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

Abstract-Semantic web technologies have gained some spotlight in recent years, mostly explained by the spread of mobile devices and broadband Internet access. As once envisioned by Tim Berners-Lee, semantic web technologies have fostered the development of standards that enable, in turn, the emergence of semantic search engines that give users the information they are looking for. This paper presents the results of a systematic literature review that focuses on understanding the proposals on the semantic search engines from an architectural point of view. From the results it is possible to say that most of the studies propose an integral solution for their users where their requirements, the context and the modules that comprise the search engine have a great role to play. Ontologies and knowledge also play an important role in these architectures as they evolve, enabling a great myriad of solutions that respond in a better way to the users' expectations.

show abstract

“…• 7 studies propose general purpose engines, that is, that can be applied to any field: [5], [6], [23], [19], [18], [24], [31].…”

Section: A General Facts From Selected Studies 1) Quality Assessmentmentioning

confidence: 99%

“…• Extractor components, such as crawlers used by [9] and [11], or extraction systems in [6], which navigates within raw data and store it for further processing. These can also make some sort of filtering, based on system's needs or requirements [13].…”

Section: ) Rq1: Modules Most Used By Semantic Search Engines Implemementioning

confidence: 99%

Research on Proposals and Trends in the Architectures of Semantic Search Engines: A Systematic Literature Review

Morales

Melgar

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…It is capable of marking up web documents with semantic data and present the result to the user 16 [24].…”

Section: Contentusmentioning

confidence: 99%

“…For instance, a newspaper article might be 15 http://www.iais.fraunhofer.de/contentus.html. 16 For a screencast see: http://www.yovisto.com/labs/vissw2011/. presented in its original scanned format, complemented by the text extracted with OCR.…”

Section: Contentusmentioning

confidence: 99%

DBpedia Mashups

Héder¹,

Solt²

2013

Semantic Mashups

View full text Add to dashboard Cite

If you see Wikipedia as a main place where the knowledge of mankind is concentrated, then DBpedia-which is extracted from Wikipedia-is the best place to find the machine representation of that knowledge. DBpedia constitutes a major part of the semantic data on the web. Its sheer size and wide coverage enables you to use it in many kind of mashups: it contains biographical, geographical, bibliographical data; as well as discographies, movie metadata, technical specifications, and links to social media profiles and much more. Just like Wikipedia, DBpedia is a truly cross-language effort, e.g., it provides descriptions and other information in various languages. In this chapter we introduce its structure, contents, and its connections to outside resources. We describe how the structured information in DBpedia is gathered, what you can expect from it and what are its characteristics and limitations. We analyze how other mashups exploit DBpedia and present best practices of its usage. In particular, we describe how Sztakipedia-an intelligent writing aid based on DBpedia-can help Wikipedia contributors to improve the quality and integrity of articles. DBpedia offers a myriad of ways to accessing the information it contains, ranging from SPARQL to bulk download. We compare the pros and cons of these methods. We conclude that DBpedia is an unavoidable resource for applications dealing with commonly known entities like notable persons, places; and for others looking for a rich hub connecting other semantic resources. IntroductionIn this section, we take a closer look at Wikipedia itself, then we examine the process by which DBpedia extracts information from it. WikipediaBy now, Wikipedia is a big ubiquitous collaborative encyclopedia counting over 10 million articles in over 200 languages. Readers are very active: Wikipedia receives over 10 billion page views per month and over 200 thousand edits per day. However, growth in article count and number of contributions no longer seems to be exponential for the largest English language edition. 1 For our purposes, contrasting Wikipedia to traditional printed works is not essential, but it allows us to draw attention to some of its key characteristics. Wikipedia is not governed by a formal editorial board, but instead by the community and its self-imposed guidelines, decision making and escalation processes. Unavoidably, the coverage of articles in a given language edition is biased towards public interest of the Wikipedians speaking the language. The English language Wikipedia has been found to be on a par in accuracy with Encyclopaedia Britannica [12], and with peer reviewed medical journals [25]. Furthermore, Wikipedia has the unmatched ability to cover current events and incorporate changes in near real time.Also, Wikipedia is free to download and hack for everyone. As all digital documents, it has structural elements, like lists and tables. Like encyclopedias, it also has a category system. Furthermore, it contains many infoboxes-structured schemas that communica...

show abstract

“…SWSE (Hogan et al, 2011) Semantic Web Search engine supports SPARQL with RDF representation. Index structure comprises of complete index on quadruples with keyword search function based on inverted index form.…”

Section: Semantic Web Information Management With Automated Reasoningmentioning

confidence: 99%

Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

S.¹

2012

Journal of Computer Science

View full text Add to dashboard Cite

Problem statement: Question Answering (QA) system is taking an important role in current search engine optimization concept. Natural language processing technique is mostly implemented in QA system for asking user's question and several steps are also followed for conversion of questions to query form for getting an exact answer. Approach: This paper surveys different types of question answering system based on ontology and semantic web model with different query format. For comparison, the types of input, query processing method, input and output format of each system and the performance metrics with its limitations are analyzed and discussed. Our question answering for automatic learning system architecture is used to overcome the difficulties raised from the different QA models. Results: The semantic search methodology is implemented by using RDF graph in the application of data structure domain and the performance is also analyzed. Answers are retrieved from ontology using Semantic Search approach and question-to-query algorithm is evaluated in our system for analyzing performance evaluation. Conclusion: Performance of question answering system of getting exact result can be improved by using semantic search methodology for retrieving answers from ontology model. Our system successfully implements this technique and the system is also used in intelligent manner for automatic learning method.Key words: Semantic web, ontology, RDF, semantic search, question answering system NTRODUCTIONThe Question Answering system plays a major role in current era. It is needed when the user gets an indepth knowledge in a particular domain. QA system is classified as two types namely closed domain or restricted domain and open domain model. In QA systems two types of search is available namely keywords based search and semantic search (Zhang, 2006). Normal search engines are working under keyword based searching concept. But some time, there is a problem of getting wrong answer for different meaning of same word. So, semantic search is used to solve the above problem.Semantic search is used to improve the accuracy of search by understanding the intent of the user and the meaning of the terms in the searching sentence. Mainly there are two search are available as namely Navigation Search and Research Search.In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic Search is not applicable to navigational searches. In Research Search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. Rather than Google's PageRank algorithm, Semantic Search uses semantics to produce highly relevant searching results.This Semantic Search technique can be used to retrieve the knowledge from the data source like ontology. Ontology (Fernandez et al., 2009) is a technology used to enable the domain knowledge at a high level and improve the query time used in Questi...

show abstract

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

Cited by 168 publications

References 86 publications

Research on Proposals and Trends in the Architectures of Semantic Search Engines: A Systematic Literature Review

Research on Proposals and Trends in the Architectures of Semantic Search Engines: A Systematic Literature Review

DBpedia Mashups

Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

Contact Info

Product

Resources

About