=Paper=
{{Paper
|id=Vol-1747/BIT105_ICBO2016
|storemode=property
|title=A Web Application for Extracting Key Domain Information for Scientific Publications Using Ontology
|pdfUrl=https://ceur-ws.org/Vol-1747/BIT105_ICBO2016.pdf
|volume=Vol-1747
|authors=Weijia Xu,Amit Gupta,Pankaj Jaiswal,Crispin Taylor,Patti Lockhart
|dblpUrl=https://dblp.org/rec/conf/icbo/XuGJTL16a
}}
==A Web Application for Extracting Key Domain Information for Scientific Publications Using Ontology ==
https://ceur-ws.org/Vol-1747/BIT105_ICBO2016.pdf
A Web Application for Extracting Key Domain
Information for Scientific Publications using 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 demos of an ongoing project, domain We present application demos to illustrate this framework
informational vocabulary extraction (DIVE), which aims to using a sets of plant biology articles. We detail the design and
enrich digital publications through entity and key informational implementation of the system, including entity detection, the
words detection and by adding additional annotations. The extraction pipeline, and the web interface; we also present a use
system implements multiple strategies for biological entity case demonstration. We would like to engage publishers and
detection, including using regular expression rules, ontologies, biology data curators in discussion and feedback.
and a keyword dictionary. These extracted entities are then
stored in a database and made accessible through an interactive There are three major steps in processing the documents:
web application for curation and evaluation by authors. Through text extraction, entity candidate extraction, and candidate
the web interface, the user can make additional annotations and assessment. The input for the text extraction process is the
corrections to the current results. The updates can then be used structured document tagged by Journal Article Tag Suite
to improve the entity detection in subsequent processed articles. (JATS) [1]. The input document is processed into two data
Although the system is being developed in the context of
annotating journal articles, it can also be beneficial to domain
structures for textual data and structural data. This dual data
curators and researchers at large. structure allows for efficient text processing of the publication
content while still being able to easily retrieve the meta-
Keywords—component; Information systems applications; structure around a particular set of words during the
Information integration; Ontology; Text Mining subsequent steps of processing. To identify informational
vocabulary candidates, our application implemented four sets
I. INTRODUCTION of extraction rules: regular expression rules, word dictionary,
publishing convention, and ontology rules. The ontology rules
Due to its technical depth and rich informational content, a
journal article often requires that readers, domain experts, and utilize five biological ontologies including gene ontology [2],
curators invest significant amounts of time and effort to fully plant ontology [3], plant trait ontology [4], plant environment
comprehend and make intelligent use of its content. This can be condition ontology [5] and Chemical Entities of Biological
especially true in emerging areas, where novel ideas and new Interest (ChEBI) [6]. The results from document processing
terminologies may be presented without precedent. As new are stored in a MySQL database and serve as data storage for
technologies accelerating scientific discovery and more content the web application.
becomes available online, the number of new articles that must The web front end in our prototype is implemented using
be read and understood continues to rise. Therefore, there is a Django (v 1.8). Based on Python, the web front is easily
pressing need to develop computational methods and tools that programmable, extensible, and is pluggable with multiple
can enrich the information content of digital publications, popular databases. It forms the presentation layer of this
improve its accessibility and utility, and facilitate the readers’ system, relying on the back end code to run the entity
understanding by creating links between journal articles and extraction algorithms from the manuscript and to transfer the
relevant database entities during the article production process. results in a JSON format.
To address this issue, we present software developments from
The system can benefit the entire life cycle of the digital
an ongoing project, DIVE, which features auto extraction of
publication, from initial manuscript submission to publishing
informational vocabulary, web based access and curation tools,
the article and presenting information to readers. At the initial
and integration into the digital publication process.
manuscript submission stage, the manuscript can be processed
The framework implements several strategies in entity to extract known key informational vocabulary, such as
extraction, including using regular expression rules, ontology biological entities, as well as to identify potential new technical
and a keyword dictionary. The results of the extracted words. That information may be used by editors to identify
biological entities are then stored in a database and made appropriate reviewers for the manuscript. After the article has
accessible through an interactive web application for curation been accepted for publication, additional information about the
and evaluation by authors and other domain experts. Through key informational words, such as links to external repositories
the web interface, a user can make additional annotations and or reference sites, may also be embedded during the pre-
corrections to the initial result set. The updates are stored and publication production process to enrich the information
managed via the relational database for future improvements. content and accessibility. Publication curators may also
leverage the information for curation. New information defined The entity extraction phase can also generate phrases, while
and verified by experts may also be injected to other matching the ontology term alphabtetically, are not used for the
information resources, such as Planteome [7]. purpose implied by the term. Those situations requires expert
knolwedge and input.
II. APPLICATION FEATURES OVERVIEW Theorefore, each row also includes user control buttons for
Let us use an example to illustrate the features of our web editing the record. Figure 3 shows an example of the Entitiy
interface, thereby displaying the various views, layouts, and record editing interface. In this view, there are editable fields of
functions available. Our prototype includes 609 manuscripts this record where a user may correct or enter new values. A
from the journal Plant Physiology. dynamic search box can be used to search and add new species
into the species menu, if the appropriate species was not
detected or inferred from the article. This search box uses an
online service from NCBI to provide a very comprehensive list
of options as the user dynamically types into it. Sentences of
occurrence of this entity are extracted from the manuscript with
Figure 1. Collection paginated view
the entity name highlighted in yellow. This again provides
The publication list view (Figure 1) is a paginated list of all better, almost complete context information for this entity, as
the articles with an external DOI reference and the article title. per the manuscript text.
Figure 3. Interface for showing/editing entity details
The prototype is still under development, and we welcome
feedback from domain researchers and publishing
professionals for future developments and improvements.
ACKNOWLEDGMENT
Figure 2. Interface for exploring entities in a publication DIVE is partially supported by CyVerse (NSF award DBI-
Figure 2 shows example of exploring entities extracted 0735191 and DBI-1265383) and the Gramene, a Comparative
from a full article (i.e. 1002.xml in Figure 1). In the top of the Plant Genomics Database (NSF award IOS-1127112).
page, the title and abstract of the article [8] are presented to
give user some context of the manuscript. The list of entities REFERENCES
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