SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

Yumuşak, Semih; Doğdu, Erdoğan; Kodaz, Halife; Kamilaris, Andreas; Vandenbussche, Pierre-Yves

doi:10.1587/transinf.2016dap0025

Cited by 17 publications

(23 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with our idea of leveraging Web search engines to discover relevant datasets and query services, SpEnD [34] is a metacrawler designed to discover SPARQL endpoints. It first creates a list of keywords commonly found on Web pages advertising SPARQL endpoints, such as the pages of DataHub.…”

Section: Related Workmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

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

View full text Add to dashboard Cite

show abstract

“…DyLDO [25,26] 86,696 (documents) LODCloud [15] 1184 Linghub [27] 272 SPARQLES [28] 557 SpEnD [29] 1487…”

Section: (Via Sparql)mentioning

confidence: 99%

“…SPARQLES [28] (http://sparqles.ai.wu.ac.at) and SpEnD [29] (http://wis.etu.edu.tr/spend/) are services containing metadata and statistics for hundreds of SPARQL endpoints. Comparing to LODsyndesis, we mainly focus on providing measurements and services about hundreds of LOD datasets (and not for SPARQL endpoints).…”

Section: (Via Sparql)mentioning

confidence: 99%

High Performance Methods for Linked Open Data Connectivity Analytics

Mountantonakis

Tzitzikas

2018

Information

View full text Add to dashboard Cite

The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or "slices" (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.

show abstract

“…the discovery of SPARQL endpoints). In this study, the complete design of SpEnD on discovering, analyzing, and publishing Linked Data endpoints is explained in detail and we compare SpEnD repository with DataHub, LOD Cloud, SPARQLES and LODStats repositories unlike the previous study [44] which consists of describing SpEnD software and comparing SpEnD repository with only DataHub repository. In this way, we address the existing need of the Semantic Web community for a web-based search engine that is capable of discovering Linked Data endpoints and datasets in frequent intervals (e.g.…”

Section: Our Contributionmentioning

confidence: 99%

A Discovery and Analysis Engine for Semantic Web

Yumuşak

Kamilaris

Doğdu

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

Self Cite

View full text Add to dashboard Cite

The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods. CCS CONCEPTS • Information systems → Web searching and information discovery;

show abstract

SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

Cited by 17 publications

References 24 publications

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

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

High Performance Methods for Linked Open Data Connectivity Analytics

A Discovery and Analysis Engine for Semantic Web

Contact Info

Product

Resources

About