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

^*Corresponding author for this work

Department of Management Science

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

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.

Original language	English
Article number	102098
Journal	World Patent Information
Volume	68
DOIs	https://doi.org/10.1016/j.wpi.2022.102098
State	Published - Mar 2022

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.wpi.2022.102098

Cite this

@article{d85102d64b7647da8706c22ea8937f29,

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",

month = mar,

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

language = "English",

volume = "68",

journal = "World Patent Information",

issn = "0172-2190",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Ontology-based knowledge representation and semantic topic modeling for intelligent trademark legal precedent research

AU - Li, Gi Kuen J.

AU - Trappey, Charles V.

AU - Trappey, Amy J.C.

AU - Li, Annie A.S.

PY - 2022/3

Y1 - 2022/3

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

UR - http://www.scopus.com/inward/record.url?scp=85125676066&partnerID=8YFLogxK

U2 - 10.1016/j.wpi.2022.102098

DO - 10.1016/j.wpi.2022.102098

M3 - Article

AN - SCOPUS:85125676066

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

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this