Proceedings of the 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences - SWAT4LS '11

2012 ◽  
Keyword(s):  
Semantic Web ◽  
Web Applications ◽  
International Workshop ◽  
Life Sciences

scholarly journals Semantic Web Applications and Tools for Life Sciences, 2008 – Introduction

2009 ◽  
Vol 10 (S10) ◽  
Author(s):  
Albert Burger ◽  
Paolo Romano ◽  
Adrian Paschke ◽  
Andrea Splendiani
Keyword(s):  
Semantic Web ◽  
Web Applications ◽  
Life Sciences

scholarly journals Semantic Web Applications and Tools for the Life Sciences: SWAT4LS 2010

2011 ◽  
Vol 13 (Suppl 1) ◽  
pp. S1 ◽  
Author(s):  
Albert Burger ◽  
Adrian Paschke ◽  
Paolo Romano ◽  
M Scott Marshall ◽  
Andrea Splendiani
Keyword(s):  
Semantic Web ◽  
Web Applications ◽  
Life Sciences

Editorial: Semantic Web for Life Sciences

2006 ◽  
Author(s):  
Michael Schroeder ◽  
Eric Neumann
Keyword(s):  
Semantic Web ◽  
Life Sciences

scholarly journals YeastHub: a semantic web use case for integrating data in the life sciences domain

2005 ◽  
Vol 21 (Suppl 1) ◽  
pp. i85-i96 ◽  
Author(s):  
K.-H. Cheung ◽  
K. Y. Yip ◽  
A. Smith ◽  
R. deKnikker ◽  
A. Masiar ◽  
...  
Keyword(s):  
Semantic Web ◽  
Life Sciences ◽  
Use Case ◽  
Web Use

Service-Oriented Development of Workflow-Based Semantic Reasoning Applications

2016 ◽  
pp. 687-700
Author(s):  
Alexey Cheptsov ◽  
Stefan Wesner ◽  
Bastian Koller
Keyword(s):  
Semantic Web ◽  
High Performance ◽  
Web Applications ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Semantic Reasoning ◽  
Service Oriented ◽  
Cloud Infrastructures ◽  
New Generation ◽  
Reasoning Algorithm

The modern Semantic Web scenarios require reasoning algorithms to be flexible, modular, and highly-configurable. A solid approach, followed in the design of the most currently existing reasoners, is not sufficient when dealing with today's challenges of data analysis across multiple sources of heterogeneous data or when the data amount grows to the “Big Data” sizes. The “reasoning as a workflow” concept has attracted a lot of attention in the design of new-generation Semantic Web applications, offering a lot of opportunities to improve both flexibility and scalability of the reasoning process. Considering a single workflow component as a service offers a lot of opportunities for a reasoning algorithm to target a much wider range of potentially enabled Semantic Web use cases by taking benefits of a service-oriented and component-based implementation. We introduce a technique for developing service-oriented Semantic Reasoning applications based on the workflow concept. We also present the Large Knowledge Collider - a software platform for developing workflow-based Semantic Web applications, taking advantages of on-demand high performance computing and cloud infrastructures.

Linked Data, Towards Realizing the Web of Data

2018 ◽  
pp. 292-312
Author(s):  
Leila Zemmouchi-Ghomari
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Web Applications ◽  
Online Data ◽  
Web Of Data ◽  
Promising Solution ◽  
Basic Ideas ◽  
Online Sources ◽  
Significant Effort ◽  
The Web

Data play a central role in the effectiveness and efficiency of web applications, such as the Semantic Web. However, data are distributed across a very large number of online sources, due to which a significant effort is needed to integrate this data for its proper utilization. A promising solution to this issue is the linked data initiative, which is based on four principles related to publishing web data and facilitating interlinked and structured online data rather than the existing web of documents. The basic ideas, techniques, and applications of the linked data initiative are surveyed in this paper. The authors discuss some Linked Data open issues and potential tracks to address these pending questions.

Explaining Semantic Web Applications

2011 ◽  
pp. 2304-2327
Author(s):  
Deborah L. McGuinness ◽  
Vasco Furtado ◽  
Paulo Pinheiro da Silva ◽  
Li Ding ◽  
Alyssa Glass ◽  
...  
Keyword(s):  
Semantic Web ◽  
Information Extraction ◽  
Web Applications ◽  
Deductive Reasoning ◽  
Learning Systems ◽  
Full Potential ◽  
Integrated Learning ◽  
Integrated Learning Systems ◽  
The One

In this chapter, we introduce the concept of explanation for Semantic Web applications by providing motivation, description, and examples. We describe the Inference Web explanation toolkit that provides support for a broad range of explanation tasks ranging from explaining deductive reasoning, to information extraction, to hybrid integrated learning systems. We argue that an explanation solution such as the one we endorse is required if we are to realize the full potential of hybrid, distributed, intelligent Web agents that users can trust and use.

Web knowledge

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Web Applications ◽  
Semantic Network ◽  
Knowledge Bases ◽  
Web Standards ◽  
Description Framework ◽  
The Web

In the introductory chapter of this book, we discussed the means by which knowledge can be made available on the Web. That is, the representation of the knowledge in a form by which it can be automatically processed by a computer. To recap, we identified two essential steps that were deemed necessary to achieve this task: 1. We discussed the need to agree on a suitable structure for the knowledge that we wish to represent. This is achieved through the construction of a semantic network, which defines the main concepts of the knowledge, and the relationships between these concepts. We presented an example network that contained the main concepts to differentiate between kinds of cameras. Our network is a conceptualization, or an abstract view of a small part of the world. A conceptualization is defined formally in an ontology, which is in essence a vocabulary for knowledge representation. 2. We discussed the construction of a knowledge base, which is a store of knowledge about a domain in machine-processable form; essentially a database of knowledge. A knowledge base is constructed through the classification of a body of information according to an ontology. The result will be a store of facts and rules that describe the domain. Our example described the classification of different camera features to form a knowledge base. The knowledge base is expressed formally in the language of the ontology over which it is defined. In this chapter we elaborate on these two steps to show how we can define ontologies and knowledge bases specifically for the Web. This will enable us to construct Semantic Web applications that make use of this knowledge. The chapter is devoted to a detailed explanation of the syntax and pragmatics of the RDF, RDFS, and OWL Semantic Web standards. The resource description framework (RDF) is an established standard for knowledge representation on the Web. Taken together with the associated RDF Schema (RDFS) standard, we have a language for representing simple ontologies and knowledge bases on the Web.

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