Link grammar를 이용한 도메인 온톨로지 확장 방안

Abstract

Ontology is constructed with concept, definition, and these relation. lately, much of data, ontology do not support reasoning information to users. so, there are so many necessary for ontology population. therefore many studies in ontology population. but most of study, they used manually extraction to concept, relation, properties. this method spend a lot of time and money to data mining. so, to solve this problem, automatic ontology population have studied. it save much of time, money. however it requires another knowledge-base or thesaurus. and they dependent on its source(thesaurus, knowledge-base, etc) In my study, I deal with ontology population which are not using another famous dictionary, but using Link grammar and infobox of wikipedia. Link grammar is a syntactic parsing theory of English. I analyze link pattern to determine what link pattern will be candidate of relation-concept and extracted triples. then added weight value to each triples. in the result, i got relation and concepts, in order to weight value. weight values are purpose to extract good triples. then i apply infobox, navbox to classify relation, concepts. First, I gather biology documents in wikipedia. then to get a body part of wiki-document, apply stampling process. and extract terminologies, which make database to extract important sentences. i set the process for terminology extraction(tagging, tokenize, extraction). and verify terminologies. then i select important sentences to apply Link grammar. I define 7-patterns to get concept-relation triple. after i find good triple through the PMI value and TF value. At last, i classify concept with infobox, navbox and classify relation with pre-defined classify table which defined Relation hierarchy. i make metadata with this properties, then visualization it. There are some error in my study, it occurs in wrong Pos tagging, stopword interruption. anaphora relosolution. but visualization works well, and it's possible to extract more than 1 relations in 1 sentence. finally, I will study about named entity recognization to finding correct triples .