Knowledge Representation Issues in Semantic Graphs for Relationship Detection

Biodefense Knowledge Center, Lawrence Livermore National Laboratory.An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the de-tection of relationships between seemingly disjoint entities. A structure used for this task is a semantic graph,also known as a relational data graph or an attributed relational graph. These graphs encode relationships astyped links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks usedin AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore,each node has a set of attributes associated with it (e.g., “age” may be an attribute of a node of type “person”).Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and issomewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operateson semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and providesome possible solutions using recently developed ideas in the field of complex networks. In particular, we usethe concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting rela-tionships. We also propose new statistical measures for semantic graphs and illustrate these semantic measureson graphs constructed from movies and terrorism data.I. INTRODUCTION