research-article

Generalized graph pattern discovery in linked data with data properties and a domain ontology

Authors:

Victor Fuentes,

Petko Valtchev,

Abdoulaye Baniré Diallo,

René LacroixAuthors Info & Claims

SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

Pages 1890 - 1899

https://doi.org/10.1145/3477314.3507301

Published: 06 May 2022 Publication History

Abstract

Nowadays, in many practical situations, analytical tasks need to be performed on complex heterogeneous data, often described by a domain ontology (DO). Such cases abound in life science fields such as agro-informatics, where observations and measures on animals/plants are logged for subsequent mining. The data is naturally structured as graph(s), unlabelled and missing some values, hence it fits well pattern mining. In our own precision farming project aimed at decision support for dairy cow management, we mine for knowledge in milk production data. In one task, we aim at contrast patterns explaining the relative impact of independent production factors. To that end, ontologically-generalized graph patterns (OGPs), a variety of generalized graph patterns, where vertices and edges are labelled by DO classes and properties, respectively, were defined. A mining methodology was also designed that reconciles OWL DOs, abstraction from RDF graphs and literals in data. To address the well-known cost-related limitations of graph mining -exacerbated here by class/property specializations and data properties- we split the mining task into (1) mining of generic object property topology patterns and (2) label refinement. Those focus on two sorts of OGPs, called topologies and class stars, respectively, which, after being mined separately, get (3) assembled into fully-fledged OGPs.

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Cited By

Palme RWelke P(2023)Frequent Generalized Subgraph Mining via Graph Edit DistancesMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-23633-4_32(477-483)Online publication date: 31-Jan-2023
https://doi.org/10.1007/978-3-031-23633-4_32

Index Terms

Generalized graph pattern discovery in linked data with data properties and a domain ontology
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Decision support systems
  2. World Wide Web
    1. Web data description languages
      1. Semantic web description languages

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cover image ACM Conferences

SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

April 2022

2099 pages

ISBN:9781450387132

DOI:10.1145/3477314

Conference Chairs:
Jiman Hong
Soongsil University
,
Miroslav Bures
Czech Technical University, Czechia
,
Program Chairs:
Juw Won Park
University of Louisville
,
Tomas Cerny
Baylor University

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Published: 06 May 2022

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SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing

April 25 - 29, 2022

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Cited By

Palme RWelke P(2023)Frequent Generalized Subgraph Mining via Graph Edit DistancesMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-23633-4_32(477-483)Online publication date: 31-Jan-2023
https://doi.org/10.1007/978-3-031-23633-4_32

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