摘要:
A method for calculating at least one relationship metric of a relationship between objects has the steps of providing a multi-layered relationship network with a first relationship layer derived from potential relations between objects, a second relationship layer derived from interactions between objects, a third relationship layer derived from explicit relations between objects, and an aggregated relationship layer derived from at least two layers of the relationship layers, and wherein each layer has a graph with one vertex for every object represented in the network and at least one unidirectional and/or bidirectional edge between at least two of the objects, and calculating the edge weights based on the edge weights of at least two relationship layers, selecting at least the relationship with a highest edge weight between an object and other objects, and finally outputting the weights of the selected relationships as the calculated relationship metrics.
摘要:
A method for calculating at least one relationship metric of a relationship between objects has the steps of providing a multi-layered relationship network with a first relationship layer derived from potential relations between objects, a second relationship layer derived from interactions between objects, a third relationship layer derived from explicit relations between objects, and an aggregated relationship layer derived from at least two layers of the relationship layers, and wherein each layer has a graph with one vertex for every object represented in the network and at least one unidirectional and/or bidirectional edge between at least two of the objects, and calculating the edge weights based on the edge weights of at least two relationship layers, selecting at least the relationship with a highest edge weight between an object and other objects, and finally outputting the weights of the selected relationships as the calculated relationship metrics.
摘要:
A method and apparatus for matching data network resources with an appropriate group of concepts of an ontology has the steps of, receiving a request indicating at least one expert field, providing at least one data network resource of the expert field having at least one tag and an ontology of the expert field having at least one concept, determining a minimum spanning tree of the concepts in the ontology corresponding to the tags of the data network resources and returning the concepts of the selected minimum spanning tree in response to the received request. The data network resources is matched thematically related to concepts of an ontology to the concepts of an ontology without knowing the exact terms used in the concepts and vice versa. It can be used by experts to search resources created by laymen using their expert terms without the need to know these terms.
摘要:
A semantic multilayer network is established, wherein a first layer in said semantic multilayer network including tag annotations, and a second layer in said semantic multilayer network including data structured by an ontology, are established and wherein the first layer and the second layer are connected. Thus, connecting of two entirely different worlds of the dynamic, emergent “social tagging” (e.g., web applications like Web 2.0) and of the regular “ontology engineering” (e.g. Semantic Web) data, dynamic data tag annotations and ontology, seen as too opposed or conflictive, therefore being treated separately and independently so far, becomes possible. Here, by utilizing advantages of both worlds, computer aided handling of large amounts of data, including e.g. processing, management, or querying of data, becomes considerably efficient and effective, wherein said data may be distributed in different areas, locations, or systems.
摘要:
A semantic multilayer network is established, wherein a first layer in said semantic multilayer network including tag annotations, and a second layer in said semantic multilayer network including data structured by an ontology, are established and wherein the first layer and the second layer are connected. Thus, connecting of two entirely different worlds of the dynamic, emergent “social tagging” (e.g., web applications like Web 2.0) and of the regular “ontology engineering” (e.g. Semantic Web) data, dynamic data tag annotations and ontology, seen as too opposed or conflictive, therefore being treated separately and independently so far, becomes possible. Here, by utilizing advantages of both worlds, computer aided handling of large amounts of data, including e.g. processing, management, or querying of data, becomes considerably efficient and effective, wherein said data may be distributed in different areas, locations, or systems.