Efficient Retrieval of Data Using Semantic Search Engine Based on NLP and RDF

2021 ◽  
Author(s):  
Usha Yadav ◽  
Neelam Duhan
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Sparql Query ◽  
Easy Access ◽  
Accurate Information ◽  
Web 3.0 ◽  
User Query ◽  
Query Word ◽  
Occurrence Matrix ◽  
Value Decomposition

With the evolution of Web 3.0, the traditional algorithm of searching Web 2.0 would become obsolete and underperform in retrieving the precise and accurate information from the growing semantic web. It is very reasonable to presume that common users might not possess any understanding of the ontology used in the knowledge base or SPARQL query. Therefore, providing easy access of this enormous knowledge base to all level of users is challenging. The ability for all level of users to effortlessly formulate structure query such as SPARQL is very diverse. In this paper, semantic web based search methodology is proposed which converts user query in natural language into SPARQL query, which could be directed to domain ontology based knowledge base. Each query word is further mapped to the relevant concept or relations in ontology. Score is assigned to each mapping to find out the best possible mapping for the query generation. Mapping with highest score are taken into consideration along with interrogative or other function to finally formulate the user query into SPARQL query. If there is no search result retrieved from the knowledge base, then instead of returning null to the user, the query is further directed to the Web 3.0. The top “k” documents are considered to further converting them into RDF format using Text2Onto tool and the corpus of semantically structured web documents is build. Alongside, semantic crawl agent is used to get <Subject-Predicate-Object> set from the semantic wiki. The Term Frequency Matrix and Co-occurrence Matrix are applied on the corpus following by singular Value decomposition (SVD) to find the results relevant for the user query. The result evaluations proved that the proposed system is efficient in terms of execution time, precision, recall and f-measures.

SWFQA Semantic Web Based Framework for Question Answering

2019 ◽  
Vol 9 (1) ◽  
pp. 88-106
Author(s):  
Irphan Ali ◽  
Divakar Yadav ◽  
Ashok Kumar Sharma
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Knowledge Bases ◽  
Digital Information ◽  
Web Based ◽  
Question Answering System ◽  
User Query

A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Answer Relevance Score” to find the relevance of each answer returned by the system. The results obtained thereof are quite promising. The real-time performance of the system has been evaluated on the answers, extracted from the knowledge base.

scholarly journals DOMAIN ORIENTED ONTOLOGY BASED SEMANTIC WEB SEARCH METHODOLOGY USING SPARQL QUERY

2014 ◽  
Vol 12 (9) ◽  
pp. 3875-3885
Author(s):  
P. Nandha Kumar ◽  
M. Hemalathar
Keyword(s):  
Semantic Web ◽  
Web Search ◽  
Sparql Query ◽  
Correct Result ◽  
Domain Specific ◽  
User Query ◽  
Evaluation Time ◽  
Automated Processing ◽  
The Given ◽  
The Web

Semantic web facilitates the use of automated processing of descriptions on the web and exchange and representation of information is done in a meaningful way. But a conventional search engine, the context and semantics of the user query is not analyzed fully and the data is not well structured so it does not provide the relevant content needed by the user. Hence to overcome this problem, semantic web search has become an essential part in today’s world. In this proposed method the user given query is analyzed semantically and the web data is stored in an ontology which is well structured and conventional search is performed using SPARQL query viewer plug-in. Finally, ranking algorithm is used to rank the extracted links for the given query.  The results obtained are accurate enough to satisfy the request made by the user. The level of accuracy is enhanced since the ontology is made consistent and query is analyzed semantically to retrieve the correct result. The domain specific evaluation time obtained shows promising results.

scholarly journals A Semantic Web Framework for Automated Smart Assistants: A Case Study for Public Health

2021 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Yusuf Sermet ◽  
Ibrahim Demir
Keyword(s):  
Public Health ◽  
Semantic Web ◽  
Information Needs ◽  
Heuristic Algorithms ◽  
Accurate Information ◽  
Domain Experts ◽  
Web Technologies ◽  
Public Health Departments ◽  
Web Framework ◽  
Control And Prevention

The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e., chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits.

scholarly journals Building an OWL ontology with Xper3

2018 ◽  
Vol 2 ◽  
pp. e25614 ◽  
Author(s):  
Florian Pellen ◽  
Sylvain Bouquin ◽  
Isabelle Mougenot ◽  
Régine Vignes-Lebbe
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Expressive Power ◽  
Knowledge Bases ◽  
User Friendliness ◽  
Standard Format ◽  
Ontology Language ◽  
Closed World ◽  
Web Standards ◽  
Description Framework

Xper3 (Vignes Lebbe et al. 2016) is a collaborative knowledge base publishing platform that, since its launch in november 2013, has been adopted by over 2 thousand users (Pinel et al. 2017). This is mainly due to its user friendly interface and the simplicity of its data model. The data are stored in MySQL Relational DBs, but the exchange format uses the TDWG standard format SDD (Structured Descriptive DataHagedorn et al. 2005). However, each Xper3 knowledge base is a closed world that the author(s) may or may not share with the scientific community or the public via publishing content and/or identification key (Kopfstein 2016). The explicit taxonomic, geographic and phenotypic limits of a knowledge base are not always well defined in the metadata fields. Conversely terminology vocabularies, such as Phenotype and Trait Ontology PATO and the Plant Ontology PO, and software to edit them, such as Protégé and Phenoscape, are essential in the semantic web, but difficult to handle for biologist without computer skills. These ontologies constitute open worlds, and are expressed themselves by RDF triples (Resource Description Framework). Protégé offers vizualisation and reasoning capabilities for these ontologies (Gennari et al. 2003, Musen 2015). Our challenge is to combine the user friendliness of Xper3 with the expressive power of OWL (Web Ontology Language), the W3C standard for building ontologies. We therefore focused on analyzing the representation of the same taxonomic contents under Xper3 and under different models in OWL. After this critical analysis, we chose a description model that allows automatic export of SDD to OWL and can be easily enriched. We will present the results obtained and their validation on two knowledge bases, one on parasitic crustaceans (Sacculina) and the second on current ferns and fossils (Corvez and Grand 2014). The evolution of the Xper3 platform and the perspectives offered by this link with semantic web standards will be discussed.

scholarly journals PLATFORMIZING KNOWLEDGE: MESS AND MEANING IN WEB 3.0 INFRASTRUCTURES

2020 ◽  
Author(s):  
Andrew Iliadis ◽  
Wesley Stevens ◽  
Jean-Christophe Plantin ◽  
Amelia Acker ◽  
Huw Davies ◽  
...  
Keyword(s):  
Semantic Web ◽  
Information Exchange ◽  
Semantic Annotation ◽  
Data Access ◽  
Web 3.0 ◽  
Fine Grained ◽  
The Status ◽  
Nature Of Information ◽  
Data Portability ◽  
Machine Readable

This panel focuses on the way that platforms have become key players in the representation of knowledge. Recently, there have been calls to combine infrastructure and platform-based frameworks to understand the nature of information exchange on the web through digital tools for knowledge sharing. The present panel builds and extends work on platform and infrastructure studies in what has been referred to as “knowledge as programmable object” (Plantin, et al., 2018), specifically focusing on how metadata and semantic information are shaped and exchanged in specific web contexts. As Bucher (2012; 2013) and Helmond (2015) show, data portability in the context of web platforms requires a certain level of semantic annotation. Semantic interoperability is the defining feature of so-called "Web 3.0"—traditionally referred to as the semantic web (Antoniou et al, 2012; Szeredi et al, 2014). Since its inception, the semantic web has privileged the status of metadata for providing the fine-grained levels of contextual expressivity needed for machine-readable web data, and can be found in products as diverse as Google's Knowledge Graph, online research repositories like Figshare, and other sources that engage in platformizing knowledge. The first paper in this panel examines the international Schema.org collaboration. The second paper investigates the epistemological implications when platforms organize data sharing. The third paper argues for the use of patents to inform research methodologies for understanding knowledge graphs. The fourth paper discusses private platforms’ extraction and collection of user metadata and the enclosure of data access.

A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems

2007 ◽  
Vol 19 (2) ◽  
pp. 297-309 ◽  
Author(s):  
Yuanbo Guo ◽  
Abir Qasem ◽  
Zhengxiang Pan ◽  
Jeff Heflin
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Knowledge Base Systems ◽  
Web Knowledge

A NOVEL APPROACH FOR SEMANTIC WEB APPLICATION IN ONLINE EDUCATION BASED ON STEGANOGRAPHY

2022 ◽  
Vol 17 (4) ◽  
pp. 0-0
Keyword(s):  
Online Education ◽  
Semantic Web ◽  
Web 2.0 ◽  
Language Processing ◽  
Web Application ◽  
Information Needs ◽  
Extensive Literature ◽  
Web 3.0 ◽  
Novel Approach ◽  
Current Concerns

Semantic Web technology is not new as most of us contemplate; it has evolved over the years. Linked Data web terminology is the name set recently to the Semantic Web. Semantic Web is a continuation of Web 2.0 and it is to replace existing technologies. It is built on Natural Language processing and provides solutions to most of the prevailing issues. Web 3.0 is the version of Semantic Web caters to the information needs of half of the population on earth. This paper links two important current concerns, the security of information and enforced online education due to COVID-19 with Semantic Web. The Steganography requirement for the Semantic web is discussed elaborately, even though encryption is applied which is inadequate in providing protection. Web 2.0 issues concerning online education and semantic Web solutions have been discussed. An extensive literature survey has been conducted related to the architecture of Web 3.0, detailed history of online education, and Security architecture. Finally, Semantic Web is here to stay and data hiding along with encryption makes it robust.

Personal/Cloud Learning Environment, Semantic Web 3.0, and Ontologies

2015 ◽  
pp. 119-146
Keyword(s):  
Learning Environment ◽  
Semantic Web ◽  
Learning Environments ◽  
Environmental Sustainability ◽  
Teaching And Learning ◽  
Personal Learning ◽  
Web 3.0 ◽  
Digital World ◽  
New Vision ◽  
Multiple Challenges

In the era of digital world that we live in, a new vision for learning is required. Learning is essentially personal, sociocultural, distributed, ubiquitous, flexible, dynamic, and complex in nature. There are multiple challenges, opportunities, and movements in learning that must be considered in the development and implementation of online learning environments. From the emerging computational capacity as a virtualized resource pool available over the network, several benefits can be obtained with regard to the management of computing infrastructures, such as environmental sustainability and improved Personal/Cloud Learning Environment use. In fact, Personal learning environments, Cloud computing, Semantic Web 3.0 and Ontologies are relatively new terms that hold considerable promise for future development and research in higher education contexts. Motivated by the aforementioned perspectives, the purpose of this chapter is to explore and discuss how these terms can be understood towards a more personalized, sociocultural, open, dynamic and encouraging model to support/facilitate teaching and learning processes, fulfilling the integrated view of the educational context presented in Part I of this book.

Web 3.0 and E-Learning

2015 ◽  
pp. 101-123 ◽  
Author(s):  
Amit Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Learning Goals ◽  
Learning Behavior ◽  
Defining Moment ◽  
Web 3.0 ◽  
E Learning ◽  
Future Of Education ◽  
The Web

The annals of the Web have been a defining moment in the evolution of education and e-Learning. The evolution of Web 1.0 almost three decades ago has been a precursor to Web 3.0 that has reshaped education and learning today. The evolution to Web 3.0 has been synonymous with “Semantic Web” or “Artificial Intelligence” (AI). AI makes it possible to deliver custom content to the learners based on their learning behavior and preferences. As a result of these developments, the learners have been empowered and have at their disposal a range of Web tools and technology powered by AI to pursue and accomplish their learning goals. This chapter traces the evolution and impact of Web 3.0 and AI on e-Learning and its role in empowering the learner and transforming the future of education and learning. This chapter will be of interest to educators and learners in exploring techniques that improve the quality of education and learning outcomes.

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