Ontology-based knowledge representation and semantic topic modeling for intelligent trademark legal precedent research

Gi Kuen J. Li; Charles V. Trappey; Amy J.C. Trappey; Annie A.S. Li

doi:10.1016/j.wpi.2022.102098

Ontology-based knowledge representation and semantic topic modeling for intelligent trademark legal precedent research

Gi Kuen J. Li^*, Charles V. Trappey, Amy J.C. Trappey, Annie A.S. Li

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管理科學系

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摘要

An intelligent methodology and its prototype system are developed for automatically discovering legal precedents using semantic analysis. The concept of the trademark legal precedent recommendation was originated from our TE2020 conference paper. The approach is to identify matching cases related to given seed case with respect to their legal case brief attributes using advanced text mining techniques. In the paper, dynamic topic modeling is further developed to analyze the dataset over three time-sequential cohorts to identify trademark law topics varied over time. Further, the prototype system was demonstrated and verified using real trademark case analysis with satisfactory results.

原文	English
文章編號	102098
期刊	World Patent Information
卷	68
DOIs	https://doi.org/10.1016/j.wpi.2022.102098
出版狀態	Published - 3月 2022

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title = "Ontology-based knowledge representation and semantic topic modeling for intelligent trademark legal precedent research",

abstract = "An intelligent methodology and its prototype system are developed for automatically discovering legal precedents using semantic analysis. The concept of the trademark legal precedent recommendation was originated from our TE2020 conference paper. The approach is to identify matching cases related to given seed case with respect to their legal case brief attributes using advanced text mining techniques. In the paper, dynamic topic modeling is further developed to analyze the dataset over three time-sequential cohorts to identify trademark law topics varied over time. Further, the prototype system was demonstrated and verified using real trademark case analysis with satisfactory results.",

keywords = "Latent Dirichlet allocation (LDA), Legal research, Ontology-based knowledge system, Semantic topic modeling",

author = "Li, {Gi Kuen J.} and Trappey, {Charles V.} and Trappey, {Amy J.C.} and Li, {Annie A.S.}",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

year = "2022",

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doi = "10.1016/j.wpi.2022.102098",

language = "English",

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AU - Li, Gi Kuen J.

AU - Trappey, Charles V.

AU - Trappey, Amy J.C.

AU - Li, Annie A.S.

PY - 2022/3

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N2 - An intelligent methodology and its prototype system are developed for automatically discovering legal precedents using semantic analysis. The concept of the trademark legal precedent recommendation was originated from our TE2020 conference paper. The approach is to identify matching cases related to given seed case with respect to their legal case brief attributes using advanced text mining techniques. In the paper, dynamic topic modeling is further developed to analyze the dataset over three time-sequential cohorts to identify trademark law topics varied over time. Further, the prototype system was demonstrated and verified using real trademark case analysis with satisfactory results.

AB - An intelligent methodology and its prototype system are developed for automatically discovering legal precedents using semantic analysis. The concept of the trademark legal precedent recommendation was originated from our TE2020 conference paper. The approach is to identify matching cases related to given seed case with respect to their legal case brief attributes using advanced text mining techniques. In the paper, dynamic topic modeling is further developed to analyze the dataset over three time-sequential cohorts to identify trademark law topics varied over time. Further, the prototype system was demonstrated and verified using real trademark case analysis with satisfactory results.

KW - Latent Dirichlet allocation (LDA)

KW - Legal research

KW - Ontology-based knowledge system

KW - Semantic topic modeling

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DO - 10.1016/j.wpi.2022.102098

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SN - 0172-2190

VL - 68

JO - World Patent Information

JF - World Patent Information

M1 - 102098

ER -

Ontology-based knowledge representation and semantic topic modeling for intelligent trademark legal precedent research

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