%0 Conference Proceedings
%T Commonsense Reasoning Using Theorem Proving and Machine Learning
%+ Harz University of Applied Sciences
%+ Universität Koblenz-Landau [Koblenz]
%A Siebert, Sophie
%A Schon, Claudia
%A Stolzenburg, Frieder
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 3rd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Canterbury, United Kingdom
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-11713
%P 395-413
%8 2019-08-26
%D 2019
%R 10.1007/978-3-030-29726-8_25
%K Commonsense reasoning
%K Causal reasoning
%K Machine learning
%K Theorem proving
%K Large background knowledge
%Z Computer Science [cs]Conference papers
%X Commonsense reasoning is a difficult task for a computer to handle. Current algorithms score around 80% on benchmarks. Usually these approaches use machine learning which lacks explainability, however. Therefore, we propose a combination with automated theorem proving here. Automated theorem proving allows us to derive new knowledge in an explainable way, but suffers from the inevitable incompleteness of existing background knowledge. We alleviate this problem by using machine learning. In this paper, we present our approach which uses an automatic theorem prover, large existing ontologies with background knowledge, and machine learning. We present first experimental results and identify an insufficient amount of training data and lack of background knowledge as causes for our system not to stand out much from the baseline.
%G English
%Z TC 5
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-02520044/document
%2 https://inria.hal.science/hal-02520044/file/485369_1_En_25_Chapter.pdf
%L hal-02520044
%U https://inria.hal.science/hal-02520044
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-11713