Ontologies in the BioMedical Domain Joanne S. Luciano, PhD Predictive Medicine, Inc., Belmont, MA (predmed.com) Rensselaer Polytechnic Institute, Troy, NY (twc.rpi.edu) Semantic Computing in Healthcare Industry September.

Transcript Ontologies in the BioMedical Domain Joanne S. Luciano, PhD Predictive Medicine, Inc., Belmont, MA (predmed.com) Rensselaer Polytechnic Institute, Troy, NY (twc.rpi.edu) Semantic Computing in Healthcare Industry September.

Ontologies in the BioMedical Domain

Joanne S. Luciano, PhD

Predictive Medicine, Inc., Belmont, MA (predmed.com) Rensselaer Polytechnic Institute, Troy, NY (twc.rpi.edu) Semantic Computing in Healthcare Industry September 16-18, 2013 Hyatt Regency Irvine, Irvine, CA, USA 4/29/2020 1

PRESENTER

Joanne S. Luciano

Email: [email protected]

[email protected]

Enable Health and Wellbeing through Knowledge Technology Interests Community BS, MS Computer Science PhD Cognitive and Neural Systems (Computational Neuroscience) Wang Labs Harvard Medical School MITRE Lotus Development Predictive Medicine, Inc.

Rensselaer Polytechnic Institute Flying planes, climbing rocks, traveling, balancing rocks, art & music, tbd BioPathways Consortium, BioPAX, W3C HCLSIG Open to exploring opportunities. 4/29/2020 2

Research to Practice Timeline

(earlier work: 10 years in Software Research & Development and Product Development) World Congress on Neural Networks, July 11-15, 1993, Portland, Oregon SIG Mental Function and Dysfunction PhD US Patents No. 6,063,028 Awarded Patents Offered at Ocean Tomo Auction Chicago, IL BioPAX EMPWR Patents Sold to Advanced Biological Laboratories Belgium U Pitt Greg Siegle Collaboration

Center for

Yuezhang

Proactive

Xiao Master’s

Depression Treatment

Thesis (RPI) Proposal Approved 1995 1997 2001 2006

2008 2009 2010 2011 2012 1996 2000 Samson, Mc Lean Hospital Depression Poster Presented ISMB 1997 PSB 1998 Linked Data W3C HCLS BioDASH EPOS US Patent No. Modeling of Cognitive and Brain Disorders 6,317,73 Awarded Rensselaer (RPI) Brendan Ashby Actively SEEKING (RPI) Actively SEEKING FUNDING Nightingale Nightingale 3

Healthcare Singularity and the age of Semantic Medicine

2,300 years after the first report of angina for the condition to be commonly taught in medical curricula, modern discoveries are being disseminated at an increasingly rapid pace. 4

Healthcare Singularity and the age of Semantic Medicine

Focusing on the last 150 years, the trend still appears to be linear, approaching the axis around

2025

http://research.microsoft.com/en 4/29/2020 us/collaboration/fourthparadigm/4th_paradigm_book_part2_gillam.pdf

Times have changed

       Aging population (end of life costly) More people with chronic illnesses The end of the blockbuster era Personalized Medicine (right treatment to the right patient at the right time) Need lower cost drug development Improved patient response to treatment (Evidence Based) Web and Mobile   The technology itself (ubiquitous) Patients increasingly engaging Photo: http://www.flickr.com/photos/sepblog/4014143391/ 4/29/2020 6

Data Driven Medicine: Shifts in thinking and practice:



Data, Not Programs



Sharing, Not Hoarding



Personal, Not Population

4/29/2020 7

Data Sharing

http://www.youtube.com/watch?v=N2zK3sAtr-4 4/29/2020 8

Overview

Introduction (10 minutes) 1.

Background BioMed Domain – Health care and Life Science 2.

Reference and Application Ontology Granularity and Layout

Examples: (40 minutes) Reference Ontology Examples 1.

UMLS – High level across biomedicine (5) 2.

BioPAX – Mid level – biological pathways (10) Gene Ontology (“GO”) – Gene annotation (5) 2.

Application Ontology Examples Influenza Ontology (5) 3.

Best Practices (10) 1.

Conclusion (5 minutes) Process: Start with Use Case, develop prototype, Evaluation 2.

Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) Conferences 4/29/2020 9

Background

Domain: Health Care, Life Science, and People Times have changed Data Driven Medicine Health Care Singularity 2.

What are you building: Reference vs. Application Ontology Spectrum Reference vs Application Ontology 3.

Why: Function (Use Case) Link, Aggregate, Search, Integrate, etc.

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Scope: HCLS Domain

Health Care & Life Science

The Open Biological and Biomedical Ontologies http://www.obofoundry.org

Goal: a suite of orthogonal interoperable reference ontologies Barry Smith U Buffalo, NCBO From: Nat Biotechnol. 2007 November; 25(11): 1251.

doi: 10.1038/nbt1346 4/29/2020 11

Existing formalisms

Ontology Spectrum

Weak Semantics Strong Semantics Reuse of terminological resources for efficient ontological engineering in Life Sciences

by Jimeno-Yepes, Antonio; Jiménez-Ruiz, Ernesto; Berlanga-Llavori, Rafael; Rebholz-Schuhmann, Dietrich Bioinformatics

Vol.

Issue Journal:

Suppl 10

DOI:

BMC 10.1186/1471-2105-10 S10-S4 http://www.mkbergman.com/wp content/themes/ai3v2/images/2007Posts/070501d_SemanticSpe ctrum.png

4/29/2020 12

Strong Semantics

Ontology Spectrum

Weak Semantics

http://www.mkbergman.com/wp content/themes/ai3v2/images/2007Posts/070501d_SemanticSpe ctrum.png

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Application vs. Reference Ontology

Reference Ontology    Intended as an authoritative source True to the limits of what is known Used by others  Application Ontology     Built to support a particular application (use case) Reused rather than define terms Skeleton structure to support application Terms defined refine or create new concepts directly or through new classes based on inference http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf

4/29/2020 14

Overview

Introduction (10 minutes) 1.

Background BioMed Domain – Health care and Life Science 2.

Reference and Application Ontology Granularity and Layout

Examples

: (40 minutes) Reference Ontology Examples

UMLS – High level across biomedicine (5)

BioPAX – Mid level – biological pathways (10) Gene Ontology (“GO”) – Gene annotation (5) 2.

Application Ontology Examples 1.

Influenza Ontology (5) Best Practices (10) 3.

Conclusion (5 minutes) Process: Start with Use Case, develop prototype, Evaluation 2.

Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) Conferences 4/29/2020 15

Examples

3 Reference Ontology Examples

  

UMLS – High level across biomedicine

BioPAX – Mid level – biological pathways Gene Ontology (“GO”) – Gene annotation

2 Application Ontology Example

  Influenza Ontology Translational Medicine Ontology 4/29/2020 16

The Open Biological and Biomedical Ontologies

http://www.obofoundry.org

From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 4/29/2020 17

What is UMLS?

The UMLS, or

Unified Medical Language System

Enables Interoperability between computer systems   Files Software that brings together many health and biomedical  vocabularies and standards You can use the UMLS to enhance or develop applications, such as electronic health records, classification tools, dictionaries and language translators.

http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf

http://www.nlm.nih.gov/research/umls/quickstart.html

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Unified Medical Language System

Access to the UMLS

The UMLS Terminology Services (UTS) provides three ways to access the UMLS: 

Web Browsers

You can search the data through UTS applications: 

Metathesaurus Browser

- Retrieve UMLS concept information, including CUIs, semantic types, and synonymous terms.



Semantic Network Browser

- View the names, definitions, and hierarchical structure of the Semantic Network.



Local Installation

RRF browser.

download, customize and load into your database system, or browse your data using the MetamorphoSys 

Web Services APIs

application.

You can use NLM’s application programming interfaces (APIs) to query the UMLS data within your own 4/29/2020 19

Unified Medical Language System

License Required



Request a license (FREE)



Sign up for a UMLS Terminology Services (UTS) account.

 UMLS licenses are issued only to individuals  NLM is a member of the IHTSDO (owner of SNOMED CT), and there is no charge for SNOMED CT use in the United States and other member countries. Some uses of the UMLS may require additional agreements with individual terminology vendors.

The UTS account allows you to browse, download, and query the UMLS.

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Unified Medical Language System

Knowledge Sources

The UMLS has three tools, called the UMLS Knowledge Sources: 

Metathesaurus

: Terms and codes from many vocabularies, including CPT®, ICD-10-CM, LOINC®, MeSH ®, RxNorm, and SNOMED CT® 

Semantic Network

: Broad categories (semantic types) and their relationships (semantic relations) 

SPECIALIST Lexicon and Lexical Tools

: Natural language processing tools 4/29/2020 21

Unified Medical Language System

Use UMLS to link health information, medical terms, drug names, and billing codes across different computer systems. Some examples:  Linking terms and codes between doctor, pharmacy, and insurance company   Patient care coordination among several departments within a hospital SNOMED, ICD-9, LOINC, RxNorm – clinical setting, more about this later in the next part of the tutorial The UMLS has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.

http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf

4/29/2020 22

Overview

Introduction (10 minutes) 1.

Background BioMed Domain – Health care and Life Science 2.

Reference and Application Ontology Granularity and Layout

Examples: (40 minutes)

Reference Ontology Examples 1.

UMLS – High level across biomedicine (5)

BioPAX – Mid level – biological pathways (10)

Gene Ontology (“GO”) – Gene annotation (5) 2.

Application Ontology Examples 1.

Influenza Ontology (5) Best Practices (10) 3.

Conclusion (5 minutes) Process: Start with Use Case, develop prototype, Evaluation 2.

Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) Conferences 4/29/2020 23

Examples

3 Reference Ontology Examples

   UMLS – High level across biomedicine

BioPAX – Mid level – biological pathways

Gene Ontology (“GO”) – Gene annotation

2 Application Ontology Example

  Influenza Ontology Translational Medicine Ontology 4/29/2020 24

The Open Biological and Biomedical Ontologies

http://www.obofoundry.org

From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 4/29/2020 25

BioPAX

Bio

logical

thway e

change

An abstract data model for biological pathway

integration

Initiative

arose from the community

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BioPAX Biological Pathways of the Cell

What’s a pathway?

Depends on who you ask!

Metabolic Pathways BioPAX Level 1 Molecular Interaction Networks BioPAX Level 2 Signaling Pathways BioPAX Level 3 Gene Regulation

4/29/2020

BioPAX Level 4

BioPAX

BioPAX Level 1

Biological Pathways of the Cell

Metabolic Pathways

A series of

chemical reactions, catalyzed

enzymes

The

products

of one are the

reactants

of the next

e.g. Conversion, Transport

4/29/2020 28

BioPAX

BioPAX Level 2

Biological Pathways of the Cell

Molecular Interaction Networks

http://www.estradalab.org/research/

Cells are complex systems whose physiology is governed by an intricate network of Molecular Interactions (MIs) of which a relevant subset are protein –protein interactions (PPIs).

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BioPAX

BioPAX Level 2

Biological Pathways of the Cell

Molecular Interaction Networks Human Protein Interaction Network (PIN)

http://www.estradalab.org/research/ 4/29/2020 30

Biological Pathways of the Cell BioPAX

BioPAX Level 3 Signaling molecules trigger cellular responses.

Molecules bind to the cell surface causing a cascade of activation Reactions A

activates

Signaling Pathways B

activates

C….

and http://www.hartnell.edu/tutorials/biology/signaltransduction.html

Biological Pathways of the Cell BioPAX

The modulation of any of the stages of gene expression that control: which genes are switched on and off when, how long, and how much Gene regulation may occur many stages : Transcription Post-transcriptional modification RNA transport Translation mRNA degradation Post-translational modifications among many others (more recently discovered!)

Gene Regulation

http://en.wikipedia.org/wiki/Regulation_of_gene_expression http://www.biology-online.org/dictionary/Gene_regulation 4/29/2020 32

BioPAX Biological Pathways of the Cell

What’s a pathway?

Depends on who you ask!

Metabolic Pathways BioPAX Level 1 Molecular Interaction Networks BioPAX Level 2 Signaling Pathways BioPAX Level 3 Gene Regulation

4/29/2020

BioPAX Level 4

BioPAX Ontology

a set of interactions parts how the parts are known to interact

4/29/2020 Level 1 v1.0 (July 7th, 2004) 34

BioPAX Biochemical Reaction

OWL

(

schema

)

Instances (Individuals) (data) phosphoglucose isomerase 5.3.1.9

4/29/2020 35

BioPAX - Simplify

>200 DBs and tools

Application User

Before BioPAX

Database

With BioPAX

Common “ computable semantic ” discovery enables scientific 4/29/2020 36

Overview

Introduction (10 minutes) 1.

Background BioMed Domain – Health care and Life Science 2.

Reference and Application Ontology Granularity and Layout 2.

Examples: (40 minutes) Reference Ontology Examples 1.

UMLS – High level across biomedicine (5) 2.

BioPAX – Mid level – biological pathways (10) Gene Ontology (“GO”) – Gene annotation (5) 2.

Application Ontology Examples Influenza Ontology (5) 3.

Best Practices (10) 1.

Conclusion (5 minutes) Process: Start with Use Case, develop prototype, Evaluation 2.

Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) Conferences 4/29/2020 37

Examples

3 Reference Ontology Examples

   UMLS – High level across biomedicine BioPAX – Mid level – biological pathways Gene Ontology (“GO”) – Gene annotation

2 Application Ontology Example

  Influenza Ontology Translational Medicine Ontology 4/29/2020 38

The Open Biological and Biomedical Ontologies

http://www.obofoundry.org

From: Nat Biotechnol. 2007 November; 25(11): 1251. doi: 10.1038/nbt1346 4/29/2020 39

Gene Ontology (GO)

Standard representations:  Gene and gene product attributes Structured controlled vocabularies organized as 3 independent Ontologies    Molecular Interactions Biological Processes Cellular Location  Across species and databases [1] Rhee, S.Y, Wood, V., Dolinski, K. and Draghici, S. 2008. Use and misuse of the gene ontology annotations. Nature Reviews Genetics 9:509-515. 4/29/2020 [2] http://people.oregonstate.edu/~knausb/rna_seq/annot.pdf

Gene Ontology

Two Key Uses:  Resource: to look up genes with similar functionality or location within the cell to help characterize the function of a sequence or structure  Use to annotate genomes to enable the analysis of the genome through the annotation terms.

4/29/2020 41

Manually-assigned evidence codes fall into

Four categories: Experimental Computational analysis Author statements, Curatorial statements

Gene Ontology Evidence Codes

Inferred from Electronic Annotation (IEA)

is not assigned by a curator.

Adapted from: http://people.oregonstate.edu/~knausb/rna_seq/annot.pdf

See also: http://www.geneontology.org/GO.evidence.shtml

Sequence Ontology

Sequence Ontology (SO) ‘terms and relationships used to describe the features and attributes of biological sequence.’ (E.g., binding_site, exon, etc.) sequence_attribute feature_attribute polymer_attribute sequence_location variant_quality sequence_feature junction region sequence_alteration sequence_variant functional_variant structural_variant Relationship (lots!) (snuck this one in as another example) SO http://www.sequenceontology.org/ 4/29/2020 43

Overview

Introduction (10 minutes) 1.

Background BioMed Domain – Health care and Life Science 2.

Reference and Application Ontology Granularity and Layout 2.

Examples: (40 minutes) Reference Ontology Examples 1.

UMLS – High level across biomedicine (5) 2.

BioPAX – Mid level – biological pathways (10) Gene Ontology (“GO”) – Gene annotation (5) 2.

Application Ontology Examples Influenza Ontology (5) 3.

Best Practices (10) 1.

Conclusion (5 minutes) Process: Start with Use Case, develop prototype, Evaluation 2.

Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) Conferences 4/29/2020 44

Examples

3 Reference Ontology Examples

   UMLS – High level across biomedicine BioPAX – Mid level – biological pathways Gene Ontology (“GO”) – Gene annotation

2 Application Ontology Example

  Influenza Ontology Translational Medicine Ontology 4/29/2020 45

Application vs. Reference Ontology

Reference Ontology    Intended as an authorative source True to the limits of what is known Used by others  Application Ontology     Built to support a particular application (use case) Reused rather than define terms Skeleton structure to support application Terms defined refine or create new concepts directly or through new classes based on inference http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf

4/29/2020 46

Application Ontology Influenza Ontology

http://www-test.ebi.ac.uk/industry/Documents/workshop 4/29/2020 materials/DiseaseOntologiesAndInformation190608/The%20Influenza%20Infectious%20Disease%20Ontology%20(I-IDO)%20 %20Joanne%20Luciano.pdf

Application Ontology Influenza Ontology

4/29/2020 http://www-test.ebi.ac.uk/industry/Documents/workshop materials/DiseaseOntologiesAndInformation190608/The%20Influenza%20Infectious%20Disease%20Ontology%20(I-IDO)%20-%20Joanne%20Luciano.pdf

Application Ontology Influenza Ontology

http://www-test.ebi.ac.uk/industry/Documents/workshop 4/29/2020 materials/DiseaseOntologiesAndInformation190608/The%20Influenza%20Infectious%20Disease%20Ontology%20(I-IDO)%20 %20Joanne%20Luciano.pdf

Application Ontology Influenza Ontology

http://www-test.ebi.ac.uk/industry/Documents/workshop 4/29/2020 materials/DiseaseOntologiesAndInformation190608/The%20Influenza%20Infectious%20Disease%20Ontology%20(I-IDO)%20 %20Joanne%20Luciano.pdf

Overview

Introduction (10 minutes) 1.

Background BioMed Domain – Health care and Life Science 2.

Reference and Application Ontology Granularity and Layout 2.

Examples: (40 minutes) Reference Ontology Examples 1.

UMLS – High level across biomedicine (5) 2.

BioPAX – Mid level – biological pathways (10) Gene Ontology (“GO”) – Gene annotation (5) 2.

Application Ontology Examples Influenza Ontology (5) 3.

Best Practices (10) 1.

Conclusion (5 minutes) Process: Start with Use Case, develop prototype, Evaluation 2.

Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) Conferences 4/29/2020 51

Examples

3 Reference Ontology Examples

   UMLS – High level across biomedicine BioPAX – Mid level – biological pathways Gene Ontology (“GO”) – Gene annotation

2 Application Ontology Example

  Influenza Ontology Translational Medicine Ontology 4/29/2020 52

Application vs. Reference Ontology

Reference Ontology    Intended as an authoritative source True to the limits of what is known (which does change!) Used by others  Application Ontology     Built to support a particular application (use case) Reused rather than define terms Skeleton structure to support application Terms defined refine or create new concepts directly or through new classes based on inference http://www.nlm.nih.gov/research/umls/presentations/2004-medinfo_tut.pdf

4/29/2020 53

Translational Medicine Ontology

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside Luciano et al. Journal of Biomedical Semantics 2011, 2(Suppl 2):S1 http://www.jbiomedsem.com/content/2/S2/S1 4/29/2020 54

Individuals, Not Populations

Quickly retrieve pharmacogenomic markers of patients when needed No central storage of data is necessary, giving patients full control over their personal health information.

http://safety-code.org/ Photo: http://www.flickr.com/photos/sepblog/4014143391/ 4/29/2020 Distinguished paperMedInfo 2013 55

Translational Medicine Ontology

Overview of selected types, subtypes (overlap) and existential restrictions (arrows) in the Translational Medicine Ontology.

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Translational Medicine

Translational Medicine Ontology with mappings to ontologies and terminologies listed in the NCBO BioPortal.

Knowledge Base

The TMO provides a global schema for Indivo-based electronic health records (EHRs) and can be used with formalized criteria for Alzheimer’s Disease. The TMO maps types from Linking Open Data sources.

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Overview

Introduction (10 minutes) 1.

Background BioMed Domain – Health care and Life Science 2.

Reference and Application Ontology Granularity and Layout 2.

Examples: (40 minutes) Reference Ontology Examples 1.

UMLS – High level across biomedicine (5) 2.

BioPAX – Mid level – biological pathways (10) Gene Ontology (“GO”) – Gene annotation (5) 2.

Application Ontology Examples Influenza Ontology (5) 3.

Best Practices (10) 1.

Conclusion (5 minutes) Process: Start with Use Case, develop prototype, Evaluation 2.

Standards: BioMedical Ontology Best practices (BioPortal, BFO, SIO) Conferences 4/29/2020 58

Best Practices

Semantic Web Methodology & Technology Development Process Fox, Peter & McGuinness, Deborah 2008 http://tw.rpi.edu/web/doc/TWC_SemanticWebMethodology 4/29/2020 59

Generalized Ontology Evaluation Framework (GOEF)

Two stages: 1. Recast use case into its components: Three Levels of Evaluation 2. Evaluate components using objective metrics 60

BioPortal

http://bioportal.bioontology.org/ Provides access to commonly used biomedical ontologies and to tools for working with them. BioPortal allows you to    

Browse

 the library of ontologies   mappings between terms in different ontologies a selection of projects that use BioPortal resources

Search

 biomedical resources for a term  for a term across multiple ontologies

Receive recommendations

 on which ontologies are most relevant for a corpus

Annotate text

 with terms from ontologies All information available through the BioPortal Web site is also available through the NCBO Web service REST API. Please see REST API documentation for more information.

http://www.bioontology.org/wiki/index.php/NCBO_REST_services 4/29/2020 61

Conferences

Conference on Semantics in Health Care and Life Sciences (CSHALS) Semantic web applications and tools for life sciences (SWAT4LS)

Edinburgh 2013

4/29/2020 62

Tutorial sources

Conclusion

  BioPortal W3C HCLSIG Consortia to join      W3C HCLSIG OpenPHACTS Identifiers.org

Pistoia Alliance BioPAX (check for new name) 4/29/2020 63

THANK YOU!

RPI Tetherless World Constellation RPI Web Science Research Center Predictive Medicine, Inc.

W3C Health Care & Life Science SIG BioPathways Consortium BioPAX Harvard Medical School, Mass General Hospital Abha Moitra, Petr Haug, Larry Hunter, Bob Powers, Scott Marshall, Matthias Samwald, Michel Dumontier, Ted Slater, Eric Neumann, Lynette Hirschman, Lynn Schriml, Rick Lathrop and many many others!

NSF, NIH, NIST, IEEE and many others!

4/29/2020 64

Backup Slides

4/29/2020 65

HL-7 and RIM

HL-7 and RIM: http://www.w3.org/2013/HCLS tutorials/RIM/#%286%29 

RDF RIM Tutorial

Eric Prud'hommeaux , < [email protected]

>  Basic understanding of the structure of how data written in HL7's RIM can be expressed in RDF.  It is not a substitute for HL7's documentation, but instead the author's notion of a quick way to familiarize oneself with the concepts and terms used in the RIM and how the graph structure of RDF is a natural way to represent this data. Copyright © 2013 W3C ® policies apply . ( MIT , ERCIM , Keio , Beihang ) Usage 4/29/2020 66

Personalized Medicine

Components • • • •   Understand disease heterogeneity Comprehend disease progression Determine genetic and environmental contributors Create treatments against relevant targets  drugs against relevant targets (molecular structures)  Yoga against stress   Exercise against obesity Elimination against food intolerance or allergy Develop

markers

to predict response Identify concrete

endpoints

to measure response 4/29/2020 67

Scope Ontology Uses

 Knowledge Management  Annotate data (such as genomes)   Access information (search, find, and access) Map across ontologies relate   Data integration and exchange  Model dynamic cellular processes  Identify Drug Interactions    Decision support SafetyCodes Diabetic Care Lab Alerts (Bodenreider YBMI 2008) practices-pitfalls-and-positives-cbo-2009/ 68

Unified Medical Language System

Metathesaurus

NLM uses the Semantic Network and Lexical Tools to produce the Metathesaurus.

Metathesaurus production involves:    Processing the terms and codes using the Lexical Tools Grouping synonymous terms into concepts Categorizing concepts by semantic types from the Semantic Network  Incorporating relationships and attributes provided by vocabularies  Releasing the data in a common format They can be accessed separately or in any combination according to your needs.

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