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Erschienen in:
Formal Concept Analysis and Extensions for Complex Data Analytics Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

2. Conceptual Navigation in Large Knowledge Graphs

verfasst von : Sébastien Ferré

Erschienen in: Complex Data Analytics with Formal Concept Analysis

Verlag: Springer International Publishing

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Abstract

A growing part of Big Data is made of knowledge graphs. Major knowledge graphs such as Wikidata, DBpedia or the Google Knowledge Graph count millions of entities and billions of semantic links. A major challenge is to enable their exploration and querying by end-users. The SPARQL query language is powerful but provides no support for exploration by end-users. Question answering is user-friendly but is limited in expressivity and reliability. Navigation in concept lattices supports exploration but is limited in expressivity and scalability. In this paper, we introduce a new exploration and querying paradigm, Abstract Conceptual Navigation (ACN), that merges querying and navigation in order to reconcile expressivity, usability, and scalability. ACN is founded on Formal Concept Analysis (FCA) by defining the navigation space as a concept lattice. We then instantiate the ACN paradigm to knowledge graphs (Graph-ACN) by relying on Graph-FCA, an extension of FCA to knowledge graphs. We continue by detailing how Graph-ACN can be efficiently implemented on top of SPARQL endpoints, and how its expressivity can be increased in a modular way. Finally, we present a concrete implementation available online, Sparklis, and a few application cases on large knowledge graphs.

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Vorheriges Kapitel Formal Concept Analysis and Extensions for Complex Data Analytics
Nächstes Kapitel FCA2VEC: Embedding Techniques for Formal Concept Analysis
Fußnoten
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Metadaten
Titel
Conceptual Navigation in Large Knowledge Graphs
verfasst von
Sébastien Ferré
Copyright-Jahr
2022
Buch
Complex Data Analytics with Formal Concept Analysis

Print ISBN: 978-3-030-93277-0

Electronic ISBN: 978-3-030-93278-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-93278-7_2