Automatically inducing ontologies from corpora

Natural Language Processing and Information …, 2000

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.

International Journal of Human-Computer …, 2004

Proceedings of the 6th …, 2008

Domain ontology plays an important role in annotating web resources with proper semantic information. The underlying assumption behind this work is that the noun phrases appearing in the headings of a document as well as the document's hierarchical structure can be used to discover the concepts and is-a relations between them in the documents' domain. In order to verify this assumption a methodology was proposed, and a system was implemented and applied on a set of Arabic agricultural extension documents. The system takes as input a root concept, analyzes all input documents' heading structure, extracts concepts from headings and builds a taxonomical ontology. The resulting ontology was verified against a modified version of AGROVOC ontology, which is a hand-made ontology developed by Food and Agriculture Organization of the United Nation (FAO). The F-score obtained was 52.29% for lexical evaluation of diseases ontology and 39.64% for lexical evaluation of insects' ontology. Taxonomical F-score was 44.59% for diseases ontology and 31.38% for insects' ontology.

2001

This paper describes a method for constructing an ontology which will represent the set of web pages on a specified site. We are developing a technique that will extract knowledge from digital sources, create ontologies containing reusable knowledge to be shared with software agents, and present a view of this knowledge to users. This method will provide a solution to the problem of classifying information and supporting mechanisms that explore its structure, as well as allowing knowledge to be extracted and shared with other software agents.

On the Move to …, 2004

We report on an a set of experiments carried out in the context of the Flemish OntoBasis project. Our purpose is to extract semantic relations from text corpora in an unsupervised way and use the output as preprocessed material for the construction of ontologies from scratch. The experiments are evaluated in a quantitative and "impressionistic" manner. We have worked on two corpora: a 13M words corpus composed of Medline abstracts related to proteins (SwissProt), and a small legal corpus (EU VAT directive) consisting of 43K words. Using a shallow parser, we select functional relations from the syntactic structure subject-verb-direct-object. Those functional relations correspond to what is a called a "lexon". The selection is done using prepositional structures and statistical measures in order to select the most relevant lexons. Therefore, the paper stresses the filtering carried out in order to discard automatically all irrelevant structures . Domain experts have evaluated the precision of the outcomes on the SwissProt corpus. The global precision has been rated 55%, with a precision of 42% for the functional relations or lexons, and a precision of 76% for the prepositional relations. For the VAT corpus, a knowledge engineer has judged that the outcomes are useful to support and can speed up his modelling task. In addition, a quantitative scoring method (coverage and accuracy measures resulting in a 52.38% and 47.12% score respectively) has been applied.

RDF/XML has been widely recognized as the standard for annotating online Web documents and for transforming the HTML Web to the so called Semantic Web. In order to enable widespread usability for the Semantic Web there is a need to bootstrap large, rich and up-todate domain ontologies that organize most relevant concepts, their relationships and instances. In this paper, we present automated techiques for bootstrapping and populating specialized domain ontologies by organizing and mining a set of relevant Web sites provided by the user. We develop algorithms that detect and utilize HTML regularities in the Web documents to turn them into hierarchical semantic structures encoded as XML. Next, we present tree-mining algorithms that identify key domain concepts and their taxonomical relationships. We also extract semistructed concept instances annotated with their labels whenever they are available. Experimental evaluation for the News and Hotels domain indicates that our algorithms can bootstrap and populate domain specific ontologies with high precision and recall.

Proceedings of the international …, 2001

Though the utility of domain Ontologies is now widely acknowledged in an increasing number of domains, several barriers must be overcome before Ontologies become practical and useful tools. A critical issue is the task of identifying, defining, and entering the concept definitions. In case of large and complex application domains this task can be lengthy, costly, and controversial (since different persons may have different points of view about the same concept). To reduce time, cost (and, sometimes, harsh discussions) it is highly advisable to refer, in constructing or updating an ontology, to the documents available in the field. In this paper we describe OntoLearn, a text-mining tool devised to improve human productivity during the process of ontology construction.

2012

There is a huge body of domain-specific knowledge embedded in free-text repositories such as engineering documents, instruction manuals, medical references and legal files.

WIT Transactions on Information and Communication Technologies, 2000

The paper describes a multi-phase process of automatic construction of the domain specific ontology from text databases, in which various text mining and natural-language understanding methods are used. The ontology we wish to develop describes a well-defined technical domain. Hence it is called a domain specific ontology, opposed to universal ontologies. We discuss the major techniques used in the process and show some preliminary results.