Commonsense knowledge is paramount to enable intelligent systems. Typically, it is characterized as being implicit and ambiguous, hindering thereby the automation of its acquisition. To address these challenges, this paper presents semantically enhanced models to enable reasoning through resolving part of commonsense ambiguity. The proposed models enhance in a knowledge graph embedding (KGE) framework for knowledge base completion. Experimental results show the effectiveness of the new semantic models in commonsense reasoning.
Earliest efforts for commonsense knowledge acquisition have relied on manual annotation by system experts to formalize and codify valid assertions including Cyc @cite_8 , SUMO ontology @cite_33 and Open Mind Common Sense (OMCS) @cite_40 . To increase the efficiency of manual knowledge gathering, researchers resorted to collective efforts through public platforms such as crowd-sourcing websites and games with purpose @cite_21 .
@cite_40 @cite_21 @cite_33 @cite_8
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Open Mind Common Sense is a knowledge acquisition system designed to acquire commonsense knowledge from the general public over the web. We describe and evaluate our first fielded system, which enabled the construction of a 450,000 assertion commonsense knowledge base. We then discuss how our second-generation system addresses weaknesses discovered in the first. The new system acquires facts, descriptions, and stories by allowing participants to construct and fill in natural language templates. It employs word-sense disambiguation and methods of clarifying entered knowledge, analogical inference to provide feedback, and allows participants to validate knowledge and in turn each other. We address the problem of collecting a database of ""common-sense facts"" using a computer game. Informally, a common-sense fact is a true statement about the world that is known to most humans: ""milk is white,"" ""touching hot met al hurts,"" etc. Several efforts have been devoted to collecting common-sense knowledge for the purpose of making computer programs more intelligent. Such efforts, however, have not succeeded in amassing enough data because the manual process of entering these facts is tedious. We therefore introduce Verbosity, a novel interactive system in the form of an enjoyable game. People play Verbosity because it is fun, and as a side effect of them playing, we collect accurate common-sense knowledge. Verbosity is an example of a game that not only brings people together for leisure, but also collects useful data for computer science. The Suggested Upper Merged Ontology (SUMO) is an upper levelontology that has been proposed as a starter document for TheStandard Upper Ontology Working Group, an IEEE-sanctioned workinggroup of collaborators from the fields of engineering, philosophy,and information science. The SUMO provides definitions forgeneral-purpose terms and acts as a foundation for more specificdomain ontologies. In this paper we outline the strategy used tocreate the current version of the SUMO, discuss some of thechallenges that we faced in constructing the ontology, and describein detail its most general concepts and the relations between them. Since 1984, a person-century of effort has gone into building CYC, a universal schema of roughly 10 5 general concepts spanning human reality. Most of the time has been spent codifying knowledge about these concepts; approximately 10 6 commonsense axioms have been handcrafted for and entered into CYC's knowledge base, and millions more have been inferred and cached by CYC. This article examines the fundamental assumptions of doing such a large-scale project, reviews the technical lessons learned by the developers, and surveys the range of applications that are or soon will be enabled by the technology.