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An Overview of Shallow and Deep Natural Language Processing for Ontology Learning

An Overview of Shallow and Deep Natural Language Processing for Ontology Learning

Amal Zouaq (Simon Fraser University - Athabasca University, Canada)
Copyright: © 2011 |Pages: 22
DOI: 10.4018/978-1-60960-625-1.ch002
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Abstract

This chapter gives an overview over the state-of-the-art in natural language processing for ontology learning. It presents two main NLP techniques for knowledge extraction from text, namely shallow techniques and deep techniques, and explains their usefulness for each step of the ontology learning process. The chapter also advocates the interest of deeper semantic analysis methods for ontology learning. In fact, there have been very few attempts to create ontologies using deep NLP. After a brief introduction to the main semantic analysis approaches, the chapter focuses on lexico-syntactic patterns based on dependency grammars and explains how these patterns can be considered as a step towards deeper semantic analysis. Finally, the chapter addresses the “ontologization” task that is the ability to filter important concepts and relationships among the mass of extracted knowledge.
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2. Background

There are a number of resources that describe what an ontology is, with the most cited definition being the one presented by (Gruber, 93): “An ontology is a formal specification of a conceptualization”. Although this definition may seem too broad, we can extract from it two keywords that are essential for our understanding of ontologies: formal and conceptualization.

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    The formal characteristic: In the domain of computer science and formal logic, a formal system designates a system using a formal language, a grammar that indicates the well-expressed formulas according to the language and a set of axioms or inference rules to reason over this language. A formal language is defined using a set of symbols.

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    The conceptual characteristic: Having its root in philosophy, the notion of concept has been widely used in the Artificial Intelligence community. According to (Guarino, 98), a conceptualization must be defined on an intentional level and an extensional level. The intentional level deals with the meaning of what is being defined (the domain of interest), while the extensional level describes the instances of that domain.

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