Authors:
Martha O. Perez-Arriaga
;
Trilce Estrada
and
Soraya Abad-Mota
Affiliation:
University of New Mexico, United States
Keyword(s):
Table Understanding, Information Extraction, Information Integration, Semantic Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Collaboration and e-Services
;
Data Engineering
;
Data Management and Quality
;
e-Business
;
Enterprise Information Systems
;
Information Integration
;
Information Quality
;
Integration/Interoperability
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Semi-Structured and Unstructured Data
;
Symbolic Systems
;
Text Analytics
Abstract:
The large number of scientific publications produced today prevents researchers from analyzing them rapidly.
Automated analysis methods are needed to locate relevant facts in a large volume of information. Though
publishers establish standards for scientific documents, the variety of topics, layouts, and writing styles impedes
the prompt analysis of publications. A single standard across scientific fields is infeasible, but common
elements tables and text exist by which to analyze publications from any domain. Tables offer an additional
dimension describing direct or quantitative relationships among concepts. However, extracting tables information,
and unambiguously linking it to its corresponding text to form accurate semantic relationships are
non-trivial tasks. We present a comprehensive framework to conceptually represent a document by extracting
its semantic relationships and context. Given a document, our framework uses its text, and tables content
and structure to ide
ntify relevant concepts and relationships. Additionally, we use the Web and ontologies
to perform disambiguation, establish a context, annotate relationships, and preserve provenance. Finally, our
framework provides an augmented synthesis for each document in a domain-independent format. Our results
show that by using information from tables we are able to increase the number of highly ranked semantic
relationships by a whole order of magnitude.
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