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Tytuł pozycji:

Empirical study of constructing a knowledge organization system of patent documents using topic modeling

Tytuł :
Empirical study of constructing a knowledge organization system of patent documents using topic modeling
Autorzy :
GTM-2012 Global Tech Mining Conference(2 ; Montreal, Quebec, ; 2012-09-05)
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Temat :
Analyse composante principale
Principal component analysis
Análisis componente principal
Analyse corrélation
Correlation analysis
Análisis correlación
Classification automatique
Automatic classification
Clasificación automática
Produit recherche
Search result
Resultado búsqueda
Recherche scientifique
Scientific research
Investigación científica
Knowledge organization system
Principal Component Analysis
Term clumping
Text clustering
Topic model
Sciences exactes et technologie
Exact sciences and technology
Sciences et techniques communes
Sciences and techniques of general use
Sciences de l'information. Documentation
Information science. Documentation
Sciences de l'information et des bibliothèques. Etude d'ensemble
Library and information science. General aspects
Bibliométrie. Scientométrie. Evaluation
Bibliometrics. Scientometrics. Evaluation
Sciences de l'information et de la communication
Information and communication sciences
Bibliométrie. Scientométrie
Bibliometrics. Scientometrics
Sciences de l'information communication
Źródło :
Tech Mining, Analysis, and VisualizationScientometrics (Print). 100(3):787-799
Wydawca :
Dordrecht: Springer, 2014.
Rok publikacji :
Opis fizyczny :
print; 13; 3/4 p
Materiał oryginalny :
Typ dokumentu :
Conference Paper
Opis pliku :
Język :
Afiliacje autora :
University of Chinese Academy of Sciences, No.19A Yuquan Rd., Beijing 100049, China
Chengdu Document and Information Center, Chinese Academy of Sciences, No. 16 South Sec.2 Yihuan Rd., Chengdu 610041, China
Georgia Institute of Technology, Atlanta, GA, United States
Search Technology, Inc., Norcross, GA, United States
Dostęp URL :
Prawa :
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Numer akcesji :
A knowledge organization system (KOS) can help easily indicate the deep knowledge structure of a patent document set. Compared to classification code systems, a personalized KOS made up of topics can represent the technology information in a more agile, detailed manner. This paper presents an approach to automatically construct a KOS of patent documents based on term clumping, Latent Dirichlet Allocation (LDA) model, K-Means clustering and Principal Components Analysis (PCA). Term clumping is adopted to generate a better bag-of-words for topic modeling and LDA model is applied to generate raw topics. Then by iteratively using K-Means clustering and PCA on the document set and topics matrix, we generated new upper topics and computed the relationships between topics to construct a KOS. Finally, documents are mapped to the KOS. The nodes of the KOS are topics which are represented by terms and their weights and the leaves are patent documents. We evaluated the approach with a set of Large Aperture Optical Elements (LAOE) patent documents as an empirical study and constructed the LAOE KOS. The method used discovered the deep semantic relationships between the topics and helped better describe the technology themes of LAOE. Based on the KOS, two types of applications were implemented: the automatic classification of patents documents and the categorical refinements above search results.

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