Situation-based ontologies for a computational framework for identity focusing on crime scenes

Abstract

We are interested in how evidence in a case fits together to support a judgment about the identity of an agent. We present a computational framework that extends to the cyber world although our current work focuses on physical evidence from a crime scene. We take Barwise's situation theory as a foundation. Situations support items of information and, by virtue of constraints, some carry information about other situations. In particular, an utterance situation carries information about a described situation. We provide an account of the support for an identity judgment (in an utterance situation called an id-situation) that looks at building a case (called an id-case), like a legal case, since identity cases can lead to multiple situations that impact the value of our evidence. We have developed a novel situation ontology on which we built an id-situation ontology. To capture our current focus, we developed a physical biometrics ontology, a law enforcement ontology, and several supporting stubs. We show how a case can be encoded in the RDF in conformance with our ontologies. We complement our id-situation ontology with SWRL rules to infer the agent in a crime scene and to classify situations and id-cases. Combining possibly conflicting evidence is handled with Dempster-Shafer theory, as reported elsewhere.