An Ontology-Driven Mediator for Querying Time-Oriented Biomedical Data

Most biomedical research databases contain considerable amounts of time-oriented data. However, temporal knowledge about the contextual meaning of such data is not usually represented in a principled fashion. As a result, investigators often develop custom techniques for temporal data analysis that are difficult to reuse. We addressed this problem by developing a set of knowledge-driven methods and tools for temporally representing and querying biomedical data, and have integrated them using a mediator approach. A central issue driving our work is a need to integrate temporal representations of data in relational databases with the domain-specific semantics of temporal patterns used in querying. This paper presents a formal temporal knowledge model using the semantic Web ontology and rule languages, OWL and SWRL, respectively. The model informs the mediator of the temporal semantics used for data analysis. We show that our approach provides the computational foundation for much-needed software to make sense of complex temporal patterns in two biomedical research domains

[1]  llsoo Ahn,et al.  Temporal Databases , 1986, Computer.

[2]  Samson W. Tu,et al.  RASTA: A Distributed Temporal Abstraction System to Facilitate Knowledge-Driven Monitoring of Clinical Databases , 2001, MedInfo.

[3]  Isaac S. Kohane,et al.  Application of Information Technology: Temporal Expressiveness in Querying a Time-stamp - based Clinical Database , 2000, J. Am. Medical Informatics Assoc..

[4]  Yuval Shahar.  Dimension of Time in Illness: An Objective View , 2000, Annals of Internal Medicine.

[5]  Susan Jones,et al.  LEGOL 2.0: A relational specification language for complex rules , 1979, Inf. Syst..

[6]  Benjamin N. Grosof,et al.  Supporting Rule System Interoperability on the Semantic Web with SWRL , 2005, SEMWEB.

[7]  Yuval Shahar,et al.  Knowledge-based temporal abstraction in clinical domains , 1996, Artif. Intell. Medicine.

[8]  Yuval Shahar,et al.  Integration of Temporal Reasoning and Temporal-Data Maintenance into a Reusable Database Mediator to Answer Abstract, Time-Oriented Queries: The Tzolkin System , 1999, Journal of Intelligent Information Systems.

[9]  M A Musen,et al.  A Temporal Query System for Protocol-Directed Decision Support , 1994, Methods of Information in Medicine.

[10]  Bryan Chan,et al.  Human immunodeficiency virus reverse transcriptase and protease sequence database , 2003, Nucleic Acids Res..

[11]  F Pinciroli,et al.  Managing Different Time Granularities of Clinical Information by an Interval-based Temporal Data Model , 1995, Methods of Information in Medicine.

[12]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[13]  Gio Wiederhold,et al.  Mediators in the architecture of future information systems , 1992, Computer.

[14]  Elaine Holmes,et al.  The challenges of modeling mammalian biocomplexity , 2004, Nature Biotechnology.

[15]  Samson W. Tu,et al.  The Chronus II temporal database mediator , 2002, AMIA.

[16]  Cynthia Brandt,et al.  Temporal query of attribute-value patient data: utilizing the constraints of clinical studies , 2003, Int. J. Medical Informatics.

[17]  Sushil Jajodia,et al.  Temporal Databases: Theory, Design, and Implementation , 1993 .

[18]  Holger Knublauch,et al.  The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications , 2004, SEMWEB.

[19]  C Combi,et al.  Temporal reasoning and temporal data maintenance in medicine: Issues and challenges , 1997, Comput. Biol. Medicine.

[20]  Mark A. Musen,et al.  SYNCHRONUS: a reusable software module for temporal integration , 2002, AMIA.

[21]  Martin J. O'Connor,et al.  Computational method for temporal pattern discovery in biomedical genomic databases , 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).

[22]  Richard T. Snodgrass,et al.  The TSQL2 Temporal Query Language , 1995 .