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Dynamic generation of concepts hierarchies for knowledge discovering in bio-medical linked data sets

Published: 20 February 2012 Publication History

Abstract

Since most bio-medical Linked Data Sets are simply extracted from the Relational database, lots of them are lack of ontology or concept hierarchy structure for user better understanding the data sets. This problem also limited usage of bio-medical Linked Data Sets. To resolve the problem, this paper introduced a method to dynamically generate the concept hierarchy from the Linked Data Sets. Based on the hierarchical clustering algorithm, we applied Vector Space Model(VSM) and Jaccard's Coefficient(JC) to formalize the hierarchy structure after pre-processing data. We implemented our method using two Linked Data Sets: DrugBank and Diseasome from Linked Life Data and evaluated performance with the gold standard.

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cover image ACM Conferences
ICUIMC '12: Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
February 2012
852 pages
ISBN:9781450311724
DOI:10.1145/2184751
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 February 2012

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  1. bio-medical linked data sets
  2. concept hierarchy
  3. knowledge discovering

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  • (2021)A survey on semantic schema discoveryThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00717-x31:4(675-710)Online publication date: 27-Nov-2021
  • (2021)OntoCSM: Ontology-Aware Characteristic Set Merging for RDF Type DiscoveryDatabase Systems for Advanced Applications10.1007/978-3-030-73194-6_22(323-339)Online publication date: 11-Apr-2021
  • (2018)Clustering of Propositions Equipped with UncertaintyInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications10.1007/978-3-319-91479-4_59(715-726)Online publication date: 18-May-2018
  • (2017)Constructing faceted taxonomy for heterogeneous entities based on object properties in linked dataData & Knowledge Engineering10.1016/j.datak.2017.09.006112:C(79-93)Online publication date: 1-Nov-2017
  • (2015)Structure Inference for Linked Data Sources Using ClusteringTransactions on Large-Scale Data- and Knowledge-Centered Systems XIX10.1007/978-3-662-46562-2_1(1-25)Online publication date: 24-Feb-2015
  • (2015)Schema Discovery in RDF Data SourcesConceptual Modeling10.1007/978-3-319-25264-3_36(481-495)Online publication date: 8-Dec-2015
  • (2014)Modelling of Experienced-Based Data in Linked Data EnvironmentProceedings of the 2014 International Conference on Intelligent Networking and Collaborative Systems10.1109/INCoS.2014.122(731-736)Online publication date: 10-Sep-2014
  • (2014)A Linked Data Based Decision Support System for Cancer TreatmentProceedings of the 2014 Enterprise Systems Conference10.1109/ES.2014.15(39-44)Online publication date: 2-Aug-2014
  • (2014)A Comparison of Unsupervised Taxonomical Relationship Induction Approaches for Building Ontology in RDF ResourcesSemantic Technology10.1007/978-3-319-14122-0_32(445-459)Online publication date: 21-May-2014
  • (2014)Learning Categories from Linked Open DataInformation Processing and Management of Uncertainty in Knowledge-Based Systems10.1007/978-3-319-08852-5_41(396-405)Online publication date: 2014
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