Saturday 7 June 2014

CoRE: A Context-Aware Relation Extraction Method for Relation Completion



ABSTRACT:

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications
such as Entity Reconstruction and Data Enrichment. Given a semantic relation R, RC attempts at linking entity pairs between two entity lists under the relation R. To accomplish the RC goals, we propose to formulate search queries for each query entity a based on some auxiliary information, so that to detect its target entity b from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several
real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.
EXISTING SYSTEM:
This data is typically unstructured and naturally lacks any binding information (i.e., foreign keys). Linking this data clearly goes beyond the capabilities of current data integration systems. This motivated novel frameworks that incorporate information extraction (IE) tasks such as named entity recognition (NER) and relation extraction (RE). Those frameworks have been used to enable some of the emerging data linking applications such as entity reconstruction and data enrichment.
DISADVANTAGES OF EXISTING SYSTEM:
v The number of retrieved documents is expected to be prohibitively large and in turn, processing them incurs a large overhead.
v Those documents would include significant amount of noise, which might eventually lead to a wrong.

PROPOSED SYSTEM:
          Our goal is to formulate effective and efficient search queries based on RE methods. In general, given some semantic relation R (e.g., (Lecturer, University)), general RE tasks target at obtaining relation instances of the relation R from free text.
             we propose a novel Context-Aware Relation Extraction method (CoRE), which is particularly designed for the RC task.
CoRE recognizes and exploits the particular context of an RC task. Towards this, instead of representing a relation in the form of strict high-quality patterns, CoRE uses context terms, which we call relation-context terms (RelTerms). For example in Fig. 1b, CoRE searches the web for documents that contain each of the seed instance pairs and from those documents it learns some RelTerms such as “department” and “faculty”. Based on those RelTerms, CoRE can formulate a query such as “Bob Brown + (department OR faculty)” for the incomplete instance (Bob Brown, ?). From the returned documents, we can then obtain “UIUC” as the target entity.
ADVANTAGES OF PROPOSED SYSTEM:
v It allows more flexibility in formulating the search queries based on context terms instead of patterns.
v It seamlessly allows including any query entity as one of the context terms, which further improves the chances of finding a matching target rather than lowering it.

SYSTEM ARCHITECTURE:


SYSTEM CONFIGURATION:-



HARDWARE REQUIREMENTS:-

ü Processor                  -        Pentium –IV
ü Speed                        -        1.1 Ghz
ü RAM                         -        512 MB(min)
ü Hard Disk                 -        40 GB
ü Key Board                -        Standard Windows Keyboard
ü Mouse                       -        Two or Three Button Mouse
ü Monitor                     -        LCD/LED

 

SOFTWARE REQUIREMENTS:

         Operating system :         Windows XP.
         Coding Language :         .Net
         Data Base             :         SQL Server 2005
         Tool                     :         VISUAL STUDIO 2008.

REFERENCE:
Zhixu Li, Mohamed A. Sharaf, Laurianne Sitbon, Xiaoyong Du, and Xiaofang Zhou, “CoRE: A CONTEXT-AWARE RELATION EXTRACTION METHOD FOR RELATION COMPLETION” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 4, APRIL 2014

No comments:

Post a Comment