NDLI: Learning to Match Heterogeneous Structures using Partially Labeled Data

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Content Provider	ACM Digital Library
Author	Tang, Enya Kong Soon, Lay-Ki Tan, Saravadee Sae Lim, Tek Yong
Abstract	This paper addresses the problem of matching between highly heterogeneous structures. The problem is modeled as a classification task where training examples are used to learn the matching between structures. In our approach, training is performed using partially labeled data. We propose a Greedy Mapping approach to generate training examples from partially labeled data. Different types of structures may have different types of attributes that can be exploited to enhance the matching problem. We utilize three types of attributes, namely, text content, structure name and path correspondence, in the matching problem. Experiments are performed on two types of structures: semantic domain and semantic role. We evaluate the effectiveness of the Greedy Mapping as well as the performance on different types of attributes. Finally, the results are presented and discussed.
Starting Page	45
Ending Page	48
Page Count	4
File Format	PDF
ISBN	9781450316064
DOI	10.1145/2663792.2663797
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Heterogeneous structure Structure matching
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Learning to Match Heterogeneous Structures using Partially Labeled Data