Ontology Learning from Text Task
(Redirected from Ontology Learning Task)
An ontology learning from text task is an ontology update task (ontology design and/or ontology population) that is a supervised information extraction task.
- AKA: Ontology Learning.
- Context:
- Input: a Corpus.
- output: an Ontology.
- It can be solved by an Ontology Learning System that applies an (Ontology Learning Algorithm.
- …
- Counter-Example(s):
- See: Formal Ontology, Domain Of Discourse, Ontology Language, Ontology Engineering, Terminology Extraction, Noun Phrase.
References
2015
- (Navigli, 2015) ⇒ Roberto Navigli. (2015). “Ontologies.” In: Reference Book Journal.
- QUOTE: This chapter is about ontologies, that is, knowledge models of a domain of interest. We introduce ontologies, view them from the perspective of several fields of knowledge, and present existing ontologies and the different tasks of ontology building, learning, matching, mapping and merging.
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/ontology_learning Retrieved:2014-2-16.
- Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between those concepts from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time consuming, there is great motivation to automate the process.
Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical [1] or symbolic [2] [3]
techniques are used to extract relation signatures.
- Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between those concepts from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time consuming, there is great motivation to automate the process.
- ↑ A. Maedche and S. Staab. Learning ontologies for the semantic web. In Semantic Web Workshop 2001.
- ↑ Marti A. Hearst. Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the Fourteenth International Conference on Computational Linguistics, pages 539--545, Nantes, France, July 1992.
- ↑ Roberto Navigli and Paola Velardi. Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites, Computational Linguistics, 30(2), MIT Press, 2004, pp. 151-179.
2012
- (Wong et al., 2012) ⇒ Wilson Wong, Wei Liu, and Mohammed Bennamoun. (2012). “Ontology Learning from Text: A Look Back and Into the Future.” In: ACM Computing Surveys (CSUR) Journal, 44(4). doi:10.1145/2333112.2333115
- QUOTE: … it was not until the … the explosion of information due to the Read/Write Web that the need for a systematic body of study in large-scale extraction and representation of facts and patterns became more obvious. Over the years, that realization gave rise to a research area now known as ontology learning from text which aims to turn facts and patterns from an ever growing body of information into shareable high-level constructs for enhancing everyday applications (e.g., Web search) and enabling intelligent systems (e.g., Semantic Web).
2011
- (Sammut & Webb, 2011) ⇒ Claude Sammut, and Geoffrey I. Webb. (2011). “Ontology Learning.” In: (Sammut & Webb, 2011) p.743
2010
- (Petasis et al., 2011) ⇒ Georgios Petasis, Vangelis Karkaletsis, Georgios Paliouras, Anastasia Krithara, and Elias Zavitsanos. (2011). “Ontology Population and Enrichment: State of the Art.” In: Knowledge-driven multimedia information extraction and ontology evolution.
- Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project
2009
- (Wong, 2009) ⇒ Wilson Wong. (2009), "Learning Lightweight Ontologies from Text across Different Domains using the Web as Background Knowledge." PhD dissertation thesis, University of Western Australia.
2008
- (Buitelaar & Cimiano, 2008) ⇒ Paul Buitelaar (editor), and Philipp Cimiano (editor). (2008). “Ontology Learning and Population: Bridging the gap between text and knowledge." IOS Press, ISBN:1586038184
- (Navigli & Velardi, 2008) ⇒ Roberto Navigli, and Paola Velardi. (2008). “From Glossaries to Ontologies: Extracting Semantic Structure from Textual Definitions.” In: Ontology Learning and Population: Bridging the Gap between Text and Knowledge (P. Buitelaar and P. Cimiano, Eds.), Series information for Frontiers in Artificial Intelligence and Applications, IOS Press.
2007
- (Zhou, 2007) ⇒ Lina Zhou. (2007). “Ontology Learning: State of the Art and Open Issues.” In: Information Technology and Management, 8(3). doi:10.1007/s10799-007-0019-5
- ABSTRACT: … This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. ...
2006
- (Magnini et al., 2006) ⇒ Bernardo Magnini, Emanuele Pianta, Octavian Popescu, and Manuela Speranza. (2006). “Ontology Population from Textual Mentions: Task Definition and Benchmark.” In: Proceedings of the ACL 2006 Workshop on Ontology Population and Learning.
- (Navigli & Velardi, 2006) ⇒ Roberto Navigli, and Paola Velardi. (2006). “Ontology Enrichment Through Automatic Semantic Annotation of On-Line Glossaries.” In: Proceedings of the 15th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2006). doi:10.1007/11891451_14
- (Tanev & Magnini, 2006) ⇒ Hristo Tanev, and Bernardo Magnini. (2006). “Weakly Supervised Approaches for Ontology Population.” In: Proceedings of EACL 2006.
- (Cimiano, 2006) ⇒ Philipp Cimiano. (2006). “Ontology Learning and Population from Text. Algorithms, Evaluation and Applications." Springer. ISBN:0387306323
2005
- (Buitelaar et al., 2005) ⇒ Paul Buitelaar, Philipp Cimiano, and Bernardo Magnini. (2005). “Ontology Learning from Text: An Overview.” In: Paul Buitelaar, Philipp Cimiano, Bernardo Magnini (Eds.). “Ontology Learning from Text: Methods, Evaluation and Applications Frontiers.” In: Artificial Intelligence and Applications Series, Vol. 123, IOS Press, July 2005.
- (Biemann, 2005) ⇒ Chris Biemann. (2005). “Ontology Learning from Text: A Survey of Methods.” In: LDV Forum Journal, 20(2).
2004
- (Buitellar et al., 2004) ⇒ Paul Buitelaar (editor), Philipp Cimiano (editor), and Bernardo Magnini (editor). (2004). “Ontology Learning from Text: Methods, Evaluation and Applications." IOS Press. ISBN:1586035231
- (Valarakos et al., 2004) ⇒ Alexandros G. Valarakos, Georgios Paliouras, Vangelis Karkaletsis, and George Vouros. (2004). “Enhancing Ontological Knowledge through Ontology Population and Enrichment.” In: Proceedings of the 14th EKAW conference (EKAW 2004).
2003
- (Navigli et al., 2003) ⇒ Roberto Navigli, Paola Velardi, and Aldo Gangemi. (2003). “Ontology Learning and Its Application to Automated Terminology Translation.” In: IEEE Intelligent Systems, 18(1). doi:10.1109/MIS.2003.1179190
- (Gómez-Pérez & Manzano-Macho, 2003) ⇒ Asunción Gómez-Pérez, and David Manzano-Macho. (2003). “Deliverable 1.5: A Survey of Ontology Learning Methods and Techniques."
- QUOTE: This deliverable presents a survey of the most relevant methods, techniques and tools used for building ontologies from text, machine readable dictionaries, knowledge bases, structured-data, semi-structured data and unstructured data.
- (Shamsfard & Abdollahzade Barforoush, 2003) ⇒ Mehrnoush Shamsfard, and Ahmad Abdollahzadeh Barforoush. (2003). “The State of the Art in Ontology Learning: a framework for comparison.” In: The Knowledge Engineering Review, 18:4. doi:10.1017/S0269888903000687.
2002
- (Ding & Foo, 2002) ⇒ Ying Ding, and Schubert Foo. (2002). “Ontology Research and Development. Part 1 - a review of ontology generation.” In: Journal of Information Science, 28(2). doi:10.1177/016555150202800204
- QUOTE: … This survey is presented in two parts. The first part reviews the state-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as well as the problems facing such research. ...