Information Extraction from the Web by Matching Visual Presentation Patterns

Information Extraction from the Web by Matching Visual Presentation Patterns

Paper in proceedings (conference paper)

The documents available in the World Wide Web contain large amounts of information presented in tables, lists or other visually regular structures. The published information is however usually not annotated explicitly or implicitly and its interpretation is left on a human reader. This makes the information extraction from web documents a challenging problem. Most existing approaches are based on a top-down approach that proceeds from the larger page regions to individual data records, which depends on different heuristics. We present an opposite bottom-up approach. We roughly identify the smallest data fields in the document and later, we refine this approximation by matching the discovered visual presentation patterns with the expected semantic structure of the extracted information. This approach allows to efficiently extract structured data from heterogeneous documents without any kind of additional annotations as we demonstrate experimentally on various application domains.

The documents available in the World Wide Web contain large amounts of information presented in tables, lists or other visually regular structures. The published information is however usually not annotated explicitly or implicitly and its interpretation is left on a human reader. This makes the information extraction from web documents a challenging problem. Most existing approaches are based on a top-down approach that proceeds from the larger page regions to individual data records, which depends on different heuristics. We present an opposite bottom-up approach. We roughly identify the smallest data fields in the document and later, we refine this approximation by matching the discovered visual presentation patterns with the expected semantic structure of the extracted information. This approach allows to efficiently extract structured data from heterogeneous documents without any kind of additional annotations as we demonstrate experimentally on various application domains.

web data integration, information extraction, structured record extraction, page segmentation, content classification, ontology mapping

web data integration, information extraction, structured record extraction, page segmentation, content classification, ontology mapping

BURGET, R.

2018

29.10.2017

Springer International Publishing

Kobe

978-3-319-68722-3

Knowledge Graphs and Language Technology: ISWC 2016 International Workshops: KEKI and NLP&DBpedia

Lecture Notes in Computer Science vol. 10579

10

26

17

https://link.springer.com/chapter/10.1007/978-3-319-68723-0_2

burget_dbpedia_springer
burget_keki_2017