Authors:
Syed Tahseen Raza Rizvi
1
;
Dominique Mercier
2
;
Stefan Agne
3
;
Steffen Erkel
4
;
Andreas Dengel
3
and
Sheraz Ahmed
3
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI) and Kaiserslautern University of Technology, Germany
;
2
Kaiserslautern University of Technology, Germany
;
3
German Research Center for Artificial Intelligence (DFKI), Germany
;
4
Bosch Thermo-technology, Germany
Keyword(s):
Table Detection, Information Extraction, Ontology, PDF Document, Document Analysis, Table Extraction, Relevancy.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Enterprise Ontologies
;
Formal Methods
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Ontologies
;
Semantic Web
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
Abstract:
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 extract
ed 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.
(More)