Learning ontologies from natural language texts
From Ontology Learning
|title||Learning ontologies from natural language texts|
|author||Mehrnoush Shamsfard, Barforoush, Ahmad Abdollahzadeh|
Research on ontology is becoming increasingly widespread in the computer science community. The major problems in building ontologies are the bottleneck of knowledge acquisition and time-consuming construction of various ontologies for various domains/applications. Meanwhile moving toward automation of ontology construction is a solution. We proposed an automatic ontology building approach. In this approach, the system starts from a small ontology kernel and constructs the ontology through text understanding automatically. The kernel contains the primitive concepts, relations and operators to build an ontology. The features of our proposed model are being domain/application independent, building ontologies upon a small primary kernel, learning words, concepts, taxonomic and non-taxonomic relations and axioms and applying a symbolic, hybrid ontology learning approach consisting of logical, linguistic based, template driven and semantic analysis methods. Hasti is an ongoing project to implement and test the automatic ontology building approach. It extracts lexical and ontological knowledge from Persian (Farsi) texts. In this paper, at first, we will describe some ontology engineering problems, which motivated our approach. In the next sections, after a brief description of Hasti, its features and its architecture, we will discuss its components in detail. In each part, the learning algorithms will be described. Then some experimental results will be discussed and at last, we will have an overview of related works and will introduce a general framework to compare ontology learning systems and will compare Hasti with related works according to the framework.