| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 102 === Enabling commonsense reasoning is an important task for intelligent agents. The very first requirement of commonsense reasoning problem is a large knowledge base containing basic facts and the semantic relations between these facts. As a knowledge base gradually becomes larger, knowledge management becomes more complex. Rather than requiring all semantic relations between concepts are present in a semantic network, a system which is provided a basic set of facts and an inference mechanism can easily discover new relationships and likewise infer new knowledge. This study attempts to address the need of such an inference mechanism and to present an inference method for this purpose.
In this study, I offer another perspective to formalize commonsense reasoning problem in terms of semantic graph walk problem. Considering the knowledge base is encoded as a semantic hypergraph, reasoning can be formulated as a process to discover hidden relations between concepts. Likewise, semantic graph walk formalization allows the reasoning agents not to require all semantic relations between the concepts are present in their knowledge base and to be able to infer new knowledge by composing semantic relations in a graph walk manner.
Although this study focuses on a specific domain, emph{i.e.} commonsense semantic networks, the composition mechanism encapsulates a generic method for so-called digraphs. Because each specific link is represented by a unique vector, the method can be generalized for all type of hypergraphs for discovering new links. An analysis of the experiments for semantic graph walk inference on top of ConceptNet 5 commonsense knowledge base demonstrates that there are strong heuristics that can be provided by relation composition method in terms of reasoning. The study also evaluates the shortcomings of ConceptNet definition of semantic networks as well as a few novel directions that semantic graph walk inference method might be used in the future.
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