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Commonsense reasoning using path analysis on semantic networks

  • Commonsense reasoning can be seen as a process of identifying dependencies amongst events and actions. Understanding the circumstances surrounding these events requires background knowledge with sufficient breadth to cover a wide variety of domains. In the recent decades, there has been a lot of work in extracting commonsense knowledge, a number of these projects provide their collected data as semantic networks such as ConceptNet and CausalNet. In this thesis, we attempt to undertake the Choice Of Plausible Alternatives (COPA) challenge, a problem set with 1000 questions written in multiple-choice format with a premise and two alternative choices for each question. Our approach differs from previous work by using shortest paths between concepts in a causal graph with the edge weight as causality metric. We use CausalNet as primary network and implement a few design choices to explore the strengths and drawbacks of this approach, and propose an extension using ConceptNet by leveraging its commonsense knowledge base.

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  • Master's Thesis for the completion of my studies in Web Science. Subject: Commonsense reasoning using path analysis on semantic networks

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Author:Adam Mtarji
Referee:Steffen Staab
Advisor:Claudia Schon, Steffen Staab
Document Type:Master's Thesis
Date of completion:2019/10/21
Date of publication:2019/10/21
Publishing institution:Universität Koblenz, Universitätsbibliothek
Granting institution:Universität Koblenz, Fachbereich 4
Date of final exam:2019/10/21
Release Date:2019/10/21
Number of pages:ix, 38
Institutes:Fachbereich 4 / Institute for Web Science and Technologies
BKL-Classification:54 Informatik
Licence (German):License LogoEs gilt das deutsche Urheberrecht: § 53 UrhG