Ontology-based Information Extraction from Technical Documents

Syed Tahseen Raza Rizvi; Dominique Mercier; Stefan Agne; Steffen Erkel; Andreas Dengel; Sheraz Ahmed
In: International Conference on Agents and Artificial Intelligence. International Conference on Agents and Artificial Intelligence (ICAART), 10th International Conference on Agents and Artificial Intelligence, January 16-18, Funchal, Madeira, Portugal, SCITEPRESS, 2018.


This paper presents a novel system for extracting user relevant tabular information from documents. The presented system is generic and can be applied to any documents irrespective of their domain and the information they contain. In addition to the generic nature of the presented approach, it is robust and can deal with different document layouts followed while creating those documents. The presented system has two main modules; table detection and ontological information extraction. The table detection module extracts all tables from a given technical document while, the ontological information extraction module extracts only relevant tables from all of the detected tables. The generalization in this system is achieved by using ontologies, thus enabling the system to adapt itself, to a new set of documents from any other domain, according to any provided ontology. Furthermore, the presented system also provides a confidence score and explanation of the score for each of the extracted tables in terms of its relevancy. The system was evaluated on 80 real technical documents of hardware parts containing 2033 tables from 20 different brands of Industrial Boilers domain. The evaluation results show that the presented system extracted all of the relevant tables and achieves an overall precision, recall, and F-measure of 0.88, 1 and 0.93 respectively.

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