Using Methods of Parallel Semi-structured Data Processing for Semantic Web

2009 ◽  
Author(s):  
David Bednarek ◽  
Jirí Dokulil ◽  
Jakub Yaghob ◽  
Filip Zavoral
Keyword(s):  
Semantic Web ◽  
Data Processing ◽  
Structured Data

Spatiotemporal-Thematic Data Processing for the Semantic Web

2007 ◽  
pp. 79-89 ◽  
Author(s):  
Farshad Hakimpour ◽  
Boanerges Aleman-Meza ◽  
Matthew Perry ◽  
Amit Sheth
Keyword(s):  
Semantic Web ◽  
Data Processing ◽  
Thematic Data

A WIKIPEDIA-BASED FRAMEWORK FOR COLLABORATIVE SEMANTIC ANNOTATION

2011 ◽  
Vol 20 (05) ◽  
pp. 847-886 ◽  
Author(s):  
N. FERNÁNDEZ ◽  
J. A. FISTEUS ◽  
D. FUENTES ◽  
L. SÁNCHEZ ◽  
V. LUQUE
Keyword(s):  
Semantic Web ◽  
Data Processing ◽  
Web Search ◽  
Semantic Annotation ◽  
Formal Ontology ◽  
Manual Annotation ◽  
Annotation Task ◽  
Web Resources ◽  
User Actions ◽  
Common User

The semantic web aims at automating web data processing tasks that nowadays only humans are able to do. To make this vision a reality, the information on web resources should be described in a computer-meaningful way, in a process known as semantic annotation. In this paper, a manual, collaborative semantic annotation framework is described. It is designed to take advantage of the benefits of manual annotation systems (like the possibility of annotating formats difficult to annotate in an automatic manner) addressing at the same time some of their limitations (reduce the burden for non-expert annotators). The framework is inspired by two principles: use Wikipedia as a facade for a formal ontology and integrate the semantic annotation task with common user actions like web search. The tools in the framework have been implemented, and empirical results obtained in experiences carried out with these tools are reported.

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.

Management of SME's Semi Structured Data Using Semantic Technique

Web Services ◽  
2019 ◽  
pp. 1812-1835
Author(s):  
Saravjeet Singh ◽  
Jaiteg Singh
Keyword(s):  
Semantic Web ◽  
Data Management ◽  
Complex Form ◽  
Web Technology ◽  
Structured Data ◽  
Unstructured Data ◽  
Effective Solution ◽  
Semantic Web Technology ◽  
Semantic Data ◽  
To Come

Management of data for an organization is crucial task but when data goes to its complex form then it becomes multifaceted as well as vital. In today era most of the organizations generating semi structured or unstructured data that requires special techniques to handle and manage. With the needs to handle unstructured data, semantic web technology provides a way to come up with the effective solution. In this chapter Synthetic Semantic Data Management (SSDM) is explained that is based semantic web technique and will helps to manage data of small and Midsized Enterprise (SME). SSDM provide the procedure to handle, store, manages and retrieval of semi structured data.

Management of SME's Semi Structured Data Using Semantic Technique

2018 ◽  
pp. 1614-1637
Author(s):  
Saravjeet Singh ◽  
Jaiteg Singh
Keyword(s):  
Semantic Web ◽  
Data Management ◽  
Complex Form ◽  
Web Technology ◽  
Structured Data ◽  
Unstructured Data ◽  
Effective Solution ◽  
Semantic Web Technology ◽  
Semantic Data ◽  
To Come

Management of data for an organization is crucial task but when data goes to its complex form then it becomes multifaceted as well as vital. In today era most of the organizations generating semi structured or unstructured data that requires special techniques to handle and manage. With the needs to handle unstructured data, semantic web technology provides a way to come up with the effective solution. In this chapter Synthetic Semantic Data Management (SSDM) is explained that is based semantic web technique and will helps to manage data of small and Midsized Enterprise (SME). SSDM provide the procedure to handle, store, manages and retrieval of semi structured data.

scholarly journals Cooperation of Business Intelligence and Big Data in one Ecosystem

2020 ◽  
Vol 83 ◽  
pp. 01008
Author(s):  
Matej Černý
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Business Intelligence ◽  
Structured Data ◽  
The Other ◽  
Unstructured Data ◽  
Data Types ◽  
Data Ecosystem

This paper is focused on the issue, how the business can analyze all data types (structured and unstructured) in one cooperative environment. With structured data handle Business Intelligence and with unstructured data on the other side Big Data. As a solution to this issue, we have suggested our Business Intelligence and Big Data ecosystem. This model - the ecosystem is based on already proven data processing processes running in Business Intelligence and in Big Data areas. Both processes are integrated into one unit. We have also described their common functioning.

scholarly journals Statistics based Optimization Technique for Query Processing

2019 ◽  
Vol 8 (10) ◽  
pp. 714-718
Keyword(s):  
Semantic Web ◽  
Optimization Technique ◽  
Optimization Method ◽  
Structured Data ◽  
Query Graph ◽  
User Input ◽  
Search Result ◽  
Query Recommendation ◽  
User Query ◽  
The Cost

Semantic web consists of the data in the structure manner and query searching methods can access these structured data to provide effective search result. The query recommendation in the semantic web relevance is needed to be improved based on the user input query. Many existing methods are used to improve the query recommendation efficiency using the optimization technique such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO). These methods involve in the use of many features which are selected from the user query. This in-turn increases the cost of a query in the semantic web. In this research, the query optimization was carried out by using the statistics method. The statistics based optimization method requires fewer features such as triple pattern and node priority etc., for finding the relevant results. The LUBM dataset contains the semantic queries and this dataset is used to measure the efficiency of the proposed Statistical based optimization method. The SPARQL queries are used to plot the query graph and triple scores are extracted from the graph. The cost value of the triple scores is measured and given as input to the proposed statistics method. The execution time of the statistics based optimization method for the query is 35 ms while the existing method has 48 ms.

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