An Overview of the Semantic Web Improving Web Data Accessibility and Performance

Pertinence ◽  
2014 ◽  
pp. 99-121
Author(s):  
Antoine Abou Rjeily ◽  
Joe Tekli ◽  
Pélagie Hongue ◽  
Richard Chbeir ◽  
Kokou Yetongnon
Keyword(s):  
Semantic Web ◽  
Web Data ◽  
Data Accessibility ◽  
And Performance

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.

Mini-ME Matchmaker and Reasoner for the Semantic Web of Things

2018 ◽  
pp. 262-294 ◽  
Author(s):  
Floriano Scioscia ◽  
Michele Ruta ◽  
Giuseppe Loseto ◽  
Filippo Gramegna ◽  
Saverio Ieva ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
Semantic Web ◽  
Knowledge Bases ◽  
Semantic Web Technologies ◽  
Experimental Performance ◽  
Web Of Things ◽  
Web Technologies ◽  
Constrained Environments ◽  
And Performance

The Semantic Web of Things (SWoT) aims to support smart semantics-enabled applications and services in pervasive contexts. Due to architectural and performance issues, most Semantic Web reasoners are often impractical to be ported: they are resource consuming and are basically designed for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper describes Mini-ME (the Mini Matchmaking Engine), a mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering, bonus, difference) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios and experimental performance evaluation are presented on PC (against other popular Semantic Web reasoners), smartphone and embedded single-board computer testbeds.

Using an Ontology-Based Framework to Extract External Web Data for the Data Warehouse

2011 ◽  
pp. 39-59
Author(s):  
Charles Greenidge ◽  
Hadrian Peter
Keyword(s):  
Semantic Web ◽  
Data Warehouse ◽  
Language Processing ◽  
Web Application ◽  
Data Warehouses ◽  
Web Data ◽  
Intermediate Data ◽  
External Data ◽  
Automatic Matching ◽  
The Web

Data warehouses have established themselves as necessary components of an effective Information Technology (IT) strategy for large businesses. In addition to utilizing operational databases data warehouses must also integrate increasing amounts of external data to assist in decision support. An important source of such external data is the Web. In an effort to ensure the availability and quality of Web data for the data warehouse we propose an intermediate data-staging layer called the Meta-Data Engine (M-DE). A major challenge, however, is the conversion of data originating in the Web, and brought in by robust search engines, to data in the data warehouse. The authors therefore also propose a framework, the Semantic Web Application (SEMWAP) framework, which facilitates semi-automatic matching of instance data from opaque web databases using ontology terms. Their framework combines Information Retrieval (IR), Information Extraction (IE), Natural Language Processing (NLP), and ontology techniques to produce a matching and thus provide a viable building block for Semantic Web (SW) Applications.

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