Semplore: A scalable IR approach to search the Web of Data

Wang, Haofen; Liu, Qiaoling; Penin, Thomas; Fu, Linyun; Zhang, Lei; Tran, Thanh; Yu, Yong; Pan, Yue

doi:10.1016/j.websem.2009.08.001

Cited by 56 publications

(21 citation statements)

References 10 publications

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“…Retrieving and combining results from search engines in the classical Web domain is not something new, and meta search engines include SavvySearch [18], Helios [19], and WebCrawler. However, on the Semantic Web domain, meta search is not currently employed on any of the existing semantic search engines [20]- [24].…”

Section: Semantic Web Crawling and Metacrawlingmentioning

confidence: 99%

SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

Yumuşak

Doğdu

Kodaz

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYLinked data endpoints are online query gateways to semantically annotated linked data sources. In order to query these data sources, SPARQL query language is used as a standard. Although a linked data endpoint (i.e. SPARQL endpoint) is a basic Web service, it provides a platform for federated online querying and data linking methods. For linked data consumers, SPARQL endpoint availability and discovery are crucial for live querying and semantic information retrieval. Current studies show that availability of linked datasets is very low, while the locations of linked data endpoints change frequently. There are linked data respsitories that collect and list the available linked data endpoints or resources. It is observed that around half of the endpoints listed in existing repositories are not accessible (temporarily or permanently offline). These endpoint URLs are shared through repository websites, such as Datahub.io, however, they are weakly maintained and revised only by their publishers. In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a "search keyword" discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, the collected search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. We analyze our findings in comparison to Datahub collection in detail.

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Section: Semantic Web Crawling and Metacrawlingmentioning

confidence: 99%

SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

Yumuşak

Doğdu

Kodaz

et al. 2017

IEICE Trans. Inf. & Syst.

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“…One way to address this limitation and take advantage of RDF data, and thus improve retrieval performance, is to combine IR approaches with inference searching based on SPARQL. Examples is this area include Sindice [19], Falcon [2], Watson [5] or SEMPLORE model [20].…”

Section: Rdf Retrievalmentioning

confidence: 99%

“…Several approaches to this problem have been presented in literature; and, the most successful models seem to be SIREN [7], based on XML indexing techniques, and SEMPLORE model [20], based on the idea of artificial documents.…”

Section: Modeling Rdf Data Using An Inverted Indexmentioning

confidence: 99%

See 1 more Smart Citation

Using BM25F for semantic search

Pérez-Agüera¹,

Arroyo²,

Greenberg³

et al. 2010

Proceedings of the 3rd International Semantic Search Workshop

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Information Retrieval (IR) approaches for semantic web search engines have become very populars in the last years. Popularization of different IR libraries, like Lucene, that allows IR implementations almost out-of-the-box have make easier IR integration in Semantic Web search engines. However, one of the most important features of Semantic Web documents is the structure, since this structure allow us to represent semantic in a machine readable format. In this paper we analyze the specific problems of structured IR and how to adapt weighting schemas for semantic document retrieval.

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“…In contrast, in our approach, the input queries are structured and the output is (possibly incomplete) sets of bindings for the query. The work on Semplore [8] is the closest to our approach. Semplore integrates structured querying techniques into a scalable IR infrastructure but uses traditional database techniques for query planning, making its performance highly dependent on query size.…”

Section: Introductionmentioning

confidence: 99%

TripleCloud: An Infrastructure for Exploratory Querying over Web-Scale RDF Data

Guéret

Kotoulas

Groth

2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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As the availability of large scale RDF data sets has grown, there has been a corresponding growth in researchers' and practitioners' interest in analyzing and investigating these data sets. However, given their size and messiness, there is significant overhead in setting up the infrastructure to store and query them. In this paper, we present TripleCloud, a system that aims to lower the entry cost to exploring Web-scale RDF data sets. The system takes advantage of existing cloud based key-value stores (e.g. BigTable, HBase) to both enable scalability as well as hide the complexities of infrastructure deployment and maintenance. It layers over these key-value stores a robust query engine able to return approximate answers. We test the scalability of the approach scaling to over 3 billion triples for complex queries. In addition to an implementation over HBase, TripleCloud runs over the Google App Engine, allowing us to perform a cost evaluation of the approach.

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Semplore: A scalable IR approach to search the Web of Data

Cited by 56 publications

References 10 publications

SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

Using BM25F for semantic search

TripleCloud: An Infrastructure for Exploratory Querying over Web-Scale RDF Data

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