Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

See all formats and pricing
More options …
Volume 28, Issue 4


Extracting Conceptual Relationships and Inducing Concept Lattices from Unstructured Text

V.S. Anoop
  • Corresponding author
  • Data Engineering Lab, Indian Institute of Information Technology and Management-Kerala (IIITM-K), Thiruvananthapuram, India
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ S. Asharaf
  • Indian Institute of Information Technology and Management-Kerala (IIITM-K), Thiruvananthapuram, India
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-09-26 | DOI: https://doi.org/10.1515/jisys-2017-0225


Concept and relationship extraction from unstructured text data plays a key role in meaning aware computing paradigms, which make computers intelligent by helping them learn, interpret, and synthesis information. These concepts and relationships leverage knowledge in the form of ontological structures, which is the backbone of semantic web. This paper proposes a framework that extracts concepts and relationships from unstructured text data and then learns lattices that connect concepts and relationships. The proposed framework uses an off-the-shelf tool for identifying common concepts from a plain text corpus and then implements machine learning algorithms for classifying common relations that connect those concepts. Formal concept analysis is then used for generating concept lattices, which is a proven and principled method of creating formal ontologies that aid machines to learn things. A rigorous and structured experimental evaluation of the proposed method on real-world datasets has been conducted. The results show that the newly proposed framework outperforms state-of-the-art approaches in concept extraction and lattice generation.

Keywords: Formal concept analysis; concept extraction; concept lattices; relation extraction; knowledge discovery


  • [1]

    E. Agichtein and L. Gravano, Snowball: extracting relations from large plain-text collections, in: Proceedings of the 5th ACM Conference on Digital Libraries, pp. 85–94, San Antonio, TX, USA: ACM, 2000.Google Scholar

  • [2]

    V. S. Anoop, S. Asharaf and P. Deepak, Learning concept hierarchies through probabilistic topic modeling, Int. J. Inf. Process. 10 (2016), 1–11.Google Scholar

  • [3]

    V. S. Anoop, S. Asharaf and P. Deepak, Unsupervised concept hierarchy learning: a topic modeling guided approach, Proc. Comput. Sci. 89 (2016), 386–394.CrossrefGoogle Scholar

  • [4]

    W. W. Armstrong, Dependency structures of data base relationships, in: IFIP Congress, vol. 74, pp. 580–583, 1974.Google Scholar

  • [5]

    E. Bartl, H. Rezankova and L. Sobisek, Comparison of classical dimensionality reduction methods with novel approach based on formal concept analysis, in: International Conference on Rough Sets and Knowledge Technology, pp. 26–35, Springer, Berlin/Heidelberg, 2011.Google Scholar

  • [6]

    R. Belohlavek, Introduction to Formal Concept Analysis, Department of Computer Science, Palacky University, Olomouc, 2008.Google Scholar

  • [7]

    M. Bogatyrev, Fact extraction from natural language texts with conceptual modeling, in: International Conference on Data Analytics and Management in Data Intensive Domains, pp. 89–102, Moscow, Russia: Springer, 2016.Google Scholar

  • [8]

    S. Brin, Extracting patterns and relations from the world wide web, in: International Workshop on the World Wide Web and Databases, pp. 172–183, Springer, Berlin/Heidelberg, 1998.Google Scholar

  • [9]

    V. Codocedo, C. Taramasco and H. Astudillo, Cheating to achieve formal concept analysis over a large formal context, in: The 8th International Conference on Concept Lattices and Their Applications-CLA 2011, pp. 349–362, LORIA Nancy, France, 2011.Google Scholar

  • [10]

    C. Cui, J. Shen, Z. Chen, S. Wang and J. Ma, Learning to rank images for complex queries in concept-based search, Neurocomputing, Elsevier (2017, In Press).Web of ScienceGoogle Scholar

  • [11]

    B. A. Davey and H. A. Priestley, Introduction to Lattices and Order, Cambridge University Press, Cambridge, UK, 2002.Google Scholar

  • [12]

    D. Dligach, T. Miller, C. Lin, S. Bethard and G. Savova, Neural temporal relation extraction, European Chapter of the Association for Computational Linguistics, p. 746, Valencia, Spain, 2017.Google Scholar

  • [13]

    J. M. Gonzalez-Calabozo, F. J. Valverde-Albacete and C. Pelaez-Moreno, Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis, BMC Bioinform. 17 (2016), 374.CrossrefGoogle Scholar

  • [14]

    M. A. Hearst, Automatic acquisition of hyponyms from large text corpora, in: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 539–545, Association for Computational Linguistics, Nantes, France, 1992.CrossrefGoogle Scholar

  • [15]

    T. Herawan, M. M. Deris and A. R. Hamdan, FCA-ARMM: a model for mining association rules from formal concept analysis, in: Recent Advances on Soft Computing and Data Mining: The Second International Conference on Soft Computing and Data Mining (SCDM-2016), Bandung, Indonesia, August 18–20, 2016 Proceedings, vol. 549, p. 213, Springer, 2017.CrossrefGoogle Scholar

  • [16]

    T. Kawaumra, M. Sekine and K. Matsumura, Hyponym/hypernym detection in science and technology thesauri from bibliographic datasets, in: Semantic Computing (ICSC), 2017 IEEE 11th International Conference on, pp. 180–187, San Diego, CA, USA: IEEE, 2017.Google Scholar

  • [17]

    C. A. Kumar and S. Srinivas, Concept lattice reduction using fuzzy k-means clustering, Expert Syst. Appl. 37 (2010), 2696–2704.Web of ScienceCrossrefGoogle Scholar

  • [18]

    C. A. Kumar, Fuzzy clustering-based formal concept analysis for association rules mining, Appl. Artif. Intell. 26 (2012), 274–301.CrossrefWeb of ScienceGoogle Scholar

  • [19]

    N. Kumar, M. Kumar and M. Singh, Automated ontology generation from a plain text using statistical and NLP techniques, Int. J. Syst. Assur. Eng. Manage. 7 (2016), 282–293.Web of ScienceCrossrefGoogle Scholar

  • [20]

    S. O. Kuznetsov and S. A. Obiedkov, Comparing performance of algorithms for generating concept lattices, J. Exp. Theor. Artif. Intell. 14 (2002), 189–216.CrossrefGoogle Scholar

  • [21]

    Y. Lin, S. Shen, Z. Liu, H. Luan and M. Sun, Neural relation extraction with selective attention over instances, in: Proceedings of ACL, vol. 1, pp. 2124–2133, 2016.Google Scholar

  • [22]

    P. Monnin, M. Lezoche, A. Napoli and A. Coulet, Using formal concept analysis for checking the structure of an ontology in LOD: the example of DBpedia, in: 23rd International Symposium on Methodologies for Intelligent Systems, ISMIS, 2017.Google Scholar

  • [23]

    E. Negm, S. AbdelRahman and R. Bahgat, PREFCA: a portal retrieval engine based on formal concept analysis, Inf. Process. Manage. 53 (2017), 203–222.Web of ScienceCrossrefGoogle Scholar

  • [24]

    H. Oliveira, R. Lima, R. D. Lins, F. Freitas, M. Riss and S. J. Simske, A concept-based integer linear programming approach for single-document summarization, in: Intelligent Systems (BRACIS), 2016 5th Brazilian Conference on, pp. 403–408, Recife, Pernambuco, Brazil: IEEE, 2016.Google Scholar

  • [25]

    J. Outrata and V. Vychodil, Fast algorithm for computing fixpoints of Galois connections induced by object-attribute relational data, Inf. Sci. 185 (2012), 114–127.CrossrefWeb of ScienceGoogle Scholar

  • [26]

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel and J. Vanderplas, Scikit-learn: machine learning in Python, J. Mach. Learn. Res. 12 (2011), 2825–2830.Google Scholar

  • [27]

    S. Roller and K. Erk, Relations such as hypernymy: identifying and exploiting Hearst patterns in distributional vectors for lexical entailment, arXiv preprint arXiv:1605.05433 (2016).Google Scholar

  • [28]

    S. K. Sahu, A. Anand, K. Oruganty and M. Gattu, Relation extraction from clinical texts using domain invariant convolutional neural network, arXiv preprint arXiv:1606.09370 (2016).Google Scholar

  • [29]

    J. Seitner, C. Bizer, K. Eckert, S. Faralli, R. Meusel, H. Paulheim and S. Ponzetto, A large database of hypernymy relations extracted from the web, in: Proceedings of the 10th edition of the Language Resources and Evaluation Conference, Portoroz, Slovenia, 2016.Google Scholar

  • [30]

    J. Seitner, C. Bizer, K. Eckert, S. Faralli, R. Meusel, H. Paulheim and S. Ponzetto, A large database of hypernymy relations extracted from the web, in: Proceedings of the 10th Edition of the Language Resources and Evaluation Conference, Portoroz, Slovenia, 2016.Google Scholar

  • [31]

    V. A. Semenova and S. V. Smirnov, Intelligent analysis of incomplete data for building formal ontologies, in: CEUR Workshop Proceedings, vol. 1638, pp. 796–805, 2016.Google Scholar

  • [32]

    P. K. Singh, C. A. Kumar and A. Gani, A comprehensive survey on formal concept analysis, its research trends and applications, Int. J. Appl. Math. Comput. Sci. 26 (2016), 495–516.Web of ScienceCrossrefGoogle Scholar

  • [33]

    A. Sun, A two-stage bootstrapping algorithm for relation extraction, in: Proceedings of Recent Advances in Natural Language Processing, pp. 76–82, Borovets, Bulgaria, 2009.Google Scholar

  • [34]

    R. Wille, Restructuring lattice theory: an approach based on hierarchies of concepts, in: Ordered Sets, pp. 445–470, Springer, The Netherlands, 1982.Google Scholar

  • [35]

    R. Wille, Concept lattices and conceptual knowledge systems, Comput. Math. Appl. 23 (1992), 493–515.CrossrefGoogle Scholar

  • [36]

    A. Yates, M. Cafarella, M. Banko, O. Etzioni, M. Broadhead and S. Soderland, TextRunner: open information extraction on the web, in: Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 25–26, Association for Computational Linguistics, Rochester, New York, 2007.Google Scholar

  • [37]

    M. Zhao and S. Zhang, Identifying and validating ontology mappings by formal concept analysis, in: Proceedings of the 15th International Semantic Web Conference, pp. 61–72, Kobe, Japan, 2016.Google Scholar

About the article

Received: 2017-05-16

Published Online: 2017-09-26

Published in Print: 2019-09-25

Citation Information: Journal of Intelligent Systems, Volume 28, Issue 4, Pages 669–681, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2017-0225.

Export Citation

©2019 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in