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Title
Abstract
FAQs
Introduction
Look at Previous Surveys
Statistics-Based
An Overview of Prominent Ontology Learning Systems
Current Trends and Future Research Directions
References
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Computer Science
Natural Language Processing

Ontology Learning from Text

Profile image of Marko GrobelnikMarko Grobelnik

2000

https://doi.org/10.1145/2333112.2333115
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36 pages

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Abstract

Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.

FAQs

sparkles

AI

What key factors hinder large-scale deployment of ontology learning systems?add

The study identifies bottlenecks in user involvement and reliance on handcrafted structured knowledge sources as significant limitations, necessitating the exploration of web-based collective intelligence to mitigate these challenges.

How do lightweight ontologies differ from formal ontologies?add

Lightweight ontologies offer minimal axiomatic structures, often lacking extensive constraints, while formal ontologies leverage frameworks like OWL for rigorous specifications and reasoning, emphasizing the necessity for axioms in intelligent systems.

What methodologies are common in contemporary ontology learning techniques?add

Recent methodology trends include the use of statistical, linguistic, and logic-based techniques, often combined in hybrid systems, demonstrating advancements in relation discovery from unstructured web data and user-generated content.

What are the main outputs produced in ontology learning processes?add

Ontology learning typically yields terms, concepts, taxonomic relations, non-taxonomic relations, and axioms, structured in a hierarchical framework to facilitate systematic organization of knowledge.

How have research interests shifted in ontology learning over the past decade?add

There has been a notable shift towards leveraging web data for enhanced term extraction and relation discovery, as well as increasing emphasis on social data for automating ontology learning.

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