Domain case study: successful application of Semantic Web technologies and tools in Biomedicine and Health
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Domain case study: successful application of Semantic Web technologies and tools in Biomedicine and Health

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The talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk showcased demos of successful applications that use semantic......

The talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013. The talk showcased demos of successful applications that use semantic web technologies in several research problems.

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  • With the first animation :The annotation process starts by highlighting words, phrases or links in the web documents. When the appropriate menus are clicked, the user is presented with a the search results from NCBO as well as the options to browse for a desired concept. When the concept is selected, the in-memory document is modified to attach the annotation.With the second animation:Once the document is fully annotated, it can be submitted via the plugin, to the Kino back-end instance. The index APIfilters out the annotations, makes lookups for synonyms for the concept via NCBO and finally indexes the document using the Apache SOLR engine. The complete set of front-end and back-end functions are exposed via APIs so that programs (other than the plugin and the front-end) can make use of the Kino system to directly submit and retrieve content.Third animation:During lookups, the users can use the AjaX front-end to lookup documents using concept names, synonyms with concept names or just plain keywords to look up documents. They can filter the results using facets and restrict the search for specific annotations.
  • A Solution to Information OveAn ontology is an explicit representation of a sharedunderstanding of important concepts in some domain of interest (Kalfoglou, 2001).So, mostly static blocks of well accepted and consensual knowledge.Used in data integration, knowledge management, and decision support.rload
  • INTENSITY – more than, abnormal, in excess of, too muchDRUG-FORM – ointment, tablet, pill, filmINTERVAL – for several years


  • 1. A Sampling of Practical Tools based on Semantic Web and NLP technologies: Kino, ASEMR, SCOONER, and PREDOSE Amit Sheth and Kno.e.sis Team Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, OH-45435
  • 2. Kino : A Semantic Annotation Tool • An integrated suite of tools to annotate unstructured resources. • Uses NCBO ontologies, via the NCBO Web API. • Includes three main components – NCBO integrated front-end • to allow convenient annotation – Browser plug-in • to submit annotated web documents – Annotation aware back-end • to provide faceted search capabilities 2
  • 3. Kino Architecture Kino browser based annotation Kino Search Interfaces Web Pages Kino Browser Plugin Kino Web Frontend Other Front ends Kino Search API SOLRJ Kino Index API NCBO Ontology Access API SOLR Web Interface NCBO Ontology Repository Lucene Index NCBO REST Service Kino Back-end 3
  • 4. Example: Annotation of biomedical document (e.g. with Mesh, RadLex) 4
  • 5. Kino Search Engine • Search documents with the concept of interest. • Return all annotated documents with selected concept. 5
  • 6. Kino Demo 6
  • 7. SCOONER – A Tool for Semantic Browsing and Knowledge Exploration • NCBI’s PubMed Web Service has over 20 million citations (abstracts), and is growing rapidly. • A study estimates physicians in the specialty epidemiology will have to spend 21 hours per day to stay current (Gillam 2009). • Keyword based search is not sufficient. • Knowledge–based search systems are necessary. 7
  • 8. SCOONER Approach 1. Carve a focused domain hierarchy out of Wikipedia. 2. Extract mentions of entities and relationships in the relevant scientific literature (Pubmed abstracts) to support non-hierarchical guidance. 3. Map extracted entity mentions to concepts, and extracted predicates to relationships to create the knowledge-base. 4. Facilitate search and browsing over research literature guided by the knowledge-base. 8
  • 9. SCOONER Workflow Domain specific keywords Base Hierarchy from Wikipedia PubMed Abstracts Focused Pattern based extraction Initial KB Creation Knoesis: Stanford parser extracted triples NLM: Rule based BKR Triples Enrich Knowledge Base Final Knowledge Base 9
  • 10. SCOONER Architecture 10
  • 11. SCOONER Demo 11
  • 12. PREDOSE: Prescription Drug abuse Online-Surveillance and Epidemiology • Using Semantic Web technology to enhance NLP and IR techniques to understand drug abuse in online user communities. • Bridge the gap between researcher and policy makers. • Capable of early identification of emerging trends in abuse. 12
  • 13. • Drug Overdose Problem in US • 100 people die everyday from drug overdoses • 36,000 drug overdose deaths in 2008 • Close to half were due to prescription drugs Gil Kerlikowske Director, ONDCP Launched May 2011 In 2008, there were 14,800 prescription painkiller deaths* 13 *
  • 14. PREDOSE Architecture 14
  • 15. Entity Identification Drug Abuse Ontology (DAO) subClassOf subClassOf +ve 83 Classes 37 Properties Suboxone Subutex Sentiment Extraction experience sucked has_slang_term bupey feel great -ve Buprenorphine has_slang_term feel pretty damn good 33:1 Buprenorphine 24:1 Loperamide bupe didn’t do shit bad headache I was sent home with 5 x 2 mg Suboxones. I also got a bunch of phenobarbital (I took all 180 mg and it didn't do shit except make me a walking zombie for 2 days). I waited 24 hours after my last 2 mg dose of Suboxone and tried injecting 4 mg of the bupe. It gave me a bad headache, for hours, and I almost vomited. I could feel the bupe working but overall the experience sucked. Of course, junkie that I am, I decided to repeat the experiment. Today, after waiting 48 hours after my last bunk 4 mg injection, I injected 2 mg. There wasn't really any rush to speak of, but after 5 minutes I started to feel pretty damn good. So I injected another 1 mg. That was about half an hour ago. I feel great now. Triples DIVERSE DATA TYPES ENTITIES Codes Triples (subject-predicate-object) DOSAGE PRONOUN Suboxone used by injection, negative experience Suboxone injection-causes-Cephalalgia INTERVAL Route of Admin. Suboxone used by injection, amount Suboxone injection-dosage amount-2mg RELATIONSHIPS SENTIMENTS Suboxone used by injection, positive experience Suboxone injection-has_side_effect-Euphoria 15
  • 16. PREDOSE: Smarter Data through Shared Context and Data Integration Ontology Lexicon Lexico-ontology Rule-based Grammar ENTITIES TRIPLES EMOTION INTENSITY PRONOUN SENTIMENT DRUG-FORM ROUTE OF ADM SIDEEFFECT DOSAGE FREQUENCY INTERVAL Suboxone, Kratom, Herion, Suboxone-CAUSE-Cephalalgia disgusted, amazed, irritated more than, a, few of I, me, mine, my Im glad, turn out bad, weird ointment, tablet, pill, film smoke, inject, snort, sniff Itching, blisters, flushing, shaking hands, difficulty breathing DOSAGE: <AMT><UNIT> (e.g. 5mg, 2-3 tabs) FREQ: <AMT><FREQ_IND><PERIOD> (e.g. 5 times a week) INTERVAL: <PERIOD_IND><PERIOD> (e.g. several years) 16
  • 17. Role of Semantic Web and Ontologies Data Type Semantic Web Technique Entity Ontology-driven Identification & Normalization Triple Schema-driven Sentiment Ontology-assisted target entity resolution Limitations of Other Approaches ML/NLP IR Requires labeled Unpredictable data term frequencies Difficult to develop language model Requires entity disambiguation Inconsistent data Diverse simple & for parse trees or complex slang rules terms & phrases 17
  • 18. PREDOSE Demo 18
  • 19. BioPortal – A successful community effort • An web open repository of biomedical ontologies – “one stop shop”. • Users can visualize, browse, search, publish, comment, align ontologies, and use them for annotations. • Statistics – 363 ontologies – 5.8 million classes – 24 billion annotations 19
  • 20. Community and Resources (NCBO) 20
  • 21. Browse through BioPortal Find ontologies Find tools and projects 21
  • 22. Kno.e.sis thank you, and please visit us at Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA 22