On-Demand Creation of Focused Domain Models using Top-down and Bottom-up 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. 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 intensionally 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