Hy-SN: Hyper-graph based semantic network

Lü, Zhen; Jiang, Zuhua

doi:10.1016/j.knosys.2010.05.005

Cited by 19 publications

(4 citation statements)

References 28 publications

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“…The TRIZ-based method allows the design of new and inventive services focusing on solving business problems with non-experiential domain background using the knowledge base [2,42]. Service blueprinting can be used to analyze the detail process to handle the business context and make sure to generate the adequate parameter of TRIZ.…”

Section: Literature Reviewmentioning

confidence: 99%

Service design for intelligent parking based on theory of inventive problem solving and service blueprint

Lee

Wang

Trappey

2015

Advanced Engineering Informatics

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Section: Literature Reviewmentioning

confidence: 99%

Service design for intelligent parking based on theory of inventive problem solving and service blueprint

Lee

Wang

Trappey

2015

Advanced Engineering Informatics

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“…6 Approaches to extracting knowledge from biological research literature to store in a hypergraph have been proposed,7 8 with similar techniques used for population stratification 9…”

Section: Biointelligence Frameworkmentioning

confidence: 99%

The BioIntelligence Framework: a new computational platform for biomedical knowledge computing

Farley

Kiefer

Lee

et al. 2013

Journal of the American Medical Informatics Association

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Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.

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“…Mining the graph would provide the user with a comprehensible network. Partitioning the hyper-graph is done using the spectral hyper-graph partitioning algorithm [65]. Further analysis of hyper-graph will lead to finding the nucleus of the domain of research.…”

Section: Graph Miningmentioning

confidence: 99%

Mining research abstracts for exploration of research communities

Mahalakshmi

Sam

Sendhilkumar

2012

Proceedings of the 5th ACM COMPUTE Conference: Intelligent &Amp; Scalable System Technologies

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Research abstracts are the 'information scents' which attract a novice researcher. To read through the entire research paper and to decide the suitability of the paper to one's research problem is a tough and abstract task. Many times, researchers do not know whether they are citing the relevant (but original?) research articles. It has been only a trial and error approach so far. To enable researchers to correctly target at the relevant and yet quality research literature, mechanisms to organise collections of research papers are essential. Though a considerable effort has been attempted earlier in this context, establishing research communities concentrated on citation based recommendations only. However, the quality and originality of research articles have not been taken into account until now. In this paper, we propose the evolution of research communities by analysing the research abstracts. We utilise Fuzzy Concept Map based approach in detecting the originality of scientific abstracts. By K-means clustering, we establish a research article hyper graph from the qualified abstracts. Later, we evolve the author clusters for every topic cluster and analyse them for redundancy. Further study on relevant bibliometrics helps us to identify a 'nucleus author' for every topic cluster.

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Hy-SN: Hyper-graph based semantic network

Cited by 19 publications

References 28 publications

Service design for intelligent parking based on theory of inventive problem solving and service blueprint

Service design for intelligent parking based on theory of inventive problem solving and service blueprint

The BioIntelligence Framework: a new computational platform for biomedical knowledge computing

Mining research abstracts for exploration of research communities

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