Cited By
View all- Sassi STissaoui AChbeir R(2022)LEOnto+: a scalable ontology enrichment approachWorld Wide Web10.1007/s11280-021-00997-x25:6(2347-2378)Online publication date: 22-Feb-2022
LEOnto: New Approach for Ontology Enrichment using LDA
Pages 132 - 139
Abstract
The Latent Dirichlet Allocation (LDA) model [18] was originally developed and utilised for document modeling and topic extraction in Information Retrieval. To design high quality domain ontologies, effective and usable methodologies are needed to facilitate their building process. In this paper, we propose a new approach for semi-automatic ontology enriching from textual corpus based on LDA model. In our approach, LDA is adopted to provide efficient dimension reduction, able to capture semantic relationships between word-topic and topic-document in terms of probability distributions with minimum human intervention. We conducted several experiments with different model parameters and the corresponding behavior of the enriching technique was evaluated by domain experts. We also compared the results of our method with two existing learning methods using the same dataset. The study showed that our method outperforms the other methods in terms of recall and precision measures.
References
[1]
Ounas Asfari, Lilia Hannachi, Fadila Bentayeb, and Omar Boussaid. 2013. Ontological Topic Modeling to Extract Twitter users' Topics of Interest. In 8th International Conference on Information Technology and Applications (ICTTA), pp. 141--146.
[2]
Gomez Perez Asuncion and David Manzano-Macho. 2003. A survey of ontology learning methods and techniques. Technical Report D1.5. Madrid. Spain.
[3]
Nathalie Aussenac-Gilles and Patrick Seguela. 2000. Les relations sémantiques: du linguistique au formel. Cahiers de Grammaire. 25 (2000), 175--198.
[4]
Bruno Bachimont. 2000. Engagement sémantique et engagement ontologique: conception et realisation d'ontologies en ingénierie des connaissances. In Proceedings: Ingénierie des Conniassances: Evolutions récentes et nouveaux défis.
[5]
Tim Berners-Lee, James Hendler, and Ora Lassila. 2001. The Semantic Web. Scientific American. 284, 5 (2001), 34--43.
[6]
Gilles Bisson, Claire Nédellec, and Dolores Canamero. 2000. Designing Clustering Methods for Ontology Building-The Mo'K Workbench. In In Proceedings of the First International Conference on Ontology Learning, V31, p 13--28.
[7]
Brigitte Biébow and Sylvie Szulman. 1999. TERMINAE: a linguistic-based tool for the building of a domain ontology. In Proceedings of the 11th European Workshop on Knowledge Acquisition, Modelling and management, LCNS Springer, pp 49--66.
[8]
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (2003), 993--1022.
[9]
Mercedes Arguello Casteleiro, Maria Jesus Fernandez Prieto, George Demetriou, Nava Maroto, Warren Read, Diego Maseda-Fernandez, Jose Julio Des Diz, Goran Nenadic, John Keane, and Robert Stevens. 2016. Ontology Learning with Deep Learning: a Case Study on Patient Safety Using . In Proceedings of the 2016 edition of Semantic Web Applications and Tools for Healthcare and Life Sciences(SWAT4LS).
[10]
Philipp Cimiano, Johanna Volker, and Rudi Studer. 2006. Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text. Information, Wissenschaft und Praxis 57, 6-7 (October 2006), 315--320. https://www.aifb.kit.edu/images/c/c7/2006_1282_Cimiano_Ontologies_on_D_1.pdf see the special issue for more contributions related to the Semantic Web.
[11]
Philipp Cimiano. 2006. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer-Verlag New York Inc., New York, NY, United States.
[12]
Hamish Cunningham. 2005. Information Extraction, Automatic. Encyclopedia of Language and Linguistics. 18, 10 (2005), 1411--1428.
[13]
Persi Diaconis. 1977. Finite forms of de finetti's theorem on exchangeability. Synthese. 36, 2 (1977), 271--281.
[14]
Andreas Faatz and Ralf Steinmetz. 2002. Ontology enrichment with texts from the www. In Proceedings of the ECML/PKDD, Second Workshop on Semantic Web Mining, Helsinki, Finland.
[15]
David Faure and Thierry Poibeau. 2000. First experiments of using semantic knowledge learned by ASIUM for information extraction task using INTEX. In Proceedings of the First International Conference on Ontology Learning, V31, p 7--12.
[16]
Michael B. Fleischman and Eduard H. Hovy. 2002. Fine Grained Classification of Named Entities. In Proceedings of the 19th International Conference on Computational Linguistics.
[17]
Thomas R. Gruber. 2003. A translation approach to portable ontology specifications. Knowledge Acquisition. 5, 2 (2003), 199--200.
[18]
Wenming Guo, Lihong Liang, and Tianlang Deng. 2016. Topic mining for call centers based on A-LDA and distributed computing. Concurrency and Computation: Practice and Experience. 29, 3 (2016).
[19]
Karel Gutiérrez-Batista, Jesus R. Campaña, Maria-Amparo Vila, and Maria J. Martín-Bautista. 2018. An ontology-based framework for automatic topic detection in multilingual environments. International Journal of Intelligent Systems. 33, 7 (2018), 1459--1475.
[20]
Thomas Hofmann. 1990. Probabilistic latent semantic analysis. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, Pages 289--296.
[21]
Depeng Hu, Wensheng Wang, Shuyu Liu, Nengfu Xie, and GuoWei Yin. 2013. Text Segmentation Model Based LDA and Ontology for Question Answering in Agriculture. In Proceedings of the World Agricultural Outlook Conference.
[22]
Tatyana Ivanova. 2012. Ontology Learning Technologies - Brief survey, trends and problems. In Proceedings of the International Conference on Information Technologies, pp. 245--255.
[23]
Xiao li Wei, Sun Yong, Shu kui Zhang, and Yan ju Miao. 2009. Ontological concept extraction method based on maximum entropy model. Computer Engineering. 35, 24 (2009).
[24]
Wei Liu, Albert Weichselbraun, Arno Scharl, and Elizabeth Chang. 2005. Semi-Automatic Ontology Extension Using Spreading Activation. Journal of universal knowledge management. 0, 1 (2005), 50--58.
[25]
Alexander Maedche and Raphael Volz. 2001. The Text-To-Onto Ontology Extraction and Maintenance Environment. In Proceedings of the ICDM Workshop on Integrating Data Mining and Knowledge Management, USA.
[26]
Michele Missikof, Roberto Navigli, and Paola Velardi. 2002. Integrated approach to web ontology learning and engineering. Computer Journal. 35, 11 (2002), 60--63.
[27]
Emmanuel Morin. 2002. Acquisition de patrons lexico-syntaxiques caractéristiques d'une relation sémantique. Traitement automatique des langues. 40, 1 (2002), 143--166.
[28]
Lisa Posch. 2014. Enriching Ontologies with Encyclopedic Background Knowledge for Document Indexing. In The Semantic Web, P. Mika et al. (Ed.). Lecture Notes in Computer Science, vol 8797. Springer.
[29]
Engels Robert. 2001. CORPORUM-OntoExtract. Ontology Extraction Tool. Technical Report Deliverable 6 Ontoknowledge.
[30]
Dandibhotla Teja Santosha, Giridhara Sudheer Babua, Shakti Prasada, and Abhishek Vivekananda. 2016. Opinion Mining of Online Product Reviews from Traditional LDA Topic Clusters using Feature Ontology Tree and Sentiwordnet. Education and Management Engineering. 6 (2016), 34--44.
[31]
Jones Steve and Gordon W. Paynter. 2002. Automatic extraction of document keyphrases for use in digital libraries: evaluation and applications. Journal of the American Society for Information Science and Technology. 53, 8 (2002), 653--677.
[32]
Mark Steyvers and Tom Griffiths. 2007. Probabilistic topic models. In Handbook of latent semantic analysis., T. K. Landauer, D. S. McNamara, S. Dennis, and W. Kintsch (Eds.). Lawrence Erlbaum Associates Publishers, pp.427--448.
[33]
Paola Velardia, Paolo Fabriani, and Michele Missikoff. 2001. Using text processing techniques to automatically enrich a domain ontology. In Proceedings of the international conference on Formal Ontology in Information Systems-Volume. pp 270--284.
[34]
Shih-Hung Wu and Wen-Lian Hsu. 2002. SOAT: a semi-automatic domain ontology acquisition tool from Chinese corpus. In Proceedings of the 19th International Conference on Computational Linguistics.
[35]
Feiyu Xu, Daniela Kurz, Jakub Piskorski, and Sven Schmeier. 2002. A Domain Adaptive Approach to Automatic Acquisition of Domain Relevant Terms and their Relations with Bootstrapping. In Proceedings of the 3rd International Conference on Language Resources an Evaluation (LREC).
[36]
Elias Zavitsanos, Georgios Paliouras, George A. Vouros, and Sergios Petridis. 2007. Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora. In IEEE/WIC/ACM International Conference on Web Intelligence (WI'07), Fremont, CA, pp.402--408.
[37]
Lina Zhou. 2007. Ontology learning: State of the art and open issues. Information Technology and Management 8, 3 (September 2007), 241--252. https://link.springer.com/article/10.1007/s10799-007-0019-5
Index Terms
- LEOnto: New Approach for Ontology Enrichment using LDA
Recommendations
Learn2Construct: An automatic ontology construction based on LDA from texual data
MEDES '21: Proceedings of the 13th International Conference on Management of Digital EcoSystemsIn recent years, the research on Ontology Learning has become a hot topic among researchers because of the exponential increase of the number of documents and textual data not only on the web but also in digital libraries. This has participated to the ...
LEOnto+: a scalable ontology enrichment approach
AbstractDistributional semantic models like the Latent Dirichlet Allocation (LDA) model Guo et al. (Concurr. Comput.: Pract. Exper. 29(3), 319–343 2016) consist of defining similar representation of words according to their similar context. LDA has been ...
Probabilistic Topic Models for Enriching Ontology from Texts
AbstractThe ontology enrichment process is text-based and the application domain in hand is circumscribed to the content of the related texts. However, the main challenge in ontology enrichment is its learning, since there is still a lack of relevant ...
Comments
Information & Contributors
Information
Published In
November 2020
170 pages
ISBN:9781450381154
DOI:10.1145/3415958
- General Chairs:
- Richard Chbeir,
- Yannis Manolopoulos,
- Ernesto Damiani,
- Djamal Benslimane,
- Program Chairs:
- Ladjel Bellatreche,
- Tadeusz Morzy
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 November 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
MEDES '20
MEDES '20: 12th International Conference on Management of Digital EcoSystems
November 2 - 4, 2020
Virtual Event, United Arab Emirates
Acceptance Rates
MEDES '20 Paper Acceptance Rate 19 of 27 submissions, 70%;
Overall Acceptance Rate 267 of 682 submissions, 39%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 51Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Oct 2024
Other Metrics
Citations
Cited By
View all- Sassi STissaoui AChbeir R(2022)LEOnto+: a scalable ontology enrichment approachWorld Wide Web10.1007/s11280-021-00997-x25:6(2347-2378)Online publication date: 22-Feb-2022
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in