=Paper=
{{Paper
|id=Vol-1747/D106-BP04_ICBO2016
|storemode=property
|title=Enhancing Information Accessibility of Publications with Text Mining and Ontology
|pdfUrl=https://ceur-ws.org/Vol-1747/D106-BP04_ICBO2016.pdf
|volume=Vol-1747
|authors=Weijia Xu,Amit Gupta,Pankaj Jaiswal,Crispin Taylor,Patti Lockhart
|dblpUrl=https://dblp.org/rec/conf/icbo/XuGJTL16
}}
==Enhancing Information Accessibility of Publications with Text Mining and Ontology ==
https://ceur-ws.org/Vol-1747/D106-BP04_ICBO2016.pdf
Enhancing Information Accessibility of Scientific
Publications with Text Mining and Ontology
Weijia Xu Amit Gupta Pankaj Jaiswal Crispin Taylor Patti Lockhart
Texas Advanced Computing Center Department of Botany and Plant Pathology American Society of Plant Biologists
University of Texas at Austin Oregon State University Rockville, Maryland, USA
Austin, Texas USA Oregon, Portland, USA {ctaylor, plockhart}@aspb.org
{xwj, agupta}@tacc.utexas.edu jaiswalp@oregonstate.edu
Abstract— We present an ongoing effort on utilizing text mining
methods and existing biological ontologies to help readers to II. METHODS AND IMPLEMENTAIONTS
access the information contained in the scientific articles. Our
approach includes using multiple strategies for biological entity A. Entity Detection Workflow
detection and using association analysis on extracted analysis.
The entity extraction processes utilizes regular expression rules,
ontologies, and keyword dictionary to get a comprehensive list of
biological entities. In addition to extract list of entities, we also
apply natural language processing and association analysis
techniques to generate inferences among entities and comparing
to known relations documented in the existing ontologies.
Keywords—component; Information systems applications;
Ontology; Text Mining; Association Analysis
I. INTRODUCTION
Due to its technical depth and rich, informational content, a
journal article often requires that readers, domain experts, and
Figure 1. Processing workflow overview
curators invest significant amounts of time and effort to fully
comprehend and make intelligent use of its content. This can be Figure 1 shows the overview of our processing workflow.
especially true in emerging areas, where novel ideas and new There are three major steps in processing the documents: text
terminologies may be presented without precedent. As new extraction, entity candidate extraction, and candidate
technologies accelerating scientific discovery and more content assessment.
becomes available online, the number of new articles that must 1) Text Extraction
be read and understood continues to rise. There are over 22 The text extraction process the input structured document
millions references indexed by MEDLINE. Therefore, there is tagged by JATS [1]. During this step, the input document will
a pressing need to develop computational methods and tools be processed into two data structures for textual data and
that can facilitate the readers’ understanding of the content of structural data. The textual information data includes as a list
the publicaiton. of string representation of the body of text included in the
To address this challange, we present software journal articles. The structural data includes the metadata
developments from an ongoing project, DIVE, which features information presented at the input document, such as section
auto extraction of informational vocabulary, web based access mark, special formatting mark etc. A mapping is maintained
and curation tools. The framework implements several between the textual value and metadata information by their
strategies in entity extraction, including using regular global positions in the original documents. This dual data
expression rules, ontology and a keyword dictionary. The structure allows for efficient text processing of the publication
results of the extracted biological entities are then stored in a content while still being able to easily retrieve the meta
database and made accessible through an interactive web structure around a particular set of words during the
application for curation and evaluation by authors and other subsequent steps of processing.
domain experts. Additional text mining and associaiton
analysis can be run on extrated entities to help readers 2) Entity Candidate Detection
understanding of the paper. The system can benefit the entire We implemented a rule-based approach for processing the text
life cycle of the digital publication, from initial manuscript and structure in order to identify informational vocabulary
submission to publishing the article and presenting information candidates. The detection rules can be defined based on
to readers. New information defined and verified by experts various heuristics and requirements such as publishing
may also be injected to other information resources. requirements, naming conventions, and domain ontologies.
New rules can be added on demand over time. Currently, there Such visual representations of inferred association between
are four types of rules implemented in the DIVE, regular diverse entity types could tremendously aid a researcher in
expression rules, word dictionary, publishing convention, and forming insights. This also has potential to be a similarity
ontology rules. metric between articles that could help editors gauge the
The regular expression rules utilize common naming novelty of a new article submission.
conventions to identify biological entities, such as gene name,
protein name, molecule structures, chemical compound, etc.
Each rule can be defined as a regular expression and used for
matching the candidate word. The word dictionary rule
consists of a pre-defined list of words that should be included
or excluded in the candidate lists. The publication content is
searched against the list at run time. The publishing
convention rules are used to identify words that are in special
format, such as in italic, or in a particular component of the
publication, such as a figure legend. The enclosing tags of the
candidates are used to define each rule. Additional rules can
be added by specifying additional tag values or by using
naming conventions to detect entities like species names. The
ontology rules utilize five biological ontology including gene
ontology [2], plant ontology [3] plant trait ontology [4], plant
environment condition ontology [5] and Chemical Entities of
Biological Interest (ChEBI) [6].
Figure 2 Top 20 inference rules from association analysis.
3) Entity Candidate Assessment
By applying the extraction rules listed above, a set of entity
candidates can be detected from the input document. Some We are continuing working on evaluating the performance of
candidates might be detected by multiple rules. Different the entity extraction over large data set and improving its
detection rules also have different accuracy. Ontology file and accuracy. We are gathering feedback from domain researchers
dictionary based approaches have the highest certainty. and publishing professionals for further entities candidate
Candidates only identified by other rules need further evaluations. We are also working on comparing the inferences
validation. We currently implemented two automatic from association analysis with known relationships
validation mechanisms. One is based on the previously documented in the existing ontologies.
validated results; the other one is based on co-location with
ACKNOWLEDGMENT
other confirmed entities. However, the primary method of
validation is by domain expert evaluation through the web DIVE is partially supported by CyVerse (NSF award DBI-
interface, which is detailed in the following section. 0735191 and DBI-1265383) and the Gramene, a Comparative
Plant Genomics Database (NSF award IOS-1127112).
B. Association Analysis
The data association analysis can be used to generate REFERENCES
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