Mining and Matching Relationships From Interaction Contexts in a Social Manufacturing Paradigm
Abstract: There is an increasing use of social interaction contexts in the cross-enterprise cross manufacturing problem solving. To transform these massive and unstructured data into decision-support support information for cross cross-enterprise enterprise manufacturing demand-capability matching, g, we present automated solutions to two phases: (1) extracting relationships based on a semi semi-supervised supervised learning approach to derive formalized heterogeneous manufacturing network from the unstructured texttext based context that contains high levels of noise and irrelevant information and (2) matching group-level level relationships among the entities in the established manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale ale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise cross social interaction, thereby supporting the integration of the resources and capabilities among different enterpris enterprise.