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1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
QUALITY-AWARE SUBGRAPH MATCHING OVER INCONSISTENT
PROBABILISTIC GRAPH DATABASES
ABSTRACT:
Resource Description Framework (RDF) has been widely used in the
SemanticWeb 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. In this paper, we formalize the RDF data by
inconsistent probabilistic RDF graphs, which contain both inconsistencies and
uncertainty. With such a probabilistic graph model, we focus on an important
problem, quality-aware subgraph matching over inconsistent probabilistic RDF
graphs (QA-gMatch), which retrieves subgraphs from inconsistent probabilistic
RDF graphs that are isomorphic to a given query graph and with high quality
scores (considering both consistency and uncertainty). In order to efficiently
answer QA-gMatch queries, we provide two effective pruning methods,
namely adaptive label pruning and quality score pruning, which can greatly
filter out false alarms of subgraphs.We also design an effective index to
facilitate our proposed pruning methods, and propose an efficient approach
for processingQA-gMatch queries. Finally, we demonstrate the efficiency and
effectiveness of our proposed approaches through extensive experiments.

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
CONCLUSIONS
In this paper, we study an important QA-gMatch problem, which retrieves
those consistently matching subgraphs from inconsistent probabilistic data
graphs with the guarantee of high quality scores. To tackle the problem, we
specifically design effective pruning methods, adaptive label pruning and
quality score pruning, for reducing the search space. Further, we build an
effective index to facilitate the QAgMatch processing. We conducted extensive
experiments to verify the efficiency and effectiveness of our approaches.
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3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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