Enhancement of domain ontology construction using a crystallizing approach

Shih, Cho-Wei; Chen, Ming-Yen; Chu, Hui-Chuan; Chen, Yuh‐Min

doi:10.1016/j.eswa.2010.12.112

Cited by 24 publications

(7 citation statements)

References 31 publications

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“…Extensive researches have concerned how to construct ontology automatically to adapt to the rapid change of massive documents on the web. Shih et al (2011) proposes a crystallising approach to enhancing domain ontology construction. Zhdanova et al (2008) proposes a community-driven ontology construction method in social networking portals.…”

Section: Related Workmentioning

confidence: 99%

“…Conceptual semantic annotation is the basis of semantic search on text (Alani, 2003), which mainly involves two aspects of technology: the auto-construction of domain ontology based on a large set of texts and the automatic semantic annotation of text. Extensive research has been conducted concerning these two aspects (Shih et al, 2011;Zhdanova et al, 2008;Goh et al, 2011;Schutz et al, 2005;Chang et al, 2006;Liu et al, 2013;Bottoni et al, 2014), opening up application potentials of conceptual semantic annotation. Yes, major technological obstacles remain.…”

Section: Introductionmentioning

confidence: 99%

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Building a large-scale testing dataset for conceptual semantic annotation of text

Wei

Zeng

Luo

et al. 2018

IJCSE

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One major obstacle facing the research on semantic annotation is lack of large-scale testing datasets. In this paper, we develop a systematic approach to constructing such datasets. This approach is based on guided ontology auto-construction and annotation methods which use little priori domain knowledge and little user knowledge in documents. We demonstrate the efficacy of the proposed approach by developing a large-scale testing dataset using information available from MeSH and PubMed. The developed testing dataset consists of a large-scale ontology, a large-scale set of annotated documents, and the baselines to evaluate the target algorithm, which can be employed to evaluate both the ontology construction algorithms and semantic annotation algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building a large-scale testing dataset for conceptual semantic annotation of text

Wei

Zeng

Luo

et al. 2018

IJCSE

View full text Add to dashboard Cite

show abstract

“…In the research of extracting knowledge tuples, existing construction models lack an in-depth study for combining knowledge tuples with context information; this issue will affect the accuracy of tuple extraction directly. References [5,[10][11][12][13] put forward their own knowledge-tuple extraction method, respectively. Reference [10] extracts adjacent vocabularies as binary knowledge tuples in the text; this approach ignores the potential semantic relationships between nonadjacent vocabularies in papers.…”

Section: Related Workmentioning

confidence: 99%

An Upper-Ontology-Based Approach for Automatic Construction of IOT Ontology

Zhang

2014

International Journal of Distributed Sensor Networks

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Ontology gives us a reliable group of concepts and the relations between concepts in an IOT system. It does not only save words of format but also accurately transfers semantic data between human users and the computers. Hence, the usefulness of resources in IOT system depends on whether the domain ontology can be constructed effectively and correctly. In this paper we propose an automated method to construct the IOT ontology. First, we explain the necessity of introducing ontology automatic construction in IOT system and summarize the major challenges in existing approaches. Secondly, we introduced the existing ontology construction methods and summarize their issues. Thirdly, we give a framework of our ontology construction and research the key algorithms in detail: (1) knowledge-tuple extraction algorithm which contains contextual information; (2) concept semantic similarity algorithm which is based on the structure of tuple; (3) knowledge-tuple extraction model which is based on the structured information. Then we build a prototype and evaluate the ontology. Finally, we make conclusions and suggest directions for future research.

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“…The development of the Semantic Web makes ontology construction a hot issue. Until now, many researchers have proposed many efficient automatic ontology construction methods [1–43]. According to the concept relation assignment methods used, most of the current automatic ontology construction methods can be classified into three types.…”

Section: Related Workmentioning

confidence: 99%

Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

Ren

2014

Journal of Information Science

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Large numbers of available scientific papers makes the research of ontology construction an attractive application area. However, there are two shortcomings for most current ontology construction approaches. First, implicit time properties of domain concepts are rarely taken into account in current approaches. Second, current automatic concept relation extraction methods mainly rely on the local context information that surrounds current considered concepts. These two problems prevent most current ontology construction methods from being employed to their full potential. To tackle these problems, we propose a hybrid learning method to integrate concepts’ global information and human experts’ knowledge together into ontology construction, among which concepts’ temporal attributes are taken into account. Our method first divides each concept into four time periods according to their attribution distribution on a time axis. Then global time-related attributions are collected for each concept. Finally, concept relations are extracted with a hybrid learning method. We evaluated our method by testing it on Chinese academic papers. It outperformed a baseline system based on only hierarchical concept relations, showing the effectiveness of our approach.

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Enhancement of domain ontology construction using a crystallizing approach

Cited by 24 publications

References 31 publications

Building a large-scale testing dataset for conceptual semantic annotation of text

Building a large-scale testing dataset for conceptual semantic annotation of text

An Upper-Ontology-Based Approach for Automatic Construction of IOT Ontology

Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

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