Personal and Ubiquitous Computing

, Volume 17, Issue 8, pp 1721–1729 | Cite as

Semi-automatic construction of domain ontology for agent reasoning

Original Article


One of the key elements of the Semantic Web technologies is domain ontologies and those ontologies are important constructs for multi-agent system. The Semantic Web relies on domain ontologies that structure underlying data enabling comprehensive and transportable machine understanding. It takes so much time and efforts to construct domain ontologies because these ontologies can be manually made by domain experts and knowledge engineers. To solve these problems, there have been many researches to semi-automatically construct ontologies. Most of the researches focused on relation extraction part but manually selected terms for ontologies. These researches have some problems. In this paper, we propose a hybrid method to extract relations from domain documents which combines a named relation approach and an unnamed relation approach. Our named relation approach is based on the Hearst’s pattern and the Snowball system. We merge a generalized pattern scheme into their methods. In our unnamed relation approach, we extract unnamed relations using association rules and clustering method. Moreover, we recommend candidate relation names of unnamed relations. We evaluate our proposed method by using Ziff document set offered by TREC.


Association Rule Relation Approach Association Rule Miner Belief Base Concept Pair 


  1. 1.
    Berners-Lee T, Hendler J, Lassila O (2001) The Semantic Web. Sci Am 284(5):34–43Google Scholar
  2. 2.
    Maedche A, Pekar V, Staab S (2002) Ontology learning part one—on discovering taxonomic relations from the web. In: Web Intelligence, Springer, BerlinGoogle Scholar
  3. 3.
    Byrd RJ, Ravin Y (1999) Identifying and extracting relations from text. In: Proceedings of the 4th international conference on applications of natural language to information systemsGoogle Scholar
  4. 4.
    Hearst MA (1992) Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th international conference on computational LinguisticsGoogle Scholar
  5. 5.
    Agichtein E, Gravano L (2000) Snowball: extracting relations from large plain-text collections. In: Proceedings of the ACM international conference on digital libraries (DL’00)Google Scholar
  6. 6.
    Frakes WB, Baeza-Yates R (eds) (1992) Information retrieval: data structures and algorithms. Prentice-Hall, Englewood CliffsGoogle Scholar
  7. 7.
    Kim H, Choi I, Kim M (2004) Refining term weights of documents using term dependencies. In: Proceedings of the 26th international ACM SIGIR conference on research and development in information retrieval, pp 552–553Google Scholar
  8. 8.
    Lawrie DJ, Croft WB (2003) Generating hierarchical summaries for web searches. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, pp 457–458Google Scholar
  9. 9.
    Sanderson M, Croft B (1999) Deriving concept hierarchies from text. In: Proceedings of the 22th annual international ACM SIGIR conference on research and development in information retrieval, pp 206–213Google Scholar
  10. 10.
    Lawrie D, Croft WB, Rosenberg A (2001) Finding topic words for hierarchical summarization. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR 2001), pp 349–357Google Scholar
  11. 11.
    Maedche A, Staab S (2000) Semi-automatic engineering of ontologies from text. In: Proceedings of the 12th international conference on Sw engineering and knowledge engineering (SEKE’2000)Google Scholar
  12. 12.
    Moreira AF, Vieira R, Bordini RH, Hübner JF (2006) Agent-oriented programming with underlying ontological reasoning. In: Proceedings of 3rd international workshop on declarative agent languages and technologies (DALT-05), pp 155–170, vol 3904, SpringerGoogle Scholar
  13. 13.
    Fuzitaki CN, Moreira ÁF, Vieira R (2010) Ontology reasoning in agent-oriented programming. In: 20th SBIA—Brazilian symposium on artificial intelligence. Lecture notes in computer science, vol 6404, pp 21–30Google Scholar
  14. 14.
    Lee C, Park S, Lee D, Lee J, Jeong OR, Lee S (2008) A comparison of ontology reasoning systems using query sequences. In: The 2nd international conference on ubiquitous information management and communication, pp 543–546Google Scholar
  15. 15.
    Cui H, Kan M-Y, Chua T-S (2004) Unsupervised learning of soft patterns for generating definition. In: Proceedings of 13th international world wide web conferenceGoogle Scholar
  16. 16.
    Brill E (1995) Transformation-based error-driven learning and natural language: a case study in part-of-speech tagging. Comput Linguist 21:543–565Google Scholar
  17. 17.
    Frantzi KT, Ananiadou S, Tsujii J (1998) The C-value/NC-value method of automatic recognition for multi-word terms. Research and advanced technology for digital libraries: second european conference, ECDL’98Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Graduate School of Information and CommunicationAjou UniversitySuwonRepublic of Korea
  2. 2.Division of Information and CommunicationBaekseok UniversityCheonanRepublic of Korea
  3. 3.College of Information and Computer EngineeringAjou UniversitySuwonRepublic of Korea

Personalised recommendations