research-article

Extraction of Address Data from Unstructured Text using Free Knowledge Resources

Authors:

Sebastian Schmidt,

Simon Manschitz,

Christoph Rensing,

Ralf SteinmetzAuthors Info & Claims

i-Know '13: Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies

Article No.: 7, Pages 1 - 8

https://doi.org/10.1145/2494188.2494193

Published: 04 September 2013 Publication History

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Abstract

The Web is populated with many Web sites containing unstructured textual information. These Web sites are a source of knowledge for various interests. As semantic annotations are only rarely used on Web sites, an automated harvesting of the knowledge without additional effort is not possible. Thus, elaborated approaches for information extraction are required. In our work we face the challenge of identifying business address data on Web sites since we see the need for this data in various applications. In order to accomplish our aim, we have developed a hybrid approach combining patterns and gazetteers obtained from freely available knowledge resources such as OpenStreetMap. Experimental evaluation on a corpus of heterogeneous Web sites shows a high recall and precision. The approach can be adapted for identification of addresses considering the different formats in various countries.

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Extraction of Address Data from Unstructured Text using Free Knowledge Resources

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Published In

i-Know '13: Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies

September 2013

271 pages

ISBN:9781450323000

DOI:10.1145/2494188

Editors:
Stefanie Lindstaedt
Know-Center Graz & Graz University of Technology, Austria
,
Michael Granitzer
University of Passau, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 04 September 2013

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i-Know '13

i-Know '13: 13th International Conference on Knowledge Management and Knowledge Technologies

September 4 - 6, 2013

Graz, Austria

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i-Know '13 Paper Acceptance Rate 27 of 87 submissions, 31%;

Overall Acceptance Rate 77 of 238 submissions, 32%

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Cited By

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Using domain knowledge for ontology-guided entity extraction from noisy, unstructured text data

A Flexible Text Mining System for Entity and Relation Extraction in PubMed

LODsyndesisIE: Entity Extraction from Text and Enrichment Using Hundreds of Linked Datasets

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${LODsyndesis}_{IE}$ : Entity Extraction from Text and Enrichment Using Hundreds of Linked Datasets