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Summarizing semantic graphs: a survey

Authors:

Šejla Čebirić,

François Goasdoué,

Haridimos Kondylakis,

Dimitris Kotzinos,

Ioana Manolescu,

Georgia Troullinou, and

Mussab ZneikaAuthors Info & Claims

The VLDB Journal — The International Journal on Very Large Data Bases, Volume 28, Issue 3

Pages 295 - 327

https://doi.org/10.1007/s00778-018-0528-3

Published: 01 June 2019 Publication History

Abstract

The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area and identify the most pertinent summarization method for a variety of usage scenarios.

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Cited By

Chu DZhang FZhang WZhang YLin X(2024)Graph Summarization: Compactness Meets EfficiencyProceedings of the ACM on Management of Data10.1145/36549432:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654943
Aimonier-Davat JNédelec BDang MMolli PSkaf-Molli HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)FedUP: Querying Large-Scale Federations of SPARQL EndpointsProceedings of the ACM on Web Conference 202410.1145/3589334.3645704(2315-2324)Online publication date: 13-May-2024
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El Husseini WEl Vaigh CGoasdoué FJaudoin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Query Optimization for Ontology-Mediated Query AnsweringProceedings of the ACM on Web Conference 202410.1145/3589334.3645567(2138-2148)Online publication date: 13-May-2024
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cover image The VLDB Journal — The International Journal on Very Large Data Bases

The VLDB Journal — The International Journal on Very Large Data Bases Volume 28, Issue 3

June 2019

131 pages

ISSN:1066-8888

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Copyright © Copyright © 2019 Springer-Verlag GmbH Germany, part of Springer Nature.

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Berlin, Heidelberg

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Published: 01 June 2019

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Chu DZhang FZhang WZhang YLin X(2024)Graph Summarization: Compactness Meets EfficiencyProceedings of the ACM on Management of Data10.1145/36549432:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654943
Aimonier-Davat JNédelec BDang MMolli PSkaf-Molli HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)FedUP: Querying Large-Scale Federations of SPARQL EndpointsProceedings of the ACM on Web Conference 202410.1145/3589334.3645704(2315-2324)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645704
El Husseini WEl Vaigh CGoasdoué FJaudoin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Query Optimization for Ontology-Mediated Query AnsweringProceedings of the ACM on Web Conference 202410.1145/3589334.3645567(2138-2148)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645567
Barile Rd’Amato CFanizzi N(2024)Explanation of Link Predictions on Knowledge Graphs via Levelwise Filtering and Graph SummarizationThe Semantic Web10.1007/978-3-031-60626-7_10(180-198)Online publication date: 26-May-2024
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Rabbani KLissandrini MHose K(2023)Extraction of Validating Shapes from Very Large Knowledge GraphsProceedings of the VLDB Endowment10.14778/3579075.357907816:5(1023-1032)Online publication date: 6-Mar-2023
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Paton NChen JWu Z(2023)Dataset Discovery and Exploration: A SurveyACM Computing Surveys10.1145/362652156:4(1-37)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3626521
Diop LMarkhoff BSoulet A(2023)TTProfiler: Types and Terms Profile Building for Online Cultural Heritage Knowledge GraphsJournal on Computing and Cultural Heritage 10.1145/358844316:3(1-22)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3588443
Hubert NPaulheim HMonnin PBrun AMonticolo D(2023)Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInEProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627550(188-196)Online publication date: 5-Dec-2023
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Belhajjame KMejri M(2023)Online maintenance of evolving knowledge graphs with RDFS-based saturation and why-provenance supportWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2023.10079678:COnline publication date: 18-Oct-2023
https://dl.acm.org/doi/10.1016/j.websem.2023.100796
Chen QHuang ZZhang ZLuo WLin TShi QCheng G(2023)Dense Re-Ranking with Weak Supervision for RDF Dataset SearchThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_2(23-40)Online publication date: 6-Nov-2023
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