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.
Author: | Adam Mtarji |
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URN: | urn:nbn:de:kola-19767 |
Referee: | Steffen Staab |
Advisor: | Claudia Schon, Steffen Staab |
Document Type: | Master's Thesis |
Language: | English |
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): | Es gilt das deutsche Urheberrecht: § 53 UrhG |