A Sampling of Practical Tools based on Semantic Web and

NLP technologies: Kino, ASEMR, SCOONER, and PREDOSEAmit Sheth and Kno.e.sis Team

Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing

Wright State University, Dayton, OH-45435

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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

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Kino Architecture

NCBO Ontology Access API

NCBO Ontology Repository

Kino Search APISOLRJ

Kino Index API

SOLR Web Interface

Lucene Index

Kino Browser PluginWeb Pages

Kino Web Front-end

Other Front -ends

NCBO REST ServiceKino Back-end

Kino browser based annotation

Kino Search Interfaces

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Example: Annotation of biomedical document (e.g. with Mesh, RadLex)

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• Search documents with the concept of interest.• Return all annotated documents with selected

concept.

Kino Search Engine

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Kino Demo

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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.

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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.

SCOONER Approach

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SCOONER Workflow

Base Hierarchy from Wikipedia Focused

Pattern based extraction

Domain specific keywords

Initial KB Creation

NLM: Rule based BKR Triples

Knoesis: Stanford parser extracted triples

PubMed Abstracts

Enrich Knowledge Base

Final Knowledge Base

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SCOONER Architecture

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SCOONER Demo

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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.

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In 2008, there were 14,800 prescription painkiller deaths*

*http://www.cdc.gov/homeandrecreationalsafety/rxbrief/

• 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 KerlikowskeDirector, ONDCP

Launched May 2011

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PREDOSE Architecture

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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.

Codes Triples (subject-predicate-object)

Suboxone used by injection, negative experience Suboxone injection-causes-Cephalalgia

Suboxone used by injection, amount Suboxone injection-dosage amount-2mg

Suboxone used by injection, positive experience Suboxone injection-has_side_effect-Euphoria

experience sucked

feel pretty damn good

didn’t do shit

feel great

Sentiment Extraction

bad headache

+ve

-ve

Triples

DOSAGE PRONOUN

INTERVAL Route of Admin.

RELATIONSHIPS SENTIMENTS

DIVERSE DATA TYPES

ENTITIES

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.

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.

Buprenorphine

subClassOf

bupe

Entity Identification

has_slang_term

SuboxoneSubutex

subClassOf

bupey

has_slang_term

Drug Abuse Ontology (DAO)83 Classes37 Properties

33:1 Buprenorphine24:1 Loperamide

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PREDOSE: Smarter Data through Shared Context and Data Integration

Ontology Lexicon Lexico-ontology Rule-based Grammar

ENTITIESTRIPLES

EMOTIONINTENSITYPRONOUN

SENTIMENT

DRUG-FORMROUTE OF ADM

SIDEEFFECT

DOSAGEFREQUENCY

INTERVAL

Suboxone, Kratom, Herion, Suboxone-CAUSE-Cephalalgia

disgusted, amazed, irritatedmore than, a, few of

I, me, mine, myIm glad, turn out bad, weird

ointment, tablet, pill, filmsmoke, inject, snort, sniffItching, 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)

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Role of Semantic Web and Ontologies

Data Type Semantic Web Technique Limitations of Other Approaches

Entity Ontology-driven Identification & Normalization

ML/NLP IR

Requires labeled data

Unpredictable term frequencies

Triple Schema-drivenDifficult to

develop language model

Requires entity disambiguation

Sentiment Ontology-assisted target entity resolution

Inconsistent data for parse trees or

rules

Diverse simple & complex slang

terms & phrases

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PREDOSE Demo

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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

http://bioportal.bioontology.org

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Community and Resources (NCBO)

http://www.bioontology.org/

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Browse through BioPortal

Find ontologies

Find tools and projects

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thank you, and please visit us at

http://knoesis.org/

Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled ComputingWright State University, Dayton, Ohio, USA

Kno.e.sis