GRAONTO: A graph-based approach for automatic construction of domain ontology

Hou, Xuyan; Ong, S. K.; Nee, A. Y. C.; Zhang, X. T.; Liu, W. J.

doi:10.1016/j.eswa.2011.03.090

Cited by 34 publications

(9 citation statements)

References 31 publications

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“…To further determine the relation type, a set of linguistic patterns along with the semantic role labelling information are used. Other similar work can be seen in the literature [12–20].…”

Section: Related Workmentioning

confidence: 54%

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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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Section: Related Workmentioning

confidence: 54%

“…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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“…Tree structure is quite common to be used to represent syntactic structure of linguistics (Cer et al, 2010;Wu et al, 2009;Hou et al, 2011). Trees are used for reflecting a constituency structure and a dependency structure.…”

Section: Dependency Relations Classificationmentioning

confidence: 99%

Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features

Zhang

Sekhari

Ouzrout

et al. 2016

Engineering Applications of Artificial Intelligence

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a b s t r a c tOpinion mining mainly involves three elements: feature and feature-of relations, opinion expressions and the related opinion attributes (e.g. Polarity), and feature-opinion relations. Although many works have emerged to achieve its aim of gaining information, the previous researches typically handled each of the three elements in isolation, which cannot give sufficient information extraction results; hence, the complexity and the running time of information extraction is increased. In this paper, we propose an opinion mining extraction algorithm to jointly discover the main opinion mining elements. Specifically, the algorithm automatically builds kernels to combine closely related words into new terms from word level to phrase level based on dependency relations; and we ensure the accuracy of opinion expressions and polarity based on: fuzzy measurements, opinion degree intensifiers, and opinion patterns. The 3458 analyzed reviews show that the proposed algorithm can effectively identify the main elements simultaneously and outperform the baseline methods. The proposed algorithm is used to analyze the features among heterogeneous products in the same category. The feature-by-feature comparison can help to select the weaker features and recommend the correct specifications from the beginning life of a product. From this comparison, some interesting observations are revealed. For example, the negative polarity of video dimension is higher than the product usability dimension for a product. Yet, enhancing the dimension of product usability can more effectively improve the product.

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“…Finally, the author uses WordNet as a reference to classify and integrate the extracted concepts and corresponding relationships into the final domain ontology. (Hou et al, 2011) proposed a method for automatic construction of domain ontology based on graph theory. (Shih et al, 2011) proposed a domain ontology construction method based on a crystallizing model.…”

Section: Introductionmentioning

confidence: 99%

Semantic Primitives Extraction for XBRL Domain Ontology

Ye¹,

Pan²

2020

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At present, XBRL (Extensible Business Reporting Language) has been used in more and more countries and organizations. Although XBRL has achieved a series of remarkable achievements, the current development of XBRL has also encountered bottlenecks. On the one hand, the XBRL field has not been unified. On the other hand, the semantics of concepts in the domain ontology of XBRL is weak, resulting in the slow application and promotion of XBRL. This paper regulates the extraction of semantic primitives in the ontology of XBRL domain from the perspective of semantics and solves the problem of "how to extract semantic primitives". The solution to this problem can promote computers to better understand XBRL financial reports and reduce the technical difficulty of XBRL. This paper comprehensively uses the theories of semantic primitives, graph theory, and domain ontology. First, it analyzes the research status and shortcomings of semantic primitive extraction methods. Second, it constructs a graph of accounting term relationship network from the perspective of graph theory. The extraction of semantic primitives. Finally, the validity of semantic primitive extraction is analyzed and verified.

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GRAONTO: A graph-based approach for automatic construction of domain ontology

Cited by 34 publications

References 31 publications

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

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

Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features

Semantic Primitives Extraction for XBRL Domain Ontology

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