The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform

Lipika Dey - One of the best experts on this subject based on the ideXlab platform.

  • Biological relation extraction and query answering from medline abstracts using Ontology based text mining
    Data and Knowledge Engineering, 2007
    Co-Authors: Muhammad Abulaish, Lipika Dey
    Abstract:

    The rapid growth of the Biological text data repository makes it difficult for human beings to access required information in a convenient and effective manner. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured text that computers cannot interpret very easily. In this paper we have presented an Ontology-based Biological Information Extraction and Query Answering (BIEQA) System, which initiates text mining with a set of concepts stored in a Biological Ontology, and thereafter mines possible Biological relations among those concepts using NLP techniques and co-occurrence-based analysis. The system extracts all frequently occurring Biological relations among a pair of Biological concepts through text mining. A mined relation is associated to a fuzzy membership value, which is proportional to its frequency of occurrence in the corpus and is termed a fuzzy Biological relation. The fuzzy Biological relations extracted from a text corpus along with other relevant information components like Biological entities occurring within a relation, are stored in a database. The database is integrated with a query-processing module. The query-processing module has an interface, which guides users to formulate Biological queries at different levels of specificity.

  • Biological Ontology enhancement with fuzzy relations a text mining framework
    Web Intelligence, 2005
    Co-Authors: Muhammad Abulaish, Lipika Dey
    Abstract:

    Domain Ontology can help in information retrieval from documents. But Ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, Ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.

  • Web Intelligence - Biological Ontology Enhancement with Fuzzy Relations: A Text-Mining Framework
    The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 1
    Co-Authors: Muhammad Abulaish, Lipika Dey
    Abstract:

    Domain Ontology can help in information retrieval from documents. But Ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, Ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.

Muhammad Abulaish - One of the best experts on this subject based on the ideXlab platform.

  • ITNG - Relation Characterization Using Ontological Concepts
    2011 Eighth International Conference on Information Technology: New Generations, 2011
    Co-Authors: Muhammad Abulaish
    Abstract:

    This paper presents a method using the concept of AND-OR tree to characterize relations, mined from MEDLINE abstracts, using Biological Ontology concepts. A Biological relation is expressed as a binary relation associated to two molecular biology concepts as defined in the GENIA Ontology. Since a Biological relation may relate different pairs of Biological concepts and vice-versa, the strength of a relation which reflects the relative frequency of occurrence of the specific association within the corpus, is calculated and stored in the underlying ontological structure. A Biological relation along with the degree of association is termed as fuzzy Biological relation.

  • Biological relation extraction and query answering from medline abstracts using Ontology based text mining
    Data and Knowledge Engineering, 2007
    Co-Authors: Muhammad Abulaish, Lipika Dey
    Abstract:

    The rapid growth of the Biological text data repository makes it difficult for human beings to access required information in a convenient and effective manner. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured text that computers cannot interpret very easily. In this paper we have presented an Ontology-based Biological Information Extraction and Query Answering (BIEQA) System, which initiates text mining with a set of concepts stored in a Biological Ontology, and thereafter mines possible Biological relations among those concepts using NLP techniques and co-occurrence-based analysis. The system extracts all frequently occurring Biological relations among a pair of Biological concepts through text mining. A mined relation is associated to a fuzzy membership value, which is proportional to its frequency of occurrence in the corpus and is termed a fuzzy Biological relation. The fuzzy Biological relations extracted from a text corpus along with other relevant information components like Biological entities occurring within a relation, are stored in a database. The database is integrated with a query-processing module. The query-processing module has an interface, which guides users to formulate Biological queries at different levels of specificity.

  • Biological Ontology enhancement with fuzzy relations a text mining framework
    Web Intelligence, 2005
    Co-Authors: Muhammad Abulaish, Lipika Dey
    Abstract:

    Domain Ontology can help in information retrieval from documents. But Ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, Ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.

  • Web Intelligence - Biological Ontology Enhancement with Fuzzy Relations: A Text-Mining Framework
    The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 1
    Co-Authors: Muhammad Abulaish, Lipika Dey
    Abstract:

    Domain Ontology can help in information retrieval from documents. But Ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, Ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.

Christine Froidevaux - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive combination of matchers: application to the mapping of Biological ontologies for genome annotation
    2009
    Co-Authors: Bastien Rance, Jean-françois Gibrat, Christine Froidevaux
    Abstract:

    Biological ontologies are widely used for genome annotation. Identifying correspondences between concepts of two ontologies (mapping) allows the reuse and sharing of annotations. Accordingly, Biological Ontology mapping has attracted a lot of interest. In this paper, we introduce O’Browser, a semi-automatic method for mapping two functional hierarchies using two sets of carefully annotated proteins. While being based on a classical Ontology mapping architecture, O’Browser computes correspondences using a combination of different kinds of matchers. A key feature of O’Browser is that it places the expert at the center of the mapping process at two stages: (i) both to validate the very strong correspondences discovered by the system and to identify functional groups of concepts and (ii) to validate the correspondences given by the combination of results found by the matchers. These matchers have been designed in O’Browser to fit best with functional hierarchy features. For instance, we have introduced a new instance-based matcher which uses homology relationships between proteins. The combination of the different matchers is based on an original notion of adaptive weighting. Here, we show the ability of O’Browser to map concepts of Subtilist to concepts of FunCat, two functional hierarchies. First results appear to be very promising.

  • DILS - An Adaptive Combination of Matchers: Application to the Mapping of Biological Ontologies for Genome Annotation
    Lecture Notes in Computer Science, 2009
    Co-Authors: Bastien Rance, Jean-françois Gibrat, Christine Froidevaux
    Abstract:

    Biological ontologies are widely used for genome annotation. Identifying correspondences between concepts of two ontologies (mapping) allows the reuse and sharing of annotations. Accordingly, Biological Ontology mapping has attracted a lot of interest. In this paper, we introduce O'Browser, a semi-automatic method for mapping two functional hierarchies using two sets of carefully annotated proteins. While being based on a classical Ontology mapping architecture, O'Browser computes correspondences using a combination of different kinds of matchers. A key feature of O'Browser is that it places the expert at the center of the mapping process at two stages: (i) both to validate the very strong correspondences discovered by the system and to identify functional groups of concepts and (ii) to validate the correspondences given by the combination of results found by the matchers. These matchers have been designed in O'Browser to fit best with functional hierarchy features. For instance, we have introduced a new instance-based matcher which uses homology relationships between proteins. The combination of the different matchers is based on an original notion of adaptive weighting. Here, we show the ability of O'Browser to map concepts of Subtilist to concepts of FunCat, two functional hierarchies. First results appear to be very promising.

Bastien Rance - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive combination of matchers: application to the mapping of Biological ontologies for genome annotation
    2009
    Co-Authors: Bastien Rance, Jean-françois Gibrat, Christine Froidevaux
    Abstract:

    Biological ontologies are widely used for genome annotation. Identifying correspondences between concepts of two ontologies (mapping) allows the reuse and sharing of annotations. Accordingly, Biological Ontology mapping has attracted a lot of interest. In this paper, we introduce O’Browser, a semi-automatic method for mapping two functional hierarchies using two sets of carefully annotated proteins. While being based on a classical Ontology mapping architecture, O’Browser computes correspondences using a combination of different kinds of matchers. A key feature of O’Browser is that it places the expert at the center of the mapping process at two stages: (i) both to validate the very strong correspondences discovered by the system and to identify functional groups of concepts and (ii) to validate the correspondences given by the combination of results found by the matchers. These matchers have been designed in O’Browser to fit best with functional hierarchy features. For instance, we have introduced a new instance-based matcher which uses homology relationships between proteins. The combination of the different matchers is based on an original notion of adaptive weighting. Here, we show the ability of O’Browser to map concepts of Subtilist to concepts of FunCat, two functional hierarchies. First results appear to be very promising.

  • DILS - An Adaptive Combination of Matchers: Application to the Mapping of Biological Ontologies for Genome Annotation
    Lecture Notes in Computer Science, 2009
    Co-Authors: Bastien Rance, Jean-françois Gibrat, Christine Froidevaux
    Abstract:

    Biological ontologies are widely used for genome annotation. Identifying correspondences between concepts of two ontologies (mapping) allows the reuse and sharing of annotations. Accordingly, Biological Ontology mapping has attracted a lot of interest. In this paper, we introduce O'Browser, a semi-automatic method for mapping two functional hierarchies using two sets of carefully annotated proteins. While being based on a classical Ontology mapping architecture, O'Browser computes correspondences using a combination of different kinds of matchers. A key feature of O'Browser is that it places the expert at the center of the mapping process at two stages: (i) both to validate the very strong correspondences discovered by the system and to identify functional groups of concepts and (ii) to validate the correspondences given by the combination of results found by the matchers. These matchers have been designed in O'Browser to fit best with functional hierarchy features. For instance, we have introduced a new instance-based matcher which uses homology relationships between proteins. The combination of the different matchers is based on an original notion of adaptive weighting. Here, we show the ability of O'Browser to map concepts of Subtilist to concepts of FunCat, two functional hierarchies. First results appear to be very promising.

Bin Hou - One of the best experts on this subject based on the ideXlab platform.

  • A Semantic Similarity Algorithm Based on the Nearest Common Ancestor Node
    DEStech Transactions on Computer Science and Engineering, 2018
    Co-Authors: Zhe Zhang, Bin Hou
    Abstract:

    The word semantic similarity calculation is the basis of keyword search in the field of semantic web information retrieval. In view of the deficiency of semantic similarity computation in the existing algorithms, this paper discussed the influence of node depth on the semantic similarity, and presented an improved similarity calculation method based on the nearest common ancestor node, which can represent the depth of the two sememe nodes that need to compute the similarity. The simulation was carried out by using simple Biological Ontology and HowNet system architecture data, and the comparison with the original algorithm was done. The results prove the correctness and rationality of the improved method.