Knowledge graph embedding models for automatic commonsense knowledge acquisition

Intelligent systems are expected to make smart human-like decisions based on accumulated commonsense knowledge of an average individual. These systems need, therefore, to acquire an understanding about uses of objects, their properties, parts and materials, preconditions and effects of actions, and many other forms of rather implicit shared knowledge. Formalizing and collecting commonsense knowledge has, thus, been an long-standing challenge for artificial intelligence research community. The availability of massive amounts of multimodal data in the web accompanied with the advancement of information extraction and machine learning together with the increase in computational power made the automation of commonsense knowledge acquisition more feasible than ever. Reasoning models perform automatic knowledge acquisition by making rough guesses of valid assertions based on analogical similarities. A recent successful family of reasoning models termed knowledge graph embedding convert knowledge graph entities and relations into compact k-dimensional vectors that encode their global and local structural and semantic information. These models have shown outstanding performance on predicting factual assertions in encyclopedic knowledge bases, however, in their current form, they are unable to deal commonsense knowledge acquisition. Unlike encyclopedic knowledge, commonsense knowledge is concerned with abstract concepts which can have multiple meanings, can be expressed in various forms, and can be dropped from textual communication. Therefore, knowledge graph embedding models fall short of encoding the structural and semantic information associated with these concepts and subsequently, under-perform in commonsense knowledge acquisition task. The goal of this research is to investigate semantically enhanced knowledge graph embedding models tailored to deal with the special challenges imposed by commonsense knowledge. The research presented in this report draws on the idea that providing knowledge graph embedding models with salient and focused semantic context of concepts and relations would result in enhanced vectors representations that can be effective for automatically enriching commonsense knowledge bases with new assertions.Master of Engineerin