Browsing Linked Data Catalogs with LODAtlas

Pietriga, Emmanuel; Gözükan, Hande; Appert, Caroline; Destandau, Marie; Čebirić, Šejla; Goasdoué, François; Manolescu, Ioana

doi:10.1007/978-3-030-00668-6_9

Cited by 23 publications

(21 citation statements)

References 28 publications

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“…or "How many vertices have outgoing edges labeled title, author, and abstract?". Other applications of structural graph summaries include cardinality computations for queries on graphs [21], data exploration [1,20,22,25], data visualization [10], vocabulary term recommendations [24], and related entity retrieval [8]. Next, we describe the tasks of data search and cardinality computation.…”

Section: Introductionmentioning

confidence: 99%

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Graph summarization is the task of finding condensed representations of graphs such that a chosen set of (structural) subgraph features in the graph summary are equivalent to the input graph. Existing graph summarization algorithms are tailored to specific graph summary models, only support one-time batch computation, are designed and implemented for a specific task, or evaluated using static graphs. Our novel, incremental, parallel algorithm addresses all these shortcomings. We support various structural graph summary models defined in our formal language FLUID. All graph summaries defined with FLUID can be updated in time O(∆ •d k), where ∆ is the number of additions, deletions, and modifications to the input graph, d is its maximum degree, and k is the maximum distance in the subgraphs considered. We empirically evaluate the performance of our algorithm on benchmark and real-world datasets. Our experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms their batch counterpart, even when about 50% of the graph database changes. The source code and the experimental results are openly available for reproducibility and extensibility.

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Section: Introductionmentioning

confidence: 99%

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…The Linked Open Data (LOD) cloud accumulates more and more triplestores, which are themselves more and more voluminous. Several statistical indicators have been proposed to monitor the content and the quality of the data: Mapping methods [3,11,29] provide statistical indicators to summarize the property and class usage and the links between them. Other indicators evaluate the completeness of the data [16,24] or the representativeness of the properties [34].…”

Section: Introductionmentioning

confidence: 99%

Anytime Large-Scale Analytics of Linked Open Data

Soulet

Suchanek

2019

Lecture Notes in Computer Science

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Analytical queries are queries with numerical aggregators: computing the average number of objects per property, identifying the most frequent subjects, etc. Such queries are essential to monitor the quality and the content of the Linked Open Data (LOD) cloud. Many analytical queries cannot be executed directly on the SPARQL endpoints, because the fair use policy cuts off expensive queries. In this paper, we show how to rewrite such queries into a set of queries that each satisfy the fair use policy. We then show how to execute these queries in such a way that the result provably converges to the exact query answer. Our algorithm is an anytime algorithm, meaning that it can give intermediate approximate results at any time point. Our experiments show that the approach converges rapidly towards the exact solution, and that it can compute even complex indicators at the scale of the LOD cloud.

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“…Some portals specialize in specific dataset formats or interface technologies. For instance, ProgrammableWeb.com 3 registers Web APIs, a loosely defined category of lightweight Web services also referred to as REST-like or Lo-REST [25] services, while LODAtlas [26] and SPARQLES [31] focus on RDF datasets and SPARQL endpoints respectively.…”

Section: Introductionmentioning

confidence: 99%

Enabling Automatic Discovery and Querying of Web APIs at Web Scale using Linked Data Standards

Michel

Faron-Zucker

Corby

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

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To help in making sense of the ever-increasing number of data sources available on the Web, in this article we tackle the problem of enabling automatic discovery and querying of data sources at Web scale. To pursue this goal, we suggest to (1) provision rich descriptions of data sources and query services thereof, (2) leverage the power of Web search engines to discover data sources, and (3) rely on simple, well-adopted standards that come with extensive tooling. We apply these principles to the concrete case of SPARQL micro-services that aim at querying Web APIs using SPARQL. The proposed solution leverages SPARQL Service Description, SHACL, DCAT, VoID, Schema.org and Hydra to express a rich functional description that allows a software agent to decide whether a microservice can help in carrying out a certain task. This description can be dynamically transformed into a Web page embedding rich markup data. This Web page is both a human-friendly documentation and a machine-readable description that makes it possible for humans and machines alike to discover and invoke SPARQL microservices at Web scale, as if they were just another data source. We report on a prototype implementation that is available on-line for test purposes, and that can be effectively discovered using Google's Dataset Search engine.

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Browsing Linked Data Catalogs with LODAtlas

Cited by 23 publications

References 28 publications

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Anytime Large-Scale Analytics of Linked Open Data

Enabling Automatic Discovery and Querying of Web APIs at Web Scale using Linked Data Standards

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