scholarly journals VSDR: Visualization of Semantic Data Representation for Information Search over Semantic Web

2017 ◽  
Vol 7 (5) ◽  
pp. 105-109 ◽  
Author(s):  
N. Kanjanakuha ◽  
◽  
C. Techawut ◽  
R. Sukhahuta ◽  
P. Janecek
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
Data Representation ◽  
Semantic Data

scholarly journals Semantic Association Analysis in Ontology-Based Information Retrieval

2009 ◽  
pp. 131-141
Author(s):  
Payam M. Barnaghi ◽  
Wei Wang ◽  
Jayan C. Kurian
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Ontology Development ◽  
Semantic Association ◽  
Information Search And Retrieval ◽  
Association Discovery ◽  
Meaningful Relationships ◽  
Search And Retrieval

The Semantic Web is an extension to the current Web in which information is provided in machine-processable format. It allows interoperable data representation and expression of meaningful relationships between the information resources. In other words, it is envisaged with the supremacy of deduction capabilities on the Web, that being one of the limitations of the current Web. In a Semantic Web framework, an ontology provides a knowledge sharing structure. The research on Semantic Web in the past few years has offered an opportunity for conventional information search and retrieval systems to migrate from keyword to semantics-based methods. The fundamental difference is that the Semantic Web is not a Web of interlinked documents; rather, it is a Web of relations between resources denoting real world objects, together with well-defined metadata attached to those resources. In this chapter, we first investigate various approaches towards ontology development, ontology population from heterogeneous data sources, semantic association discovery, semantic association ranking and presentation, and social network analysis, and then we present our methodology for an ontology-based information search and retrieval. In particular, we are interested in developing efficient algorithms to resolve the semantic association discovery and analysis issues.

scholarly journals User-centered design and evaluation of overview components for semantic data exploration

2014 ◽  
Vol 66 (5) ◽  
pp. 519-536 ◽  
Author(s):  
Josep Maria Brunetti ◽  
Roberto García
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
Design Methodology ◽  
End Users ◽  
Data Sets ◽  
User Centered Design ◽  
Data Set ◽  
Content Type ◽  
Semantic Data ◽  
User Centered

Purpose – The growing volumes of semantic data available in the web result in the need for handling the information overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies. The paper aims to discuss these issues. Design/methodology/approach – The Visual Information-Seeking Mantra “Overview first, zoom and filter, then details-on-demand” proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a data set. The objective is that the user is capable of getting an idea about the overall structure of the data set. Different information architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users. Findings – The chosen IA components are well known to web users, as they are present in most web pages: navigation bars, site maps and site indexes. The authors complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs. Originality/value – Obtaining semantic data sets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous data sets, which is typical in the Semantic Web, because traditional IA techniques do not easily scale to large data sets. There is little or no support to obtain overview information quickly and easily at the beginning of the exploration of a new data set. This can be a serious limitation when exploring a data set for the first time, especially for lay-users. The proposal is to reuse and adapt existing IA components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.

Automatic Semantic Annotation Using Machine Learning

2012 ◽  
pp. 535-578
Author(s):  
Jie Tang ◽  
Duo Zhang ◽  
Limin Yao ◽  
Yi Li
Keyword(s):  
Machine Learning ◽  
Semantic Web ◽  
Semantic Annotation ◽  
Rapid Expansion ◽  
Semantic Data ◽  
Community Boundaries ◽  
Research Problems ◽  
Common Framework ◽  
Automated Discovery ◽  
The Web

This chapter aims to give a thorough investigation of the techniques for automatic semantic annotation. The Semantic Web provides a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. However, lack of annotated semantic data is a bottleneck to make the Semantic Web vision a reality. Therefore, it is indeed necessary to automate the process of semantic annotation. In the past few years, there was a rapid expansion of activities in the semantic annotation area. Many methods have been proposed for automating the annotation process. However, due to the heterogeneity and the lack of structure of the Web data, automated discovery of the targeted or unexpected knowledge information still present many challenging research problems. In this chapter, we study the problems of semantic annotation and introduce the state-of-the-art methods for dealing with the problems. We will also give a brief survey of the developed systems based on the methods. Several real-world applications of semantic annotation will be introduced as well. Finally, some emerging challenges in semantic annotation will be discussed.

Features and Limitations of Ontologies for Coronavirus Data Management in Libraries

2021 ◽  
pp. 18-40
Author(s):  
Enrique Wulff
Keyword(s):  
Semantic Web ◽  
Data Management ◽  
Quality Criteria ◽  
Emerging Technology ◽  
Data Sources ◽  
Semantic Data ◽  
Important Quality ◽  
Selection For ◽  
Over Time ◽  
Or Applications

The purpose of this chapter is to follow the evolution of what has occurred over time in the ontologies published in response to the COVID-19 pandemic. Correctness and completeness of ontologies on the schema and instance level are important quality criteria in their selection for an application. To help both the librarians and the users, there is a need of a framework for the comparison of different semantic data sources in the COVID-19 pandemic. Meanwhile, online services and/or applications based on ontologies or SKOS-based COVID-19 thesauri are still rare. As an emerging technology in libraries, an all-integrating ontology for coronavirus disease knowledge and data refers to the continuing development of an existing technology. In spite of using ontologies in the Semantic Web, meanings of concepts and relationships are still largely unrealized in terms of obtaining accurate and timely information about COVID-19. But the nature of causal relationships on this disease is made accessible through ontologies as the material in which its main concepts are supported.

An Overview and Technological Background of Semantic Technologies

2021 ◽  
pp. 1-21
Author(s):  
Reinaldo Padilha França ◽  
Ana Carolina Borges Monteiro ◽  
Rangel Arthur ◽  
Yuzo Iano
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Data Representation ◽  
Current Data ◽  
The Internet ◽  
Semantic Technologies ◽  
Web Based ◽  
Web 3.0 ◽  
Technological Advances ◽  
The Web

The Semantic Web concept is an extension of the web obtained by adding semantics to the current data representation format. It is considered a network of correlating meanings. It is the result of a combination of web-based conceptions and technologies and knowledge representation. Since the internet has gone through many changes and steps in its web versions 1.0, 2.0, and Web 3.0, this last call of smart web, the concept of Web 3.0, is to be associated with the Semantic Web, since technological advances have allowed the internet to be present beyond the devices that were made exactly with the intention of receiving the connection, not limited to computers or smartphones since it has the concept of reading, writing, and execution off-screen, performed by machines. Therefore, this chapter aims to provide an updated review of Semantic Web and its technologies showing its technological origins and approaching its success relationship with a concise bibliographic background, categorizing and synthesizing the potential of technologies.

scholarly journals WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data Analytics

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2090
Author(s):  
Addi Ait-Mlouk ◽  
Xuan-Son Vu ◽  
Lili Jiang
Keyword(s):  
Semantic Web ◽  
Association Rules ◽  
Language Processing ◽  
Data Analytics ◽  
Data Retrieval ◽  
Heterogeneous Data ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Multiple Sources ◽  
Semantic Data

Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules.

Semantic Data Mapping on E-Learning Usage Index Tool to Handle Heterogeneity of Data Representation

2014 ◽  
Vol 69 (5) ◽  
Author(s):  
Arda Yunianta ◽  
Norazah Yusof ◽  
Mohd Shahizan Othman ◽  
Abdul Aziz ◽  
Nataniel Dengen ◽  
...  
Keyword(s):  
Data Representation ◽  
Current Data ◽  
Data Mapping ◽  
Semantic Technology ◽  
Semantic Data ◽  
Triple Store ◽  
Integration Problem ◽  
E Learning ◽  
Semantic Ontology ◽  
Future Work

Distribution and heterogeneity of data is the current issues in data level implementation. Different data representation between applications makes the integration problem increasingly complex. Stored data between applications sometimes have similar meaning, but because of the differences in data representation, the application cannot be integrated with the other applications. Many researchers found that the semantic technology is the best way to resolve the current data integration issues. Semantic technology can handle heterogeneity of data; data with different representations and sources. With semantic technology data mapping can also be done from different database and different data format that have the same meaning data. This paper focuses on the semantic data mapping using semantic ontology approach. In the first level of process, semantic data mapping engine will produce data mapping language with turtle (.ttl) file format that can be used for Local Java Application using Jena Library and Triple Store. In the second level process, D2R Server that can be access from outside environment is provided using HTTP Protocol to access using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. Future work to will continue on this topic, focusing on E-Learning Usage Index Tool (IPEL) application that is able to integrate with others system applications like Moodle E-Learning Systems. 

Semantic web: golden era of information

2018 ◽  
Vol 7 (2.8) ◽  
pp. 436
Author(s):  
Prakhar Agarwal ◽  
Shivani Jain
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
Review Paper ◽  
Development Project ◽  
Structured Data ◽  
Common Language ◽  
End User ◽  
Ontology Language ◽  
Shared Language ◽  
Definition Of

Semantic Web is the extension of existing web that allows well defined expressions for the meaning of information which can be understood by computers and people both. In this paper we are doing study on semantic and is our review paper. Semantic web is a recommended development project by W3C (World Wide Web Consortium) which focuses on the enhancing of information search by keeping the facts in structured form using eXtensible Mark-up Language (XML) and marked in such a way that it can be understand by the system. To make the development of semantic web promising, new international standard is developed for exchanging of ontologies called OWL Web Ontology language. In XML we just provide tag of the model and store data in the hierarchy without its meaning, that’s why the computer cannot be able to process the data but in Semantic Web user can provide with a definition so that the computer can better recognize its meaning and provide with the better displaying of information. A crux of semantic web is that it works on the definition of the ontologies. Ontologies are responsible for re-usability and sharing of information. Semantic Web provides with a shared language which has stored data in the non-ending linking of distinct databases which provides data related to the real world objects. RDF is a common language for semantic web and is responsible for the collection of data on web and assembles different database from diverse sources and SPARQL is there for linking of databases for unifying documents. Thus, semantic web is the well-structured data web that relates all the data that present on the web and understands them to provide the exact display requested by the end user.

scholarly journals Interactive 3D Exploration of RDF Graphs through Semantic Planes

2018 ◽  
Vol 10 (8) ◽  
pp. 81 ◽  
Author(s):  
Fabio Viola ◽  
Luca Roffia ◽  
Francesco Antoniazzi ◽  
Alfredo D’Elia ◽  
Cristiano Aguzzi ◽  
...  
Keyword(s):  
Semantic Web ◽  
3D Visualization ◽  
Data Representation ◽  
Knowledge Bases ◽  
Rdf Graph ◽  
Common Concept ◽  
Interactive 3D ◽  
3D Scene ◽  
Rdf Graphs ◽  
3D Exploration

This article presents Tarsier, a tool for the interactive 3D visualization of RDF graphs. Tarsier is mainly intended to support teachers introducing students to Semantic Web data representation formalisms and developers in the debugging of applications based on Semantic Web knowledge bases. The tool proposes the metaphor of semantic planes as a way to visualize an RDF graph. A semantic plane contains all the RDF terms sharing a common concept; it can be created, and further split into several planes, through a set of UI controls or through SPARQL 1.1 queries, with the full support of OWL and RDFS. Thanks to the 3D visualization, links between semantic planes can be highlighted and the user can navigate within the 3D scene to find the better perspective to analyze data. Data can be gathered from generic SPARQL 1.1 protocol services. We believe that Tarsier will enhance the human friendliness of semantic technologies by: (1) helping newcomers assimilate new data representation formats; and (2) increasing the capabilities of inspection to detect relevant situations even in complex RDF graphs.

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