UMBC ebiquity
Cluster-based Instance Consolidation For Subsequent Matching
Monday, October 15, 2012, 11:00am - Monday, October 15, 2012, 12:00pm
ITE 325 - B
In this week's lab meeting Jennifer Sleaman will present "Cluster-based Instance Consolidation For Subsequent Matching"
Instance consolidation is a way to merge instances that are thought to be the same or closely related that can be used to support coreference resolution and entity linking. For Semantic Web data, consolidating instances can be as simple as relating instances using owl:sameAs, as is the case in linked data, or merging instances that could then be used to populate or enrich a knowledge model. In many applications, systems process data incrementally over time and as new data is processed, the state of the knowledge model changes. Previous consolidations could prove to be incorrect. Consequently, a more abstract representation is needed to support instance consolidation. We describe our current research to perform consolidation that includes temporal support, support to resolve conflicts and an abstract representation of an instance that is the aggregate of a cluster of matched instances. We believe that this model will prove flexible enough to handle sparse instance data and can improve the accuracy of the knowledge model over time.
Instance consolidation is a way to merge instances that are thought to be the same or closely related that can be used to support coreference resolution and entity linking. For Semantic Web data, consolidating instances can be as simple as relating instances using owl:sameAs, as is the case in linked data, or merging instances that could then be used to populate or enrich a knowledge model. In many applications, systems process data incrementally over time and as new data is processed, the state of the knowledge model changes. Previous consolidations could prove to be incorrect. Consequently, a more abstract representation is needed to support instance consolidation. We describe our current research to perform consolidation that includes temporal support, support to resolve conflicts and an abstract representation of an instance that is the aggregate of a cluster of matched instances. We believe that this model will prove flexible enough to handle sparse instance data and can improve the accuracy of the knowledge model over time.