Automatically Discovering Conceptual Neighborhoods Using Machine Learning Methods

Ling Cai*, Krzysztof Janowicz, Rui Zhu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)

Abstract

Qualitative spatio-temporal reasoning (QSTR) plays a key role in spatial cognition and artificial intelligence (AI) research. In the past, research and applications of QSTR have often taken place in the context of declarative forms of knowledge representation. For instance, conceptual neighborhoods (CN) and composition tables (CT) of relations are introduced explicitly and utilized for spatial/temporal reasoning. Orthogonal to this line of study, we focus on bottom-up machine learning (ML) approaches to investigate QSTR. More specifically, we are interested in questions of whether similarities between qualitative relations can be learned from data purely based on ML models, and, if so, how these models differ from the ones studied by traditional approaches. To achieve this, we propose a graph-based approach to examine the similarity of relations by analyzing trained ML models. Using various experiments on synthetic data, we demonstrate that the relationships discovered by ML models are well-aligned with CN structures introduced in the (theoretical) literature, for both spatial and temporal reasoning. Noticeably, even with significantly limited qualitative information for training, ML models are still able to automatically construct neighborhood structures. Moreover, patterns of asymmetric similarities between relations are disclosed using such a data-driven approach. To the best of our knowledge, our work is the first to automatically discover CNs without any domain knowledge. Our results can be applied to discovering CNs of any set of jointly exhaustive and pairwise disjoint (JEPD) relations.

Original languageEnglish
Title of host publication15th International Conference on Spatial Information Theory, COSIT 2022
EditorsToru Ishikawa, Sara Irina Fabrikant, Stephan Winter
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959772570
DOIs
Publication statusPublished - 22 Aug 2022
Event15th International Conference on Spatial Information Theory, COSIT 2022 - Kobe, Japan
Duration: 5 Sept 20229 Sept 2022

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume240
ISSN (Print)1868-8969

Conference

Conference15th International Conference on Spatial Information Theory, COSIT 2022
Country/TerritoryJapan
CityKobe
Period5/09/229/09/22

Bibliographical note

Funding Information:
Funded by the National Science Foundation – OIA (2033521).

Publisher Copyright:
© 2022 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

Keywords

  • Conceptual Neighborhood
  • Knowledge Discovery
  • Machine Learning
  • Qualitative Spatial Reasoning
  • Qualitative Temporal Reasoning

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