Description
An Ontoloy is a commonly defined as an explicit and formal specification of a shared conceptualization of a domain of interest. Ontologies formalize the intensional aspects of a domain, whereas the extensional part is provided by a knowledge base that contains assertions abou instances of concepts and relations as defined by the ontology. The process of defining and instantiating a knowledge base is reffered to as knowledge markup or ontology population, whereas (semi-) automatic support in ontology development is usually referred to as ontology learning.
Ontologies have been broadly used in knowledge management applications, with a recent upsurge around Semantic Web applications and research. In recent years, ontologies have regained interest also within the NLP community, specifically in the context of such applications as information extraction, text mining, and question answering.
However, as ontology development is a tedious and costly process there has been an equally growing interest in the automatic learning or extraction of ontologies. Much of this work has been directed towards extraction from textual data as human language is a primary mode of knowledge transfer. In this way, textual data provide both a resource for the ontology learning process as well as an application medium for developed ontologies.
The tutorial will give an introduction to ontology learning from textual data.
It will assume no prior knowledge of the field and will thus be suited for people with very different backgrounds, altough some emphasis will be placed on the role of linguistic analysis, NLP and machine learning as used in ontology learning. Also, the role of ontologies in NLP applications will be discussed, i. e. in information extraction, text mining, information etrieval, machine translation, question answering, and the relation between ontologies and lexical semantics.
|
Outline of the Tutorial
Part
I. Introduction
-
Ontologies - origin and purpose
Part
II. Ontologies for NLP
-
Applications: IR, IE, MT, QA
- Ontologies and Lexical Semantics
Part
III. Layers in Ontology Learning from Text
-
Terms
- (Multilingual) Synonyms
- Concept Formation - Intension & Extension
-
Relations
-
Concept and Relation Hierarchies
-
Axiom Schemata and General Axioms
Part IV. Wrap-Up
-
What have we Learned in the Tutorial?
- Where are we Today ?
- Where are we Heading ?
|
