article

Searching and browsing Linked Data with SWSE: The Semantic Web Search Engine

Authors:

Jürgen Umbrich,

Sheila Kinsella,

Axel Polleres, and

Stefan DeckerAuthors Info & Claims

Web Semantics: Science, Services and Agents on the World Wide Web, Volume 9, Issue 4

Pages 365 - 401

https://doi.org/10.1016/j.websem.2011.06.004

Published: 01 December 2011 Publication History

Abstract

In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data - loosely also known as Linked Data - which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web - in terms of scale, unreliability, inconsistency and noise - are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a best-effort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.

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Index Terms

Searching and browsing Linked Data with SWSE: The Semantic Web Search Engine
1. Information systems
  1. Information retrieval

Index terms have been assigned to the content through auto-classification.

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LOD search engine: A semantic search over linked data
Abstract
In the last few years, there has been a significant growth in the amount of data published in RDF and adoption of Linked Data principles. Every day, a large number of people and communities contribute to the publication of datasets as Linked Data ...
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cover image Web Semantics: Science, Services and Agents on the World Wide Web

Web Semantics: Science, Services and Agents on the World Wide Web Volume 9, Issue 4

December, 2011

158 pages

ISSN:1570-8268

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Copyright © Elsevier B.V. © 2011.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2011

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Shoaib UFiaz LChakraborty CRauf H(2023)Context-aware Urdu Information Retrieval SystemACM Transactions on Asian and Low-Resource Language Information Processing10.1145/350285422:3(1-19)Online publication date: 2-Apr-2023
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Sharma PYadav DThakur R(2022)Web Page Ranking Using Web Mining TechniquesMobile Information Systems10.1155/2022/75195732022Online publication date: 1-Jan-2022
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Azad HDeepak AAzad A(2022)LOD search engine: A semantic search over linked dataJournal of Intelligent Information Systems10.1007/s10844-021-00687-059:1(71-91)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s10844-021-00687-0
Polleres AKamdar MFernández JTudorache TMusen M(2020)A more decentralized vision for Linked DataSemantic Web10.3233/SW-19038011:1(101-113)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-190380
Haller APolleres A(2020)Are we better off with just one ontology on the Web?Semantic Web10.3233/SW-19037911:1(87-99)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-190379
Jiang Y(2020)Semantically-enhanced information retrieval using multiple knowledge sourcesCluster Computing10.1007/s10586-020-03057-723:4(2925-2944)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10586-020-03057-7
(2019)Knowledge transfer for efficient cross domain ranking using AdaRank algorithmInternational Journal of Business Intelligence and Data Mining10.5555/3302593.330260014:1-2(89-105)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3302593.3302600
Mountantonakis MTzitzikas Y(2019)Large-scale Semantic Integration of Linked DataACM Computing Surveys10.1145/334555152:5(1-40)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3345551
Jiang YYang MQu R(2019)Semantic similarity measures for formal concept analysis using linked data and WordNetMultimedia Tools and Applications10.1007/s11042-019-7150-278:14(19807-19837)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11042-019-7150-2
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