Managing Knowledge in Light of Its Evolution Process: An Empirical Study on Citation Network-Based Patent Classification

Li, Xin; Chen, Hsinchun; Zhang, Zhu; Li, Jiexun; Nunamaker, Jay F.

doi:10.2753/mis0742-1222260106

Cited by 29 publications

(11 citation statements)

References 47 publications

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“…To convert graphs to string sequences so that string matching techniques can be used, Robles-Kelly and Hancock [2005] used a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. For labeled graphs, random walk paths have been used to represent graphs as string sequences of node classes with associated occurrence probabilities [Kashima et al 2003;Li et al 2009]. Accordingly, the graph comparison module utilized by GBS uses random walk paths to represent the web graphs associated with candidate pages as well as known relevant and irrelevant pages.…”

Section: Graph Comparison Modulementioning

confidence: 99%

“…Each web graph is then represented by various random walk path (RWP) sequences, where each RWP is composed of a series of labeled nodes that signify the traversal of a particular path along the graph [Kashima et al 2003]. RWP sequences have been widely used in graph comparison tasks, for example patent classification using patent citation networks [Li et al 2009]. At each step, a random walk either jumps to one of the in-links or stops based on a probability distribution.…”

Section: Graph Comparison Modulementioning

confidence: 99%

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Sentimental Spidering

Abbasi

Zeng

et al. 2012

ACM Trans. Inf. Syst.

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Despite the increased prevalence of sentiment-related information on the Web, there has been limited work on focused crawlers capable of effectively collecting not only topic-relevant but also sentiment-relevant content. In this article, we propose a novel focused crawler that incorporates topic and sentiment information as well as a graph-based tunneling mechanism for enhanced collection of opinion-rich Web content regarding a particular topic. The graph-based sentiment (GBS) crawler uses a text classifier that employs both topic and sentiment categorization modules to assess the relevance of candidate pages. This information is also used to label nodes in web graphs that are employed by the tunneling mechanism to improve collection recall. Experimental results on two test beds revealed that GBS was able to provide better precision and recall than seven comparison crawlers. Moreover, GBS was able to collect a large proportion of the relevant content after traversing far fewer pages than comparison methods. GBS outperformed comparison methods on various categories of Web pages in the test beds, including collection of blogs, Web forums, and social networking Web site content. Further analysis revealed that both the sentiment classification module and graph-based tunneling mechanism played an integral role in the overall effectiveness of the GBS crawler.

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Section: Graph Comparison Modulementioning

confidence: 99%

Section: Graph Comparison Modulementioning

confidence: 99%

Sentimental Spidering

Abbasi

Zeng

et al. 2012

ACM Trans. Inf. Syst.

Self Cite

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“…Patents, which contain a great amount of knowledge on technical innovations, provide a valuable source of information on technology development and innovative activities (Li et al 2009). It is crucial to analyze patent information to understand industrial trends and set the future developing directions.…”

Section: Introductionmentioning

confidence: 99%

Patent co-citation networks of Fortune 500 companies

Wang

Zhang

2011

Scientometrics

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This paper provides an overview of the progression of technology structure based on patent co-citation networks. Methods of patent bibliometrics, social network analysis and information visualization are employed to analyze patents of Fortune 500 companies indexed in Derwent Innovations Index, the largest patent database in the world. Based on the co-citation networks, several main technology groups are identified, including Chemicals, Petroleum Refining, Motor Vehicles, Pharmaceuticals, Electronics, etc. Relationships among the leading companies and technology groups are also revealed.

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“…Due to the growing number of patent applications every year, patent classification [3] [4] [5] based on patent content analysis has been proposed to help assign patent applications to the right examiners and classify patents to patent classification schemes, such as the United States Patent Classification (USPC) system and the International Patent Classification (IPC) system. However, for patent maintenance decision, the patents are already published and the class information is already known.…”

Section: Related Workmentioning

confidence: 99%

“…Existing web services (such as Google Patent Search, MicroPatent and Delphion) and research work on patent analysis and management have been focusing on automatic patent classification [3] [4] [5], prior art search [6] [7], patent value analysis [8] [9], patent quality prediction [10] [11] [12] [13], novelty detection [2] [14] or traditional link based searching using [15] or HITS [16].…”

Section: Related Workmentioning

confidence: 99%

Patent Maintenance Recommendation with Patent Information Network Model

Jin

Spangler

Chen

et al. 2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-Patents are of crucial importance for businesses, because they provide legal protection for the invented techniques, processes or products. A patent can be held for up to 20 years. However, large maintenance fees need to be paid to keep it enforceable. If the patent is deemed not valuable, the owner may decide to abandon it by stopping paying the maintenance fees to reduce the cost. For large companies or organizations, making such decisions is difficult because too many patents need to be investigated. In this paper, we introduce the new patent mining problem of automatic patent maintenance prediction, and propose a systematic solution to analyze patents for recommending patent maintenance decision. We model the patents as a heterogeneous time-evolving information network and propose new patent features to build model for a ranked prediction on whether to maintain or abandon a patent. In addition, a network-based refinement approach is proposed to further improve the performance. We have conducted experiments on the large scale United States Patent and Trademark Office (USPTO) database which contains over four million granted patents. The results show that our technique can achieve high performance.

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Managing Knowledge in Light of Its Evolution Process: An Empirical Study on Citation Network-Based Patent Classification

Cited by 29 publications

References 47 publications

Sentimental Spidering

Sentimental Spidering

Patent co-citation networks of Fortune 500 companies

Patent Maintenance Recommendation with Patent Information Network Model

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