# Presentation: 214856

## NIF Vocabulary Server

- Maryann Martone, Ph. D.

## NIF Technical Team

- Perry Miller, Yale
- Luis Marenco, Yale
- Yuli Li, Yale
- Arun Rangarajun, Cal Tech
- Hans-Michael Muller, Cal Tech
- Sredevi Polavarum, George Mason
- Jeff Grethe, UCSD
- Brian Sanders, UCSD
- Vadim Astakhov, UCSD
- Amarnath Gupta, UCSD
- Xufei Qian, UCSD
- Bill Bug, UCSD
- Maryann Martone, UCSD

## Basic Architecture

- The same architecture and workflow applies to the registration process

## Role of NIF Terminologies

- NIF terminologies provide a shared vocabulary for annotation of neuroscience data
- NIF terminologies provide the shared semantics for accessing resources and data through the NIF interface
- Semantic enrichment of terms to enable more targeted and meaningful queries
- Ultimately, NIF terminologies are critical for data and database interoperability

## Building the NIF Terminologies

- NIF Basic:
- Daniel Gardner held a series of workshops with neuroscientists to obtain sets of terms that are useful for neuroscientists
- NIFSTD (NIF Standardized)
- Bill Bug built a set of expanded vocabularies using the structure of the BIRNLex and the import of existing terminological resources
- Provides enhanced coverage of domains in NIF Basic
- Provides coverage of domains not included in NIF but covered by existing resources, e.g., molecules
- Encoded in OWL/RDF
- Provides mapping to source terminologies, including NIF Basic
- Provides synonyms, lexical variants, abbreviations

## Registering a Resource to NIF

- Level 1
- NIF Registry:  high level descriptions from NIF vocabularies supplied by human curators
- Level 2***
- Discovery mechanism for hidden content (Disco or SiteMaps.org)
- Level 3
- Direct query of web accessible database
- Automated registration
- Mapping of database content to NIF vocabulary by human

- ***Not yet implemented

## Level 1 Registration

- Sites are entered by curators
- Annotation with NIF **basic** vocabulary + free text
- May be searched with NIFSTD terms

## Level 2 Registration

- Automated or semi-automated discovery and indexing of web sites
- Index of web sites registered to NIF registry
- Web content is indexed against the NIFSTD vocabularies
- Discovery mechanism planned (Luis)
- XML will utilize NIFSTD

## Level 3:  NIF Data Federation

-  Allows deep query of database content through a single interface
- Limited number of resources registered for Phase 2:  proof of concept and demonstration of deep search via database mediation
- Registration process:
- Create wrapper to allow remote NIF mediator query
- Map content to **NIFSTD**
- Semi-automatic process based on high-level mapping of fields and data values:
- e.g., SumsDB *geography* maps to NIFSTD *regional part of brain*

## Mapping to Level 3:  Concept Mapping Tool

- Java webstart application
- Retrieves database schema + data from mediator registry
- Maps data to NIFSTD values
- Provides term mapping to mediator Term Index Source (TIS)

## Why is this done by a human at the moment?

- Abbreviations, ambiguous terms, non-standard names, e.g.,
- LPF:  (**if this is mapped as an abbreviation to NIFSTD, then it wouldn’t be a problem)
- Anterior cingulate:  Gyrus?  Sulcus?
- Frontal subgyral =frontal subgyral white matter?

## Your definition-My definition?

- Hippocampus (SUMS)= hippocampus (NIFSTD)?
- can’t tell just by the string;  must look at the definition

## BIRNLex Components

- BIRNLex

- Organism
- Taxonomy

- Sensory
- Behavior
- Cognition

- Disease

- Investigation

- Anatomy

**Notes:**
CARO connection is pending

## Building NIFSTD

- OBO Foundry principles and best practices
- NIFSTD is built from a set of modular ontologies
- Anatomy:  Neuronames (via BIRNLex)
- Taxonomy:  NCBI taxonomy (via BIRNLex)
- Molecule:  IUPHAR + PDPS Ki + SwissProt (neuro)
- Cell:  NIF (Senselab, Neuromorpho, CCDB)
- Subcellular anatomy:  GO + SAO
- Disease:  MESH/UMLS + NINDS + OMIM (neuro)
- Resource descriptors:  NIF, NITRC, NCBC, OBI
- Technique:  NIF + Ontology for Biomedical Investigation (OBI)
- Behavior:  NIF, BIRN, BrainMap
- Attributes:  PATO
- Each is mapped to a unique identifier
- Single inheritance with minimal assignment of properties
- Each file is imported separately, but integrated through the Basic Formal Ontology into a single vocabulary
- Imported using manual, semi-automated and automated means
- Degree of intervention dependent on the vocabulary
- At this point, large degree of manual intervention is often necessary
- Link back to source ID is maintained
- Encoded in OWL/RDF

## Adding to and amending the BIRN lexically-enhanced ontology

## Batch modifications (alpha)

- prefLabel
- synonym
- abbrev
- acronym
- tax scientific name
- tax common name
- GENBANK common name
- NCBI BLAST name
- antiquated label
- misspelling
- IMSR standard name

**Notes:**
BIRNLex lexical tags = prefLabel + synonyms + abbrev + acronyms

Grab BIRNLex ID = either
	* a lexcial mapping of this term (and its ID if it has one) to a specific BIRNLex

## Batch modification example

- IUPHAR V-gated Ion Channels (NIF)

**Notes:**
Term spreadsheet
	* rows = distinct class
	* cols = class properties (some Annotations - others ObjectProperties)
Ancestor class = required (to place it in hierarch) - sometimes requires creating additional class ancestors

## Batch modification example

- IUPHAR V-gated Ion Channels (NIF)

**Notes:**
Organism-specific mRNA transcripts, genes, and chromosomes all were automatically built using Jena-based RDF API

## Citations & Mappings

- Maintain link back to external knowledge source
- For terms/concepts and for definitions
- Mappings provide parsable representation of cross terminology synonymies

## Citations & Mappings

- External IDs
- Generic
- externalSourceId
- Specific (for common sources)
- Neuroanatomy: neuronamesID/bamsID
- Organism taxonomy: ncbiTaxID/itisID/gbifID/jaxMiceID/tacMiceID
- Cells/Tissue: atccID
- Disease: UmlsCui/MeSH
- URL templates
- Use IDs to link to external source URL references (when available)
- automatically add ref links in to tools using BIRNLex - TIS, BONFIRE, etc.
- Definition citations as well including URIs & publication references

## Use Case:  Cell Types

- Existing cell type ontology, but poor coverage of neuronal cells and generally agreed by the community to be “problemmatical”
- Senselab, CCDB, NeuroMorpho.org, NIF collated cell type terminologies
- Produced master list on Excel spreadsheet with defined properties
- Neurotransmitter, anatomical location, morphology, molecular constituent, circuit type
- Using Jena code written by BB, imported contents directly into Protégé OWL, matching strings against existing content, e.g., anatomy, molecules

## Slide 22

## Maintaining NIFSTD

- Maintenance of NIFSTD at this point will probably require the use of a human curator, although several of the functions can be automated
- Community can contribute to NIF Basic;  human curator will be needed to migrate much of the content to NIFSTD

## Availability of NIFSTD

- NIFSTD OWL file available from _[http://purl.org/nif/ontology/nif.owl](http://purl.org/nif/ontology/nif.owl)_
- NIFSTD available through Bonfire (1 and 2) for programmatic access

## Bonfire

- NIF vocabularies are served by the vocabulary server built by BIRN:  Bonfire
- Oracle database
- Cross mappings between different vocabularies
- Basic graph queries (neighborhood, shortest path)
- Web services were developed for NIF
- Based on the structure of UMLS
- User interface for graph visualization and queries (not planned for NIF delivery)
- Bonfire 2
- Optimized for NIF vocabularies
- Postgres RDMS + ontology access functions that we have built
- e.g., Given a term, produce its ancestry graph by following the edge-label(subclass-of OR part-of)

## NIF Application ArchitectureFor OntoQuest (Bonfire 2)

- Configuration

- NIF Application ArchitectureFor OntoQuest (Bonfire 2)

**Application Logic**

- Ontology
- Database

- Lucene
- Index

- Neuroscience Web sites

- Text Engine

- Ontologies

- External web sites

**Web Client**

- Term Mapper and Indexer

## What’s next

- NIFSTD:  Comprehensive “is a” hierarchy, but relations sparse - e.g., “part of”, “binds ligand”, “sequence of”, etc.
- Continue to build pipeline from loosely structured to formal ontology
- Continue to add domains
- Add relationships and definitions
- Generate additional hierarchies
- Incorporate more of the semantics into the NIF search

## Evolution of Terminologies

**NIF Basic vocabulary**
- Contributed by panels of experts
- Coarse granularity but broad coverage
- Loose hierarchy
- XML

**NIF STD**
- Imports existing terminologies developed by other communities
- Modular design
- Normalizes structure according to Basic Formal Ontology (BFO) Creates single inheritance “is a” tree
- Provides mapping between NIF and NIFSTD
- Provides synonyms, abbreviations and lexical variants
- OWL/RDF

**NIF Plus**
- Relates classes through “part of” and other OBO relations
- Consistent human and machine-readable definitions

- NIF Phase I and II

## Current Status and Future Work

- Prototype interfaces built upon Bonfire I and II
- NIFSTD 1.0 in Bonfire 1
- NIFSTD 1.1 in Bonfire 2
- Will update Bonfire 1 content after this demonstration
- Implementation and testing of vocabulary services using Bonfire 2
- Better use of lexical variants, synonyms etc.
- Mapping of NIF Registry and NIF data federation with NIFSTD
- All resources registered will mark up more content
- Coverage of behavior (sensory, motor) and behavioral assessments will be added
- More lexical variants will be used in searches
- Improved access to annotation properties through Concept Mapper