Semi-automatic Tool for Ontology Learning Tasks

The (semi-)automated integration of new information into a data model is a functionality which is required in cases when input documents are extensive and therefore a manual integration difficult or even impossible. We proposed an ontology learning procedure combining information acquisition from st...

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Bibliographic Details
Published inIndustrial Applications of Holonic and Multi-Agent Systems pp. 119 - 129
Main Authors Šebek, Ondřej, Jirkovský, Václav, Rychtyckyj, Nestor, Kadera, Petr
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2019
SeriesLecture Notes in Computer Science
Subjects
Automotive
Ontology
Ontology learning
Text mining
Web mining
Online AccessGet full text
ISBN9783030278779
3030278778
ISSN0302-9743
1611-3349
DOI10.1007/978-3-030-27878-6_10

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Summary:The (semi-)automated integration of new information into a data model is a functionality which is required in cases when input documents are extensive and therefore a manual integration difficult or even impossible. We proposed an ontology learning procedure combining information acquisition from structured resources, such as WordNet or DBpedia, and unstructured resources using text mining techniques based on an evaluation of lexico-syntactic patterns. This approach offers a robust way, how to integrate even previously unknown information disregarding target application or domain. The proposed solution was implemented in the form of semi-automatic ontology learning tool used for integration of Excel document containing spare part records and Ford Supply Chain Ontology.
ISBN:9783030278779
3030278778
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-27878-6_10