Abstract
Our research project is currently to develop an Automatic Concept Relation Extraction (ACRE) System which automatically extracts concepts and their relationships across texts in all domains of knowledge. Concept Relational Tree (CRT) is one of the text analyzer applications used in the ACRE System to automatically extract concepts and their relationships in a document. To check on the correctness of the extraction of concepts and their relationships, the PTree is designed to reconstruct the text by reverse input. In this paper we present the PTree tool to test the accuracy of the automatic tagging and tree structure created by CRT from texts. The PTree tool is implemented from Java Universal Network/ Graph Framework (JUNG) libraries. This tool provides a few functions to allow for flexibility in drawing parse trees for concept relationships. Due to its flexibility and dynamic features, PTree can be further extended for use in the deconstruction of highly complex texts.
Highlights
In this paper we present the PTree tool to test the accuracy of the automatic tagging and tree structure created by Concept Relational Tree (CRT) from texts
As a result of CRT, a newly improved parse tree which we call the Concept Relational Parser Tree (CRPT) is built that works on the simple structure that each concept is linked to another, whether in the position of agent or object, by a connector (Ungku Chulan et al, 2008).The connector links one level of the tree to the
As a result of the traversal, the reconstructed sentence will be displayed in the text area. This tool has a menu bar which contains “Save as Image”, “Print” and “Exit” function. This PTree tool was developed to test the accuracy of CRT in the Automatic Concept Relation Extraction (ACRE) system in extracting concepts and relations from sentences
Summary
In today’s information explosion it has become more and more necessary to automatically enable the extraction of main concepts and relationships between concepts in a dynamic growing knowledge schema from documents www.ccsenet.org/cis. The ACRE System is an ongoing development of a system that extracts concepts and their relationships automatically across domains of knowledge. At the present stage of development, ACRE is able to partially extract concepts automatically and the relationships between these concepts from a collection of documents. Still in the early stage of development, ACRE shows potential contribution into research on automatic extraction of concepts and their relationship, in lieu of the more time consuming method of machine learning or rule based algorithm, or the laborious process of expert-dependent input
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