|Title||On-demand Creation of Focused Domain Models using Top-down and Bottom-up Information Extraction|
|Year of Publication||2012|
|Authors||Christopher Thomas, Pankaj Mehra, Amit Sheth, Wenbo Wang|
|Keywords||domain model creation, Knowledge Management, Linked Open Data, Pattern-Based Information Extraction|
We present a hybrid method for automated on-demand creation of conceptual models of domain-specific knowledge. Models are thereby created using a two-step process of Domain Definition and Domain Description. Domain Definition creates a conceptual base whereas in the Domain Description relationships are added to the conceptual model using a pattern-based relational-targeting Information Extraction algorithm that deploys a novel relational pertinence measure to disambiguate semantically overlapping types of relationships. The two-step process has the advantage over traditional approaches to ontology learning that it provides conceptual grounding through a top-down extraction and over information extraction that the extraction operates on a conceptual level so that concept integrity and reference are guaranteed. At the core of the extraction algorithm is a novel measure for semantic overlap of relationships that allows the extraction of multiple intentionally similar relationships while disambiguating merely extensionally similar relationships. The envisioned use of the created models is primarily in Information Retrieval applications, but the models can also serve as starting points for formal ontologies in Knowledge Representation applications. We detail the techniques involved in domain definition and description as well as evaluate the outcomes in depth using qualitative and quantitative evaluation metrics.