Quality-Aware Subgraph Matching Over

Quality-Aware Subgraph Matching Over

Quality-Aware Subgraph Matching Over

Inconsistent Probabilistic Graph Databases

ABSTRACTS

Resource Description Framework (RDF) has been widely used in the Semantic Web to describe resources and their relationships. The RDF graph is one of the most commonly used representations for RDF data. However, in many real applications such as the data extraction/integration, RDF graphs integrated from different data sources may often contain uncertain and inconsistent information (e.g., uncertain labels or that violate facts/rules), due to the unreliability of data sources.

EXISTING SYSTEM

One straightforward method to solve the QA-gMatch problem is to offline enumerate all possible worlds of probabilistic RDF graph G, repair these possible worlds (via edge deletions), and obtain subgraphs with high quality scores (QA-gMatch query answers) from the repaired possible worlds. However, since there are an exponential number of repaired possible worlds, this method is very inefficient, or even infeasible, to directly repair/store/query on the materialized possible worlds, in terms of time and space costs.

DIS ADVANTAGES

  • It is challenging to efficiently process the QA-gMatch query.
  • Inefficient in terms of time and space.

PROPOSED SYSTEM

In this paper, we will propose effective pruning methods, namely adaptive label pruning (based on a cost model) and quality score pruning, to reduce the QAgMatch search space and improve the query efficiency.

We make the following contributions in this paper.

1) We propose the QA-gMatch problem in inconsistent probabilistic graphs, which, to our best knowledge, no prior work has studied.

2) We carefully design effective pruning methods, adaptive label and quality score pruning, specific for inconsistent and probabilistic features of RDF graphs.

3) We build a tree index over pre-computed data of inconsistent probabilistic graphs, and illustrate efficient QA-gMatch query procedure by traversing the index.

ADVANTAGES

  • Reduce the QAgMatch search space.
  • Improve the query efficiency.

MODULES

  • Adaptive Label Pruning
  • Quality Score Pruning
  • Index Construction
  • QA-g Match Query Procedure

SYSTEM REQUIREMENTS

H/W System Configuration:-

Processor - Pentium –III

  • RAM - 256 MB (min)
  • Hard Disk - 20 GB
  • Key Board - Standard Windows Keyboard
  • Mouse - Two or Three Button Mouse
  • Monitor - SVGA

S/W System Configuration:-

  • Operating System : Windows95/98/2000/XP
  • Application Server : Tomcat5.0/6.X
  • Front End : HTML, Jsp
  • Scripts : JavaScript.
  • Server side Script : Java Server Pages.
  • Database : MySQL 5.0
  • Database Connectivity : JDBC

Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891

#301, 303 & 304, 3rd Floor, AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702

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