Ontology Learning from Text

Research on ontology learning has been carried out in many knowledge areas, especially in Artificial Intelligence. Semi-automatic or automatic ontology learning can contribute to the field of knowledge representation. Many semi-automatic approaches to ontology learning from texts have been proposed. Most of these proposals use natural language processing techniques. This paper describes a computational framework construction for semi-automated ontology learning from texts in Portuguese. Axioms are not treated in this paper. The work described here originated from the Philipp Cimiano’s proposal along with text standardization mechanisms, natural language processing, identification of taxonomic relations and techniques for structuring ontologies. In this work, a case study on public security domain was also done, showing the benefits of the developed computational framework. The result of this case study is an ontology for this area.

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Learning Personalized Ontologies from Text

Personalized Information Retrieval and Access ◽

10.4018/978-1-59904-510-8.ch001 ◽

2011 ◽

pp. 1-29 ◽

Cited By ~ 1

Author(s):

Shan Chen ◽

Mary-Anne Williams

Keyword(s):

State Of The Art ◽

The State ◽

Future Research ◽

Ontology Learning ◽

Web Personalization ◽

Research Directions ◽

Web Technologies ◽

Future Success ◽

Learning From Text ◽

Future Research Directions

Ontology learning has been identified as an inherently transdisciplinary area. Personalized ontology learning for Web personalization involves Web technologies and therefore presents more challenges. This chapter presents a review of the main concepts of ontologies and the state of the art in the area of ontology learning from text. It provides an overview of Web personalization, and identifies issues and describes approaches for learning personalized ontologies. The goal of this survey is—through the study of the main concepts, existing methods, and practices of the area—to identify new connections with other areas for the future success of establishing principles for this new transdisciplinary area. As a result, the chapter is concluded by presenting a number of possible future research directions.

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Departing the Ontology Layer Cake

Advances in Linguistics and Communication Studies - Modern Computational Models of Semantic Discovery in Natural Language ◽

10.4018/978-1-4666-8690-8.ch007 ◽

2015 ◽

pp. 167-203 ◽

Cited By ~ 4

Author(s):

Abel Browarnik ◽

Oded Maimon

Keyword(s):

Time Complexity ◽

Alternative Model ◽

Linguistic Knowledge ◽

Ontology Learning ◽

Learning Framework ◽

Layer Cake ◽

Learning From Text ◽

Scientific Papers ◽

Entity Relationship ◽

Entity Relationship Diagrams

In this chapter we analyze Ontology Learning and its goals, as well as the input expected when learning ontologies - peer-reviewed scientific papers in English. After reviewing the Ontology Learning Layer Cake model's shortcomings we suggest an alternative model based on linguistic knowledge. The suggested model would find the meaning of simple components of text – statements. From them it is easy to derive cases and roles that map the reality as a set of entities and relationships or RDF triples, somehow equivalent to Entity-relationship diagrams. Time complexity for the suggested ontology learning framework is constant (O(1)) for a sentence, and O(n) for an ontology with n sentences. We conclude that the Ontology Learning Layer Cake is not adequate for Ontology Learning from text.

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