An ontology driven approach for knowledge discovery in Biomedicine

The explosion of biomedical data and the growing number of disparate data sources are exposing researchers to a new challenge - how to acquire, maintain and share knowledge from large and distributed databases in the context of rapidly evolving research. This paper describes research in progress on a new methodology for leveraging the semantic content of ontologies to improve knowledge discovery in complex and dynamic domains. It aims to build a multi-dimensional ontology able to share knowledge from different experiments undertaken across aligned research communities in order to connect areas of science seemingly unrelated to the area of immediate interest. We analyze how ontologies and data mining may facilitate biomedical data analysis and present our efforts to bridge the two fields, knowledge discovery in Biomedicine, and ontology learning for successful data mining in large databases. In particular we present an initial biomedical ontology case study and how we are integrating that with a data mining environment

An ontology engineering approach for knowledge discovery from data in evolving domains

Knowledge discovery in evolving domains presents several challenges in information extraction and knowledge acquisition from heterogeneous, distributed, dynamic data sources. We define an evolving process if the process is developing, changing over time in a continuous manner. Examples of such domains include biological sciences, medical sciences, and social sciences, among others. This paper describes research in progress on a new methodology for leveraging the semantic content of ontologies to improve knowledge discovery in complex and dynamical domains. We consider in this initial stage the problem of how to acquire previous knowledge from data and then use this information in the context of ontology engineering

Ontology-guided data preparation for discovering genotype-phenotype relationships

International audienceComplexity of post-genomic data and multiplicity of mining strategies are two limits to Knowledge Discovery in Databases (KDD) in life sciences. Because they provide a semantic frame to data and because they benefit from the progress of semantic web technologies, bio-ontologies should be considered for playing a key role in the KDD process. In the frame of a case study relative to the search of genotype-phenotype relationships, we demonstrate the capability of bio-ontologies to guide data selection during the preparation step of the KDD process. We propose three scenarios to illustrate how domain knowledge can be taken into account in order to select or aggregate data to mine, and consequently how it can facilitate result interpretation at the end of the process

Building evolving ontology maps for data mining and knowledge discovery in biomedical informatics

The explosion of biomedical data and the growing number of disparate data sources are exposing researchers to a new challenge - how to acquire, maintain and share knowledge from large and distributed databases in the context of rapidly evolving research. This paper describes research in progress on a new methodology for leveraging the semantic content of ontologies to improve knowledge discovery in complex and dynamic domains. It aims to build a multi-dimensional ontology able to share knowledge from different experiments undertaken across aligned research communities in order to connect areas of science seemingly unrelated to the area of immediate interest. We analyze how ontologies and data mining may facilitate biomedical data analysis and present our efforts to bridge the two fields, knowledge discovery in databases, and ontology learning for successful data mining in large databases. In particular we present an initial biomedical ontology case study and how we are integrating that with a data mining environment