This document outlines a presentation on developing a suite of semantically interoperable biomedical natural language processing (BioNLP) services using the Semantic Automated Discovery and Integration (SADI) framework. The presentation covers the motivation for the work due to the large amount of biomedical literature being produced, an overview of the proposed system architecture and components, including ontology development and SADI services, and a demonstration of the system. Experimental results showing the system can accurately extract requested information from text are also discussed. The presentation concludes by discussing future work to improve system performance.

1. Google Project Page: https://code.google.com/p/bionlp-sadi/
Project Demo Page: https://cbakerlab:8080/p/bionlp-sadi/
Presenter: Ahmad C. Bukhari
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2.  Motivation and Introduction
 Past Research Work
 Proposed Methodology
 System architecture
 System design
 Ontology Development
 SADI Service development
 Demo and code view
 Experiments and Results
 Conclusion and Future work
 References
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3.  Scientific literature, the most updated source of information
 Explosive growth observed in scientific literature
production
 Internet is full of Bio related databases and search
engines
 Text formats are provided by PubMed and OMIM.
 Sequence data is provided by GenBank, in terms of DNA, and UniProt,
in terms of protein.
 Protein structures are provided by PDB, SCOP, and CATH.
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4.  Thousands of documents produced weekly : Impossible to read all
the published documents
 Several solution developed based on AI techniques
 Lost significant due to new terms developed and static mechanism
 NLP emerged as possible solution in past decade
 NLP was widely adopted by scientists
 Several applications are available on internet based on NLP
techniques
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5.  We Introduced semantically rich interoperable suite of BioNLP
services based on SADI framework.
 Exploits the NLP technologies in order to extract the
biological useful information from scientific documents.
 Can present the extracted information in such fashion that it
would be reusable, searchable and interoperable.
 Can display the output in integrated format which further can
lead for better bio system analysis
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6. Existing text mining services
Existing text mining services with web services
•U-Compare
•Whatizit
•EBIMED
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7.  Scientific community looking for sophisticated solution which can
handle Biological data interoperability, usability and integration
challenges.
 We coupled the useful biological NLP techniques with SADI
framework to cope the biological information logistics
issues.
 Proposed solution exploit the NLP technologies to extract
bio worthy info. With semantic support
 Proposed solution provides output in reusable; searchable
and interoperable format
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8. User Interaction Layer
SADI services suite
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9. REST, XML, SOAP, or WSDL
XML, RDF, OWL, RDFS
SWS+BNLP
KLEIO
U-Compare NLP +WS = XML output
GENIA
FACTA+
etc 9

10. 10

11. 11

12. Deal with Annotation
All document related concepts
Feature Modeling
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13. 13

14.  mutationFinder
 DrugExtractor (enhanced)
 DrugDrug Interaction (80% complte)
 Drug2Food Interaction (Business logic
complte)
 Pmid2pdf (enhanced)
 Pdf2ascii (upgraded overall) // A lot bug in
existing
 SADI client level integration service
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15. Tools and technologies used
•Java
•Servlet
•RDF
•SPARQL
•JSP
•JSF
•Javascript
•XHTML
•And several
third party
libraries
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16. Demo and Code View
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17. 17

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19.  Show where the drug Amoxicillin (DB01060 )
positive effect against higher serum levels
 Give me the sentence where mutation and
drug name occur in the same sentence.
 Extract all the drug names from text and
show me the interaction (if exist) among all
the drugs
 Tell me the food which have bad interaction
with drug Cytarabine
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20. Consolidated Output Generated By system
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21.  Proposed a generalized architecture : semantic interoperability
and integration among BNLP tools
 Performed several experiments by designing different corpora’s
and by choosing different combination of services
 In most of the cases: system generated the results according to
our requirements
 . AS a future work, we will try to enhance the performance of the
system by refining the algorithms
 A registry feature will be added to give user more freedom to work.
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22.  Topic Finding
 Limited availability of tools
 Development challenges (countless)
 Integration with web
 Finding case study (still have)
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23.  E. Gatial, Z. Balogh, M. Ciglan, L. Hluchy, Focused web crawling mechanism based on page relevance, In: Proc
eedings of (ITAT 2005) information technologies applications and theory, 2005, pp. 41–45
 F.N Natalya, LM Deborah, Ontology development 101: a guide to creating your first ontology. http://protege.s
tanford.edu/publications/ontology_development/ontology101-noy-mcguinness.htm
 H. Cunningham, Y. Wilks, R. J. Gaizauskas, GATE, a General Architecture for Text Engineering. Computers and
humanities (2002), 1057-1060.
 R. Subhashini, V.J.S Kumar, Shallow NLP techniques for noun phrase extraction, In: Proceeding of Trendz in Inf
ormation Sciences & Computing (TISC), 2010 , pp.73-77.
 S. Nasrolahi, M. Nikdast, M. Boroujerdi, The semantic web: a new approach for future world wide web, In: Pro
ceedings of World Academy of Science, Engineering and Technology, 2009, pp. 1149-1154
 A.C. Bukhari, Y.G Kim, Exploiting the Heavyweight Ontology with Multi-Agent System Using Vocal Command
System: A Case Study on E-Mall, International Journal of Advancements in Computing Technology 3(2011) 233
-241.
 A.C. Bukhari, Y.G Kim, Ontology-assisted automatic precise information extractor for visually impaired inhabit
ants, Artificial Intelligence Review (2005) Issn: 0269-2821.
 D.H. Fudholi, N. Maneerat, R. Varakulsiripunth, Y. Kato, Application of Protégé, SWRL and SQWRL in fuzzy on
tology-based menu recommendation, International Symposium on Intelligent Signal Processing and Commu
nication Systems, 2009, pp. 631-634.
 Baumgartner WA, Cohen KB, Fox L, Acquaah-Mensah G, Hunter L: Manual annotation is not sufficient for cura
ting genomic databases.
 Bioinformatics 2007, 23:i41-i48. PubMed Abstract | Publisher Full Text | PubMed Central Full Text
 Laurilla J, Naderi N, Witte R, Riazanov A, Kouznetsov A, Baker CJO: Algorithms and semantic infrastructure for
mutation impact extraction and grounding.
 BMC Genomics 2010, 11(Suppl 4):S24. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text
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24. Many Thanks
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