Search results

1 – 10 of over 2000
Article
Publication date: 13 April 2015

Ahmet Uyar and Farouk Musa Aliyu

The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage…

2189

Abstract

Purpose

The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces.

Design/methodology/approach

The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography.

Findings

The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised terms.

Research limitations/implications

Both companies are continually working to improve their semantic web search engines. Therefore, the findings show their capabilities at the time of conducting this research.

Practical implications

The results show that in the near future the authors can expect both semantic search engines to expand their entity databases and improve their natural language interfaces.

Originality/value

As far as the authors know, this is the first study evaluating any aspect of newly developing semantic web search engines. It shows the current capabilities and limitations of these semantic web search engines. It provides directions to researchers by pointing out the main problems for semantic web search engines.

Details

Online Information Review, vol. 39 no. 2
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 20 June 2016

Awny Sayed and Amal Al Muqrishi

The purpose of this paper is to present an efficient and scalable Arabic semantic search engine based on a domain-specific ontological graph for Colleges of Applied Science…

Abstract

Purpose

The purpose of this paper is to present an efficient and scalable Arabic semantic search engine based on a domain-specific ontological graph for Colleges of Applied Science, Sultanate of Oman (CASOnto). It also supports the factorial question answering and uses two types of searching: the keyword-based search and the semantics-based search in both languages Arabic and English. This engine is built on variety of technologies such as resource description framework data and ontological graph. Furthermore, two experimental results are conducted; the first is a comparison among entity-search and the classical-search in the system itself. The second compares the CASOnto with well-known semantic search engines such as Kngine, Wolfram Alpha and Google to measure their performance and efficiency.

Design/methodology/approach

The design and implementation of the system comprises the following phases, namely, designing inference, storing, indexing, searching, query processing and the user’s friendly interface, where it is designed based on a specific domain of the IBRI CAS (College of Applied Science) to highlight the academic and nonacademic departments. Furthermore, it is ontological inferred data stored in the tuple data base (TDB) and MySQL to handle the keyword-based search as well as entity-based search. The indexing and searching processes are built based on the Lucene for the keyword search, while TDB is used for the entity search. Query processing is a very important component in the search engines that helps to improve the user’s search results and make the system efficient and scalable. CASOnto handles the Arabic issues such as spelling correction, query completion, stop words’ removal and diacritics removal. It also supports the analysis of the factorial question answering.

Findings

In this paper, an efficient and scalable Arabic semantic search engine is proposed. The results show that the semantic search that built on the SPARQL is better than the classical search in both simple and complex queries. Clearly, the accuracy of semantic search equals to 100 per cent in both types of queries. On the other hand, the comparison of CASOnto with the Wolfram Alpha, Kngine and Google refers to better results by CASOnto. Consequently, it seems that our proposed engine retrieved better and efficient results than other engines. Thus, it is built according to the ontological domain-specific, highly scalable performance and handles the complex queries well by understanding the context behind the query.

Research limitations/implications

The proposed engine is built on a specific domain (CAS Ibri – Oman), and in the future vision, it will highlight the nonfactorial question answering and expand the domain of CASOnto to involve more integrated different domains.

Originality/value

The main contribution of this paper is to build an efficient and scalable Arabic semantic search engine. Because of the widespread use of search engines, a new dimension of challenge is created to keep up with the evolution of the semantic Web. Whereas, catering to the needs of users has become a matter of paramount importance in the light of artificial intelligence and technological development to access the accurate and the efficient information in less possible time. However, the research challenges still in its infancy due to lack of research engine that supports the Arabic language. It could be traced back to the complexity of the Arabic language morphological and grammar rules.

Details

International Journal of Web Information Systems, vol. 12 no. 2
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 14 April 2014

Valentina Franzoni and Alfredo Milani

In this work, a new general framework is proposed to guide navigation over a collaborative concept network, in order to discover paths between concepts. Finding semantic chains…

Abstract

Purpose

In this work, a new general framework is proposed to guide navigation over a collaborative concept network, in order to discover paths between concepts. Finding semantic chains between concepts over a semantic network is an issue of great interest for many applications, such as explanation generation and query expansion. Collaborative concept networks over the web tend to have features such as large dimensions, high connectivity degree, dynamically evolution over the time, which represent special challenges for efficient graph search methods, since they result in huge memory requirements, high branching factors, unknown dimensions and high cost for accessing nodes. The paper aims to discuss these issues.

Design/methodology/approach

The proposed framework is based on the novel notion of heuristic semantic walk (HSW). In the HSW framework, a semantic proximity measure among concepts, reflecting the collective knowledge embedded in search engines or other statistical sources, is used as a heuristic in order to guide the search in the collaborative network. Different search strategies, information sources and proximity measures, can be used to adapt HSW to the collaborative semantic network under consideration.

Findings

Experiments held on the Wikipedia network and Bing search engine on a range of different semantic measures show that the proposed HSW approach with weighted randomized walk strategy outperforms state-of-the-art search methods.

Originality/value

To the best of the authors' knowledge, the proposed HSW model is the first approach which uses search engine-based proximity measures as heuristic for semantic search.

Article
Publication date: 18 November 2013

Jorge Luis Morato, Sonia Sanchez-Cuadrado, Christos Dimou, Divakar Yadav and Vicente Palacios

– This paper seeks to analyze and evaluate different types of semantic web retrieval systems, with respect to their ability to manage and retrieve semantic documents.

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Abstract

Purpose

This paper seeks to analyze and evaluate different types of semantic web retrieval systems, with respect to their ability to manage and retrieve semantic documents.

Design/methodology/approach

The authors provide a brief overview of knowledge modeling and semantic retrieval systems in order to identify their major problems. They classify a set of characteristics to evaluate the management of semantic documents. For doing the same the authors select 12 retrieval systems classified according to these features. The evaluation methodology followed in this work is the one that has been used in the Desmet project for the evaluation of qualitative characteristics.

Findings

A review of the literature has shown deficiencies in the current state of the semantic web to cope with known problems. Additionally, the way semantic retrieval systems are implemented shows discrepancies in their implementation. The authors analyze the presence of a set of functionalities in different types of semantic retrieval systems and find a low degree of implementation of important specifications and in the criteria to evaluate them. The results of this evaluation indicate that, at the moment, the semantic web is characterized by a lack of usability that is derived by the problems related to the management of semantic documents.

Originality/value

This proposal shows a simple way to compare requirements of semantic retrieval systems based in DESMET methodology qualitatively. The functionalities chosen to test the methodology are based on the problems as well as relevant criteria discussed in the literature. This work provides functionalities to design semantic retrieval systems in different scenarios.

Details

Library Hi Tech, vol. 31 no. 4
Type: Research Article
ISSN: 0737-8831

Keywords

Book part
Publication date: 4 October 2012

David Stuart

Purpose – To investigate the potential of the semantic web as a source of information about social networks within academia, as well as more widely for webometric…

Abstract

Purpose – To investigate the potential of the semantic web as a source of information about social networks within academia, as well as more widely for webometric investigations.

Methodology – The functionality of five semantic search engines were analyzed to determine their suitability for webometric investigations, with the most suitable, Sindice.com, then being used to investigate the use of Friend of a Friend (FOAF) within UK academic web space.

Findings – In comparison to the web of documents, the semantic web is still a small part of online content. Even the well-established FOAF social vocabulary was not found on the majority of academic web sites, let alone being found to represent the majority of academics, and provided little indication of social networks between institutions. Nonetheless from a webometric perspective the study does show the potential of a semantic web for a far wider range of webometric investigations, and demonstrates that, unlike the traditional web, there are currently useful tools available.

Implications – Having established that there are appropriate tools available for webometric investigations of the semantic web, and acknowledging the potential of the semantic web for far more detailed webometric investigations, there is a need for additional studies to determine the specific strengths and limitations of the tools that are available, and investigate those areas where webometric investigations can provide the most useful insights.

Originality/value – The research applies established webometric methodologies to the social semantic web, demonstrating the potential of a whole new area for future webometric investigation.

Details

Social Information Research
Type: Book
ISBN: 978-1-78052-833-5

Keywords

Article
Publication date: 5 March 2018

Ahmad Mehrbod, Aneesh Zutshi, António Grilo and Ricardo Jardim-Gonsalves

Searching the tender notices that publish every day in open tendering websites is a common way for finding business opportunity in public procurement. The heterogeneity of tender…

Abstract

Purpose

Searching the tender notices that publish every day in open tendering websites is a common way for finding business opportunity in public procurement. The heterogeneity of tender notices from various tendering marketplaces is a challenge for exploiting semantic technologies in the tender search.

Design/methodology/approach

Most of the semantic matching approaches require the data to be structured and integrated according to a data model. But the integration process can be expensive and time-consuming especially for multi-source data integration.

Findings

In this paper, a product search mechanism that had been developed in an e-procurement platform for matching product e-catalogues is applied to the tender search problem. The search performance has been compared using two procurement vocabularies on searching tender notices from two major tender resources.

Originality/value

The test results show that the matching mechanism is able to find tender notices from heterogeneous resources and different classification systems without transforming the tenders to a uniform data model.

Details

Journal of Public Procurement, vol. 18 no. 1
Type: Research Article
ISSN: 1535-0118

Keywords

Article
Publication date: 25 October 2022

Samir Sellami and Nacer Eddine Zarour

Massive amounts of data, manifesting in various forms, are being produced on the Web every minute and becoming the new standard. Exploring these information sources distributed in…

Abstract

Purpose

Massive amounts of data, manifesting in various forms, are being produced on the Web every minute and becoming the new standard. Exploring these information sources distributed in different Web segments in a unified way is becoming a core task for a variety of users’ and companies’ scenarios. However, knowledge creation and exploration from distributed Web data sources is a challenging task. Several data integration conflicts need to be resolved and the knowledge needs to be visualized in an intuitive manner. The purpose of this paper is to extend the authors’ previous integration works to address semantic knowledge exploration of enterprise data combined with heterogeneous social and linked Web data sources.

Design/methodology/approach

The authors synthesize information in the form of a knowledge graph to resolve interoperability conflicts at integration time. They begin by describing KGMap, a mapping model for leveraging knowledge graphs to bridge heterogeneous relational, social and linked web data sources. The mapping model relies on semantic similarity measures to connect the knowledge graph schema with the sources' metadata elements. Then, based on KGMap, this paper proposes KeyFSI, a keyword-based semantic search engine. KeyFSI provides a responsive faceted navigating Web user interface designed to facilitate the exploration and visualization of embedded data behind the knowledge graph. The authors implemented their approach for a business enterprise data exploration scenario where inputs are retrieved on the fly from a local customer relationship management database combined with the DBpedia endpoint and the Facebook Web application programming interface (API).

Findings

The authors conducted an empirical study to test the effectiveness of their approach using different similarity measures. The observed results showed better efficiency when using a semantic similarity measure. In addition, a usability evaluation was conducted to compare KeyFSI features with recent knowledge exploration systems. The obtained results demonstrate the added value and usability of the contributed approach.

Originality/value

Most state-of-the-art interfaces allow users to browse one Web segment at a time. The originality of this paper lies in proposing a cost-effective virtual on-demand knowledge creation approach, a method that enables organizations to explore valuable knowledge across multiple Web segments simultaneously. In addition, the responsive components implemented in KeyFSI allow the interface to adequately handle the uncertainty imposed by the nature of Web information, thereby providing a better user experience.

Details

International Journal of Web Information Systems, vol. 18 no. 5/6
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 12 April 2013

Nadjla Hariri

Purpose – The main purpose of this research is to determine whether the performance of natural language (NL) search engines in retrieving exact answers to the NL queries differs…

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Abstract

Purpose – The main purpose of this research is to determine whether the performance of natural language (NL) search engines in retrieving exact answers to the NL queries differs from that of keyword searching search engines. Design/methodology/approach – A total of 40 natural language queries were posed to Google and three NL search engines: Ask.com, Hakia and Bing. The first results pages were compared in terms of retrieving exact answer documents and whether they were at the top of the retrieved results, and the precision of exact answer and relevant documents. Findings – Ask.com retrieved exact answer document descriptions at the top of the results list in 60 percent of searches, which was better than the other search engines, but the mean value of the number of exact answer top list documents for three NL search engines (20.67) was a little less than Google's (21). There was no significant difference between the precision for Google and three NL search engines in retrieving exact answer documents for NL queries. Practical implications – The results imply that all NL and keyword searching search engines studied in this research mostly employ similar techniques using keywords of the NL queries, which is far from semantic searching and understanding what the user wants in searching with NL queries. Originality/value – The results shed light into the claims of NL search engines regarding semantic searching of NL queries.

Details

Online Information Review, vol. 37 no. 2
Type: Research Article
ISSN: 1468-4527

Keywords

Open Access
Article
Publication date: 18 August 2021

Maria Giovanna Confetto and Claudia Covucci

For companies that intend to respond to the modern conscious consumers' needs, a great competitive advantage is played on the ability to incorporate sustainability messages in…

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Abstract

Purpose

For companies that intend to respond to the modern conscious consumers' needs, a great competitive advantage is played on the ability to incorporate sustainability messages in marketing communications. The aim of this paper is to address this important priority in the web context, building a semantic algorithm that allows content managers to evaluate the quality of sustainability web contents for search engines, considering the current semantic web development.

Design/methodology/approach

Following the Design Science (DS) methodological approach, the study develops the algorithm as an artefact capable of solving a practical problem and improving the operation of content managerial process.

Findings

The algorithm considers multiple factors of evaluation, grouped in three parameters: completeness, clarity and consistency. An applicability test of the algorithm was conducted on a sample of web pages of the Google blog on sustainability to highlight the correspondence between the established evaluation factors and those actually used by Google.

Practical implications

Studying content marketing for sustainability communication constitutes a new field of research that offers exciting opportunities. Writing sustainability contents in an effective way is a fundamental step to trigger stakeholder engagement mechanisms online. It could be a positive social engineering technique in the hands of marketers to make web users able to pursue sustainable development in their choices.

Originality/value

This is the first study that creates a theoretical connection between digital content marketing and sustainability communication focussing, especially, on the aspects of search engine optimization (SEO). The algorithm of “Sustainability-contents SEO” is the first operational software tool, with a regulatory nature, that is able to analyse the web contents, detecting the terms of the sustainability language and measuring the compliance to SEO requirements.

Details

The TQM Journal, vol. 33 no. 7
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 16 November 2015

Hoang-Minh Nguyen, Hong-Quang Nguyen, Khoi-Nguyen Tran and Xuan-Vinh Vo

This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured…

Abstract

Purpose

This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured nature and sheer volume of information accessible over networks have made it drastically difficult for users to seek relevant information. Many information-retrieval methods have been developed to address this problem, and keyword-based approach is amongst the most common approach. Such an approach is often inadequate to cope with the conceptualization associated with user needs and contents. This brings about the problem of semantic ambiguation that refers to the disagreement in meaning of terms between involving parties of a communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and other related activities.

Design/methodology/approach

A novel ontology-based search approach, named GeTFIRST (short for Graph-embedded Tree Fostering Information Retrieval SysTem), is proposed to disambiguate keywords semantically. The contribution is twofold. First, a search strategy is proposed to prune irrelevant concepts for accuracy improvement using our Graph-embedded Tree (GeT)-based ontology. Second, a path-based ranking algorithm is proposed to incorporate and reward the content specificity.

Findings

An empirical evaluation was performed on United States Patent And Trademark Office (USPTO) patent datasets to compare our approach with full-text patent search approaches. The results showed that GeTFIRST handled the ambiguous keywords with higher keyword-disambiguation accuracy than traditional search approaches.

Originality/value

The search approach of this paper copes with the semantic ambiguation by using our proposed GeT-based ontology and a path-based ranking algorithm.

Details

International Journal of Web Information Systems, vol. 11 no. 4
Type: Research Article
ISSN: 1744-0084

Keywords

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