Automated extraction of concept matcher thesaurus from semi-structured catalogue-like sources of data on the web

2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT) Pub Date : 2016-04-18 DOI:10.1109/FRUCT-ISPIT.2016.7561521

Maxim Lapaev

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引用次数: 1

Abstract

Ontology design and the process of populating a data-set with knowledge following the chosen or developed ontology to fit the principles of Semantic Web and Linked Open Data is a time-consuming and iterative process, requiring either expert knowledge or a set of tools for data scraping from web. A valid and consistent ontology and knowledge withing the data-set require unification of concepts which means overcoming ambiguity and synonymy of terms which become individuals of ontology. In this paper we spot on techniques used for organising a Russian food product data-set under a light-weight FOOD Ontology and concept matching in particular. Main approaches to data-set concept unification, synonymic term matching and ways to collect dictionaries for matcher are mentioned. The tool for catalogue-like semi-structured resources parsing and thesaurus extraction is developed and introduced for the task of on-the-fly concept matching.

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从网络上半结构化的类目录数据源自动提取概念匹配词库

本体设计和按照选定或开发的本体向数据集填充知识的过程是一个耗时且反复的过程，既需要专家知识，也需要一套从Web上抓取数据的工具。一个有效的、一致的本体论和数据集中的知识需要概念的统一，即克服成为本体论个体的术语的模糊性和同义性。在本文中，我们重点介绍了在轻量级食品本体和概念匹配下组织俄罗斯食品数据集的技术。介绍了数据集概念统一的主要方法、同义词匹配的主要方法以及为匹配器收集字典的主要方法。针对动态概念匹配任务，开发并引入了类目录半结构化资源解析和同义词库提取工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT)

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