Mining and modeling linkage information from citation context for improving biomedical literature retrieval

Yin, Xiaoshi; Huang, Jimmy Xiangji; Li, Zhoujun

doi:10.1016/j.ipm.2010.03.010

Cited by 16 publications

(10 citation statements)

References 17 publications

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“…Nonetheless, the examination of sets with names was neglected, which demonstrates that there are a few equivalent words in the content that were neglected to be caught in the Bio Thesaurus [59], [60]. [54], [56], [77], [80] all extracted keywords from biomedical records. However, this study only focused on the biomedical literature corpus and could be adapted to literature retrieval in other domains [89], [90].…”

Section: Resultsmentioning

confidence: 99%

“…A framework of a probabilistic combination nature for the purpose of precisely linking citation information with the content-based information retrieval weighting model is suggested by Yin et al [54]. Through a case study, they were able to observe the model of linking information that was available in the citation graph.…”

Section: A Biomedical Text Mining Reviewmentioning

confidence: 99%

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A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abed¹,

Yuan²,

Li³

2017

ijacsa

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Abstract-The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein identification, patient medical records, genome sequencing, medical imaging and a huge set of biomedical research data are being generated day to day. The increase of biomedical data consists of both structured and unstructured data. Subsequently, in a traditional database system (structured data), managing and extracting useful information from unstructured-biomedical data is a tedious job. Hence, mechanisms, tools, processes, and methods are necessary to apply on unstructured biomedical data (text) to get the useful business data. The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way. Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research. Text mining is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are diverse and span to multiple disciplines, ranging from biomedicine to legal, business intelligence and security. In this survey paper, the researcher identifies and discusses biomedical data (text) mining issues, and recommends a possible technique to cope with possible future growth.

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Section: Resultsmentioning

confidence: 99%

Section: A Biomedical Text Mining Reviewmentioning

confidence: 99%

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abed¹,

Yuan²,

Li³

2017

ijacsa

View full text Add to dashboard Cite

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“…There are three famous linkage analysis algorithm to measuring the publication significance (Yin, Huang & Li, 2011): Degree Distribution, HITS and PageRank. Degree Distribution, this linkage analysis algorithm uses the definition of "Popularity of a document".…”

Section: Pagerank Analysismentioning

confidence: 99%

Optimization in supply chain management, the current state and future directions: A systematic review and bibliometric analysis

Movahedipour

Yang

Zeng

et al. 2016

JIEM

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Abstract:Purpose: The purpose of this paper is finding the current state of research and identifies high-potential area for future investigation in optimization in supply chain management.Design/methodology/approach: In this paper we present Bibliometric and Network analysis to examine current state research on optimization in supply chain management to identify established and emergent research field for future investigation. The systematic research review which we used in our study have not grasp or assess by other researchers on this topic. Firstly, based on our methodology Bibliometric analysis began by identifying 1610 publications raised from scientific journals, included literatures from 1994 to March of 2016. Secondly, we applied PageRank algorithm in our data for citation analysis to indicate the significance of a publication.Thirdly, the topological decision variables analysis is done based on Louvain method for network data clustering, for this proposes we used the rigorous tools.Finding: Based on our Network analysis result, the optimization in supply chain management research can be divided into four clusters/modules that introduced fundamental skill, knowledge, theory, application and method.-933-Journal of Industrial Engineering and Management -http://dx.doi. org/10.3926/jiem.2035 Research limitations/implications: We presented some limitation in our research in some fields which could allow new researchers and practitioners conduct the future research to grow up in different dimensions. Practical implications:Practitioners or policy maker usually are not familiar with these type researches so this is why mush of these survey remain in theatrical and conceptual. Future investigation needs to play in practical application domain instead stop merely in opinion.Originality/value: Based on our research, the researchers have more attention to work in conceptual analysis due to other fields but we believe that in facility location problem there many remarkable rooms still exist for future research to development. We also contributed more details in the papers.

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“…In [1], the authors recommend citations to users based on the similarity between a candidate publication's in-link citation contexts and a user's input texts. Similarly, [16] integrated linkage weighting calculated from a citation graph into the content-based probabilistic weighting model to facilitate the publication retrieval. The linkage weighting model based on link frequency can substantially and stably improve the retrieval performances.…”

Section: Citation Recommendation and Contextmentioning

confidence: 99%

Full-text based context-rich heterogeneous network mining approach for citation recommendation

Liu¹,

Yu²,

Guo³

et al. 2014

IEEE/ACM Joint Conference on Digital Libraries

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Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance.

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Mining and modeling linkage information from citation context for improving biomedical literature retrieval

Cited by 16 publications

References 17 publications

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Optimization in supply chain management, the current state and future directions: A systematic review and bibliometric analysis

Full-text based context-rich heterogeneous network mining approach for citation recommendation

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