Pax Terminologica

1
Pax Terminologica

• Barry Smith
• Institute for Formal Ontology and Medical
  Information Science (IFOMIS), Saarland University
  / University at Buffalo

2
Overview

• systems for semantic annotation
• linguistics vs. science
• semantic annotation in biomedical informatics
• improving systems for semantic annotation
• conclusions

3
The Penn Treebank Project

• annotates naturally occurring text for linguistic
  structure, producing skeletal parses showing
  syntactic and semantic information in tree form

4
Automatic Content Extraction Program (ACE)

• develops text corpora in English, Chinese and
  Arabic annotated for entities, the relations
  among them and the events in which they
  participate.

5
High Accuracy Retrieval from Documents (HARD)

• creates corpora and annotations including topics,
  metadata and relevance judgements

6
Annotation Graph Toolkit (AGTK)

• formal framework for representing linguistic
  annotations of time series data.

7
TimeML

• robust specification language for markup of
  natural language to support
• time stamping of events (identifying and
  anchoring in time)
• ordering events with respect to one another
• reasoning about persistence

8
SpaceML

• provides facilities for annotating
• category attributions to spatial regions
  (self-connected, bounded, regular, etc.)
• ascription to regions of topological, distance,
  morphological and orientation relations
• the definition of a region in terms of its
  boundary.

9
WordNet

• annotates English nouns, verbs, adjectives and
  adverbs to synonym sets, each representing one
  underlying lexical concept.

10
FrameNet

• documents the range of semantic and syntactic
  combinatory possibilities (valences) of each word
  in each of its senses

11
is there order in this chaos?
12
ISO/TC 37 / SC 4 N 076

• Ide, N., Romary, L., de la Clergerie, E. (2003).
  International Standard for a Linguistic
  Annotation Framework.
• HLT-NAACL 2003 (Edmonton)

13
OntoGloss (influenced by ISO Linguistic
Annotation Framework)

• an ontology based annotation tool that uses
  predefined terms in an ontology to mark-up a
  document
• No standard portal for semantic annotation
  tools/projects (?)

14
Purposes of semantic annotation

• information retrieval (incl. semantic indexing
  answering queries that use words not used in the
  text, including words from other languages)
• automatic translation
• disambiguation
• topic extraction and text summarization
• information integration
• reasoning

15
for linguistics

• fiction no less important than fact
• English has no privileged status
• regimentation not allowed
• annotation frameworks may be competitive
• cross-framework consistency is not important

16
for science

• factual discourse alone important
• English is language par excellence
• regimentation is allowed
• goal of truth to create a single
  computer-processable map of reality
• truth is one ? must strive for consistency of
  annotations and additivity of annotation
  frameworks

17
for science

• must end the terminology wars
• Plant Ontology (PO)
• cell def. structural and physiological unit of a
  plant
• what should PO do when it needs to study bacteria
  in plants?
• answer all shall use the word cell to mean the
  same thing!
• (all in biology)

18
the ideal (of additivity)

• WordNet for single word forms
• FrameNet for valencies/combination forms
• SpaceNet for spatial structures
• TimeNet for temporal structures
• ChemNet for chemical structures
• CellNet for cellular structures
• etc.

19
a scientific problem huge swarms of biomedical
data at different granularities, from molecule to
clinic

• methods for data integration needed to enable
  reasoning across data at multiple granularities
• (genomic medicine ...)

20
orthodox solutions to this problem

• dumb statistical number-crunching
• or
• Semantic Web, Unified Medical Language System
  (UMLS), Moby, etc.
• let a million flowers bloom
• and rely on mappings between already existing
  controlled vocabularies/annotation systems

21
an alternative solution

• use the peer-reviewed biomedical literature
• contains both textual descriptions of biological
  functions (incl. diseases) and references to
  entities represented in the biochemical databases
• use high-quality semantic annotations of the
  former to integrate across the latter ? the Gene
  Ontology

22
(No Transcript)
23
(No Transcript)
24
The methodology of annotations

• Model organism databases employ scientific
  curators, who use the experimental observations
  reported in the biomedical literature to link
  gene products (such as proteins) with GO terms in
  annotations.

25
The process of annotations

• leads to improvements and extensions of the
  ontology, which in turn leads to better
  annotations
• a virtuous cycle of improvement in the quality
  and reach of both future annotations and the
  ontology itself,
• yielding a slowly growing computer-interpretable
  map of biological reality within which major
  databases are automatically integrated in
  semantically searchable form

26
need to extend GO by means of other ontologies,
e.g. Cell Ontology, via integrated definitions
GO

Cell type

Osteoblast differentiation Processes whereby an
osteoprogenitor cell or a cranial neural crest
cell acquires the specialized features of an
osteoblast, a bone-forming cell which secretes
extracellular matrix.
New Definition
27
need to extend GO also to semantic annotation of
clinical literature
unfortunately, available (UMLS) clinical
vocabularies are of variable quality and low
mutual consistency
28
? need for prospective standards to assure
consistency and high quality

• create rules for high-quality controlled
  vocabularies for the annotation of scientific
  literature
• make everyone follow these rules
• regimentation !

29
first step

• a shared portal for (so far) 58 ontologies
• (low regimentation)
• http//obo.sourceforge.net

30
(No Transcript)
31
Second step The OBO Foundryhttp//obofoundry.or
g/
32
The OBO Foundry

• scientific standards and principles-based
  coordination of systems for semantic annotation
  of biomedical literature to create a single
  interoperable family of gold standard reference
  ontologies

33

• A subset of OBO ontologies, whose developers
  have agreed in advance to accept a common set of
  principles designed to ensure
• formal robustness
• stability
• compatibility
• interoperability
• support for logic-based reasoning

34

• Custodians
• Michael Ashburner (Cambridge)
• Suzanna Lewis (Berkeley)
• Barry Smith (Buffalo/Saarbrücken)

35
A prospective standard

• designed to guarantee interoperability of
  ontologies from the very start
• established March 2006 already 13 OBO
  ontologies have joined the Foundry and are being
  corresponding reformed three new ontologies are
  being constructed ab initio in its terms

36

• Initial Candidate Members
• GO Gene Ontology
• CL Cell Ontology
• SO Sequence Ontology
• ChEBI Chemical Ontology
• PATO Phenotype (Quality) Ontology
• FuGO Functional Genomics Investigation Ontology
• FMA Foundational Model of Anatomy
• RO Relation Ontology
• ChEBI Chemical Entities of Biological Interest
• CARO Common Anatomy Reference Ontology
• FuGO Functional Genomics Investigation Ontology
• PrO Protein Ontology
• RnaO RNA Ontology  

37

• Under development
• Disease Ontology
• Mammalian Phenotype Ontology
• OBO-UBO / Ontology of Biomedical Reality
• Organism (Species) Ontology
• Plant Trait Ontology
• Environment Ontology
• Behavior Ontology
• Biomedical Image Ontology
• Clinical Trial Ontology

38
(No Transcript)
39
CRITERIA
The OBO Foundry

• The ontology is open and available to be used by
  all.
• The ontology is in, or can be instantiated in, a
  common formal language.
• The developers of the ontology agree in advance
  to collaborate with developers of other OBO
  Foundry ontology where domains overlap.

40
CRITERIA

• The developers of each ontology commit to its
  maintenance in light of scientific advance, and
  to soliciting community feedback for its
  improvement.
• They commit to working with other Foundry members
  to ensure that, for any particular domain, there
  is community convergence on a single controlled
  vocabulary

41
CRITERIA

• The ontology possesses a unique identifier space
  within OBO.
• The ontology provider has procedures for
  identifying distinct successive versions.
• The ontology includes textual definitions for all
  terms.

42
CRITERIA

• The ontology has a clearly specified and clearly
  delineated content.
• The ontology is well-documented.
• The ontology has a plurality of independent users.

43
CRITERIA

• The ontology uses relations which are
  unambiguously defined following the pattern of
  definitions laid down in the OBO Relation
  Ontology.
• Genome Biology 2005, 6R46

44
OBO Relation Ontology
Foundational is_apart_of
Spatial located_incontained_inadjacent_to
Temporal transformation_ofderives_frompreceded_by
Participation has_participanthas_agent
45
analogy with FrameNet

• the constituent ontologies in the OBO Foundry are
  focused overwhelmingly on single nouns
• the OBO Relation Ontology is designed to ensure a
  common structure of relations shared by all
  Foundry ontologies comparable to SpaceML,
  TimeML ...
• need something like (Bio)FrameNet to pull the
  different levels of granularity together

46
CRITERIA
The OBO Foundry

• Further criteria will be added over time in order
  to bring about a gradual improvement in the
  quality of the ontologies in the Foundry

47
GOALS

• semantic alignment of OBO Foundry ontologies
  through a common system of formally defined
  relations
• to enable reasoning both within and across
  ontologies, and thus also within and between the
  literature annotated in its terms
• and thus also to support reasoning across
  associated data

48
GOALS

• to promote re-usability of data
• if data-schemas are formulated using a single
  well-integrated framework for semantic annotation
  in widespread use, then this data will be to this
  degree itself become more widely accessible and
  usable

49
GOALS

• to help in creating better mappings e.g. between
  human and model organism phenotypes
• S Zhang, O Bodenreider, Alignment of Multiple
  Ontologies of Anatomy Deriving Indirect Mappings
  from Direct Mappings to a Reference Ontology,
  AMIA 2005

50
GOALS

• to introduce the scientific method into the
  development of semantic annotation frameworks
• to introduce some of the features of scientific
  peer review into biomedical ontology development

51
GOALS

• to aid literature search
• http//www.gopubmed.org/
• to subvert the current policy of ad hoc creation
  of new annotation schemas by each clinical
  research group by providing a common shared
  framework

52
GOALS

• to use the Foundry ontologies as benchmark for
  improving existing terminologies
• to create controlled vocabularies for semantic
  annotation of clinical trial records, scientific
  journal articles, ...

53
GOALS

• to create an evolving map-like computable
  representation of the entire domain of biomedical
  reality
• to create the conditions for a step-by-step
  evolution towards high quality ontologies in the
  biomedical domain
• which will serve as stable attractors for
  clinical and biomedical researchers in the future

54
GOALS

• to end the terminology wars and to advance
  regimentation of clinical and other vocabularies
  in a scientific spirit

55
Conclusion 1

• existing linguistic resources for semantic
  annotation are scattered to the four winds
• ? need for something like the OBO Library to
  ensure that the different available tools are
  available for comparison and alignment

56
Conclusion 2

• linguists developing tools for semantic
  annotation with scientific purposes need
  something like the Foundry to ensure a complete
  set of interoperable tools which allow for
  additivity of annotations

57
the ideal

• BioWordNet for single word forms
• SpaceNet for spatial structures
• TimeNet for temporal structures
• ChemNet for chemical structures
• CellNet for cellular structures
• BioFrameNet for valencies/combination forms

58
(No Transcript)