Utilizing Knowledge-Base Semantics in Graph-Based Algorithms

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Knowledge Bases & Context

Graph-based algorithms convert a knowledge base with a graph structure into one with a tree structure (a join-tree) and then apply tree-inference on the result. Nodes in the join-tree are cliques of variables and tree-inference is exponential in w*, the size of the maximal clique in the join-tree. A central property of join-trees that validates tree-inference is the running-intersection property: the intersection of any two cliques must belong to every clique on the path between them. We present two key results in connection to graph-based algorithms. First, we show that the running-intersection property, although sufficient, is not necessary for validating tree-inference. We present a weaker property for this purpose, called running-interaction, that depends on non-structural (semantical) properties of a knowledge base. We also present a linear algorithm that may reduce w* of a join-tree, possibly destroying its running-intersection property, while maintaining its running-interaction property and, hence, its validity for tree-inference. Second, we develop a simple algorithm for generating trees satisfying the running-interaction property. The algorithm bypasses triangulation (the standard technique for constructing join-trees) and does not construct a join-tree first. We show that the proposed algorithm may in some cases generate trees that are more efficient than those generated by modifying a join-tree.