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.

Semantic Web Data Partitioning

2013 ◽  
pp. 154-165
Author(s):  
Trupti Padiya ◽  
Minal Bhise ◽  
Sanjay Chaudhary
Keyword(s):  
Semantic Web ◽  
Real Life ◽  
Scaling Up ◽  
Relational Data ◽  
Web Database ◽  
Web Data ◽  
Query Performance ◽  
Semantic Data ◽  
Rdf Data ◽  
Rdf Database

Semantic Web database is an RDF database due to increased use of Semantic Web in real life applications; one can find heavy growth in RDF database. As there is a tremendous increase in RDF data, performance and scalability issues are of main concern. This chapter discusses improving and scaling up query performance for increasingly growing Semantic Web. It discusses current Semantic Web data storage techniques, which have been found to scale poorly and have poor query performance. It discusses the partitioning techniques vertical and horizontal partitioning to improve query performance. To further improve the query performance, along with these partitioning techniques, various compression techniques can also be used. Relational data offers faster execution of queries as compared to RDF data. To demonstrate these ideas, semantic data is converted to relational data and then query performance improvement techniques are applied. The scaling up of Semantic Web data is also discussed.

Research and Development on Semantic Web Data Management

2009 ◽  
Vol 20 (11) ◽  
pp. 2950-2964 ◽  
Author(s):  
Xiao-Yong DU ◽  
Yan WANG ◽  
Bin LÜ
Keyword(s):  
Semantic Web ◽  
Research And Development ◽  
Data Management ◽  
Web Data ◽  
Web Data Management

scholarly journals Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data

2007 ◽  
pp. 228-246 ◽  
Author(s):  
Matthew Perry ◽  
Amit P. Sheth ◽  
Farshad Hakimpour ◽  
Prateek Jain
Keyword(s):  
Semantic Web ◽  
Web Data

scholarly journals Learning from Ontology Streams with Semantic Concept Drift

2017 ◽  
Author(s):  
Jiaoyan Chen ◽  
Freddy Lecue ◽  
Jeff Z. Pan ◽  
Huajun Chen
Keyword(s):  
Semantic Web ◽  
Data Stream ◽  
Concept Drift ◽  
Data Distribution ◽  
Accurate Prediction ◽  
Knowledge Structures ◽  
Semantic Concept ◽  
Web Data ◽  
Semantic Inference

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.

A Novel Approach for Practical Semantic Web Data Management

2008 ◽  
pp. 650-655 ◽  
Author(s):  
Giorgio Gianforme ◽  
Roberto De Virgilio ◽  
Stefano Paolozzi ◽  
Pierluigi Del Nostro ◽  
Danilo Avola
Keyword(s):  
Semantic Web ◽  
Data Management ◽  
Web Data ◽  
Novel Approach ◽  
Web Data Management

Formal Concept Discovery in Semantic Web Data

2012 ◽  
pp. 164-179 ◽  
Author(s):  
Markus Kirchberg ◽  
Erwin Leonardi ◽  
Yu Shyang Tan ◽  
Sebastian Link ◽  
Ryan K. L. Ko ◽  
...  
Keyword(s):  
Semantic Web ◽  
Formal Concept ◽  
Web Data ◽  
Concept Discovery

Discovering Complex Relationships of Drugs over Distributed Knowledgebases

2016 ◽  
pp. 1572-1591
Author(s):  
Juan Li ◽  
Ranjana Sharma ◽  
Yan Bai
Keyword(s):  
Drug Discovery ◽  
Semantic Web ◽  
Distributed System ◽  
Large Scale ◽  
The Internet ◽  
Full Potential ◽  
Web Data ◽  
Semantic Relationships ◽  
Complex Relationships

Drug discovery is a lengthy, expensive and difficult process. Indentifying and understanding the hidden relationships among drugs, genes, proteins, and diseases will expedite the process of drug discovery. In this paper, we propose an effective methodology to discover drug-related semantic relationships over large-scale distributed web data in medicine, pharmacology and biotechnology. By utilizing semantic web and distributed system technologies, we developed a novel hierarchical knowledge abstraction and an efficient relation discovery protocol. Our approach effectively facilitates the realization of the full potential of harnessing the collective power and utilization of the drug-related knowledge scattered over the Internet.

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