Explicit scientific knowledge comparison based on semantic description matching

Researchers begin new research by acquiring pre-existing explicit scientific knowledge that is potentially relevant to the research subject. In order to find some potentially relevant explicit scientific knowledge items, such as knowledge whose content is similar to the targeted research, a researcher must examine the semantics of each item. In this paper, after reviewing related work, an automated semantic description matching-based approach is presented for comparing items of explicit scientific knowledge. This approach obtains a matching score between semantic descriptions of two items of explicit scientific knowledge that indicates their similarity. Three dimensions are considered in this approach: matching granularity, similarity scale for instance classes, and logic similarity scale. In order to match two semantic descriptions, a six-step method is presented: creation of atomic queries, generalization of query classes, generalization of query properties, addition of rules, creation of instances implied by complex class definition, and semi-automatic pruning of matching results. Finally, some conclusions regarding the approach are presented together with plans for future work.