acquisition of hyponyms from large text corpora. In: International conference on computational linguistics, Nantes, 23–28 August 1992, pp. 539–545. Stroudsburg, PA: ACL.
13.
Finkelstein-landauMMorinE.Extracting semantic relationships between terms: supervised vs. unsupervised methods. In: International workshop on ontological engineering on the global information infrastructure, Dagstuhl Castle, Germany, 13 May 1999, pp. 71–80.
14.
YangarberRGrishmanR. NYU: description of the Proteus/PET system as used for MUC-7 ST. In: Message understanding conference, Fairfax, VA, 29 April–1 May 1998.
15.
IbrahimZNoahSANoorMM. Rules for ontology population from text of Malaysia medicinal herbs domain. In: YuJGrecoSLingrasPet al. (eds) Rough set and knowledge technology. Berlin; Heidelberg: Springer, 2010, pp. 386–394.
AlaniHKimSMillardDEet al. Automatic ontology-based knowledge extraction and tailored biography generation from the web. IEEE Intell Syst2002; 18: 14–21.
18.
MillerGA.WordNet: a lexical database for English. Commun ACM1995; 38: 39–41.
19.
MakkiJAlquierA-MPrinceV.Ontology population via NLP techniques in risk management. Int J Hum Soc Sci2009; 3: 212–217.
20.
FariaCSerraIGirardiR.A domain-independent process for automatic ontology population from text. Sci Comput Program2013; 95: 26–43.
21.
BuitelaarPCimianoPRacioppaSet al. Ontology-based information extraction with SOBA. In: International conference on language resources and evaluation, Genoa, 22–28 May 2006, pp. 2321–2324. Paris: European Language Resources Association (ELRA).
22.
DrozdzynskiWKriegerH-UPiskorskiJet al. Shallow processing with unification and typed feature structures – foundations and applications. Künstl Intell2004; 1: 17–23.
23.
WeberNBuitelaarP.Web-based ontology learning with ISOLDE. In: Workshop on web content mining with human language at the international semantic web conference, Athens, GA, 6 November 2006.
24.
BrewsterCCiravegnaFWilksY.User-centred ontology learning for knowledge management. In: AnderssonBBergholtzMJohannessonP (eds) Natural language processing and information systems. Berlin; Heidelberg: Springer, 2002, pp. 203–207.
25.
CiravegnaF.Adaptive information extraction from text by rule induction and generalisation. In: International joint conference on artificial intelligence, Seattle, WA, 4–10 August 2001, pp. 1251–1256. Burlington, MA: Morgan Kaufmann Publishers Inc.
26.
CeljuskaDVargas-VeraM. Ontosophie: a semi-automatic system for ontology population from text. In: International conference on natural language processing (ICON), Hyderabad, India, 19–22 December 2004.
27.
CravenMDiPasquoDFreitagDet al. Learning to construct knowledge bases from the World Wide Web. Artif Intell2000; 118: 69–113.
28.
QuinlanJRCameron-JonesRM.FOIL: a midterm report. In: European conference on machine learning, Vienna, 5–7 April 1993, pp. 1–20. Berlin; Heidelberg: Springer.
29.
EtzioniOCafarellaMDowneyDet al. Unsupervised named-entity extraction from the web: an experimental study. Artif Intell2005; 165: 91–134.
30.
BankoMCafarellaMJSoderlandSet al. Open information extraction from the web. In: International joint conference on artificial intelligence, Hyderabad, India, 6–12 January 2007, pp. 2670–2676. San Francisco, CA: Morgan Kaufmann Publishers.
31.
WuFWeldDS.Open information extraction using Wikipedia. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics, Uppsala, 11–16 July 2010.
32.
MausamSchmitzMBartRet al. Open language learning for information extraction. In: Proceedings of the joint conference on empirical methods in natural language processing and computational natural language learning, Jeju Island, Korea, 12–14 July 2012, pp. 523–534. Stroudsburg, PA: ACL.
33.
GuoDongZJianSJieZet al. Exploring various knowledge in relation extraction. In: Proceedings of the 43rd annual meeting of the Association for Computational Linguistics, Ann Arbor, MI, 25–30 June 2005, pp. 427–434. Stroudsburg, PA: ACL.
34.
ZengDLiuKLSiwei ZhouGet al. Relation classification via convolutional deep neural network. In: International conference on computational linguistics, Dublin, 23–29 August 2014, pp. 2335–2344. Dublin: Dublin City University and Association for Computational Linguistics.
35.
ChenYLiWLiuYet al. Exploring deep belief network for Chinese relation extraction. In: Proceedings of the CIPS-SIGHAN joint conference on Chinese language processing (CLP’10), Beijing, China, 28–29 August 2010.
36.
LiuCSunWChawWet al. Convolution neural network for relation extraction. In: International conference on advanced data mining and applications (ADMA), Hangzhou, China, 14–16 December 2013, pp. 231–242. Berlin; Heidelberg: Springer.
37.
MintzMBillsSSnowRet al. Distant supervision for relation extraction without labeled data. In: Proceedings of the 47th annual meeting of the ACL and the 4th IJCNLP of the AFNLP, Singapore, 2–7 August 2009, pp. 1003–1011. Stroudsburg, PA: ACL.
38.
SuchanekFMIfrimGWeikumG.LEILA: learning to extract information by linguistic analysis. In: Proceedings of the 2nd workshop on ontology learning and population: bridging the gap between text and knowledge, Sydney, NSW, Australia, 22 July 2006, pp. 18–25. Stroudsburg, PA: Association for Computational Linguistics.
TanevHMagniniB.Weakly supervised approaches for ontology population. In: BuitelaarPCimianoP (eds) Ontology learning and population: bridging the gap between text and knowledge. Amsterdam: IOS Press, 2008, pp. 129–143.
41.
MaynardDLiYPetersW.NLP techniques for term extraction and ontology population. In: BuitelaarPCimianoP (eds) Ontology learning and population: bridging the gap between text and knowledge. Amsterdam: IOS Press, 2008, pp. 107–127.
42.
YoonH-GHanYJParkS-Bet al. Ontology population from unstructured and semi-structured texts. In: International conference on advanced language processing and web information technology, Luoyang, China, 22–24 August 2007, pp. 135–139. New York: IEEE.
