scholarly journals Semantic Web Portal: A Platform for Better Browsing and Visualizing Semantic Data

2010 ◽  
pp. 448-460 ◽  
Author(s):  
Ying Ding ◽  
Yuyin Sun ◽  
Bin Chen ◽  
Katy Borner ◽  
Li Ding ◽  
...  
Keyword(s):  
Semantic Web ◽  
Web Portal ◽  
Semantic Data

Integrating Web Portals with Semantic Web Services

2012 ◽  
pp. 326-336
Author(s):  
César J. Acuña ◽  
Mariano Minoli ◽  
Esperanza Marcos
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Data Integration ◽  
Methodological Approach ◽  
Integrated Systems ◽  
Semantic Web Services ◽  
Web Portals ◽  
Web Portal ◽  
Integration Architecture

Several systems integration proposals have been suggested over the years. However these proposals have mainly focused on data integration, not allowing users to take advantage of services offered by Web portals. Most of the mentioned proposals only provide a set of design principles to build integrated systems and lack in suggesting a systematic way of how to develop systems based on the integration architecture they propose. In previous work we have developed PISA (Web Portal Integration Architecture)—a Web portal integration architecture for data and services—and MIDAS-S, a methodological approach for the development of integrated Web portals, built according to PISA. This work shows, by means of a case study, how both proposals fit together integrating Web portals.

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.

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.

scholarly journals Implementing E-Learning Ontology to Scale For Provenance

2019 ◽  
Vol 8 (9) ◽  
pp. 670-675
Keyword(s):  
Semantic Web ◽  
Learning Model ◽  
Model Data ◽  
Web Based ◽  
Semantic Data ◽  
Web Based Learning ◽  
Proposed Model ◽  
E Learning ◽  
Academic Resource ◽  
Future Work

This paper explores the aspects of providing education through E-learning model evaluating its relevance to distance education and for ICT systems. A subset of E-learning is a Web based learning that makes the learning -easier, impressive, structured and properly managed. The paper defines an university ontology describing how e-learning provides resources which are available online and designated cloud that can be delivered anywhere any time among the users. In the proposed model data is stored in designated cloud and users are able to share efficiently the same as it provides services to learner. Provenance or trust with respect to the academic resource is a major concern in these types of models, users accessing data must be trustable which help learners, researchers, developers, and users in future work also. This paper proposes an e-learning model which is well organized and structured, such that the machine responds with the accurate, trustable, desired information and results. The paper defines an ontology for semantic structuring, semantic rendering and applies provenance on suggested ontology to achieve authentic results. It is also desired to establish trust of the source contents of the Semantic Web, with the result that a user receiving data will need to verify whether the received data from source is in fact trustable or not. The defined ontogoly is suitable for consumption of both man and machine in the context of the e-learning and Semantic data rendering Web Keywords

scholarly journals Inference and Linking on the Humanist’s Semantic Web

2014 ◽  
Vol 5 (4) ◽  
Author(s):  
John Edward Simpson
Keyword(s):  
Semantic Web ◽  
Web Data ◽  
Inference Generation ◽  
Distributed Source ◽  
Semantic Data ◽  
Source Data

The Semantic Web promises that the pools of semantic data it interweaves together will enable people to find information that they could not otherwise find by revealing knowledge not explicitly visible in the distributed source data.  In order for this promise to be fulfilled within the humanities, the Semantic Web data being created must have certain features, but what are they? This article provides some background on Semantic Web inferencing and then argues that there are three things that humanists can do to prepare their data to participant in this sort of inference generation: add more data, reciprocate links across repositories, and add metadata specifically to support inferencing.

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
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