Ontological Analysis

1

• Ontological Analysis Integration of
  Terminologies Towards An Environmental Reference
  Ontology Library

Geri Steve, Aldo Gangemi, Domenico M. Pisanelli
Istituto di Tecnologie Biomediche, CNR, Rome,
Italy http//saussure.irmkant.rm.cnr.it steve,gan
gemi,pisanelli_at_saussure.irmkant.rm.cnr.it
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Which part are you talking about?

• If my liver is part of my digestive system, and
  that system is part of me, is my liver part of
  me?
• If my liver is a part of me and I am part of the
  CNR, is my liver part of the CNR?
• My liver is a component of my digestive system,
  while I am a member of CNR. No rule for composing
  component and member relations
• Moreover, I am a body, but I am also a person. A
  living person depends on a body. Nevertheless, a
  living person can be member of CNR, but a body
  cannot

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Object or place?

• A body region is an object that one could cut, or
  a place?
• A gene is a DNA fragment, or a DNA region
  (allele)?
• A river is an orographic object, or the
  geographic place of a watercourse?
• Despite many differences, such three cases seem
  analogous they share a polysemy partly dependent
  on an abstract difference between objects and
  regions, and a related axiom specifying that
  objects must be located at some region

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River in the GEMET thesaurus
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Should we worry about those things?

• Even in presence of polysemous names, a
  standalone application using a local databank or
  terminological repository may be able to
  accomplish its task without serious flaws.
• However, when it is integrated with another
  application, semantic mismatches constitute a
  serious obstacle for the agent or interface that
  is negotiating or sharing information.
• The ever-increasing demand of data sharing has to
  rely on a solid conceptual foundation in order to
  give a semantics to the terabytes available in
  different databases and eventually traveling over
  the networks.
• Ontologies are currently recognized as the answer
  to the needs of conceptual foundation.

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The advantages of ontologies

• to allow a more effective data and knowledge
  sharing
• to facilitate knowledge re-use in decision
  support systems
• to give theoretical foundation to vocabulary
  standardization activity

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Our task

• We learn domain ontologies (in medicine,
  environment) by integrating the conceptual models
  that can be extracted from terminological sources
• The goal is building Domain Reference Ontologies
  in the form of modular libraries of formal
  theories
• In our ONIONS methodology, ontology learning
  needs both incremental bottom-up learning from
  sources, and incremental definition and reuse of
  general theories that can account for the
  intended meaning of terms

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ONtologic Integration Of Naïve Sources

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Minimal history

• ONIONS methodology for ontology integration has
  been developed since the early 1990s to account
  for the problem of conceptual heterogeneity. It
  addresses some problems encountered in the
  context of the European project GALEN and the
  Italian projects SOLMC (Ontological and
  Linguistic Tools for Conceptual Modeling) and
  ONTOINT (Ontological Integration of Information)

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Some related research projects

• GALEN GALEN-IN-USE
• CYC anatomy
• SNOMED RT
• HL7 vocabulary committee
• MED

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What is an ontology?

• A specification of a conceptualization
• (Gruber, 1993)
• The subject of ontology is the study of the
  categories of things that exist or may exist in
  some domain. The product of such a study, called
  an ontology, is a catalog of the types of things
  that are assumed to exist in a domain of interest
  D from the perspective of a person who uses a
  language L for the purpose of talking about D.
  ...
• (Sowa, 1997)
• A partial and indirect specification of a
  conceptualization
• -restricted notion- (Guarino, 1998)

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What is an ontology (restricted notion)?

• An ontology is a set of axioms that account for
  the intended meaning (the intended models) of a
  vocabulary (the namespace of a logical language)
• A set of axioms usually only approximate such
  intended models that on their turn only
  approximate the conceptualization of vocabulary
  items
• A conceptualization is a set of conceptual
  relations that range over a domain and a set of
  relevant states of affairs (possible worlds) for
  that domain
• Therefore, a precise definition of "ontology" (in
  a restricted, formal sense) might be "a partial
  specification of the intended models of the
  conceptualization of a vocabulary"

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Types of ontologies (broad notion)

• Catalog of normalized terms, e.g. a list of terms
  used in the reports from a laboratory no
  taxonomy, no axioms, and no glosses
• Glossed catalog, e.g. a dictionary of medicine a
  catalog with glosses.
• Thesaurus, e.g. many parts of the UMLS
  Metathesaurus, GEMET a hierarchical collection
  of terms the hierarchical link is usually
  polysemous
• Taxonomy, e.g. the ICD10 a collection of classes
  with a partial order induced by inclusion
  (classification)
• Axiomatized taxonomy, e.g. the GALEN Core Model
  a taxonomy with axioms
• Ontology library, e.g. the Ontolingua repository
  a set of axiomatized taxonomies with relations
  among them. Each element of the library is a
  module, which can be included into another one.
  Also, a concept from a module can be only used
  into another one. Ontology modules can be
  considered subdivisions of the namespace of a
  model

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From Data Integration to Conceptual Integration

• Heterogeneous texts
• Heterogeneous semi-structured texts (retrieval
  of web data types and descriptions)
• Heterogeneous databases (schema integration,
  information brokering)
• gt In all these cases, heterogeneity concerns the
  conceptualization of the terminology used in the
  sources

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Polysemy and overlapping

• Since the primary causes of heterogeneity are
• polysemy (conceptual disalignment, difference of
  intended meaning of one name), and
• conceptual overlapping (different names having
  overlapping meaning)
• that arise in the union of the vocabularies of
  two any sources, ontologies are a major component
  to provide semantic access to (and integration
  of) terminological resources
• Incidentally, polysemy is usually found within
  the same source as well (views, themes, homonyms)

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Ontology Learning

• From Natural Language
• From Semi-structured Data
• From Structured Data
• From Terminologies
• gt Integration of sources needs
• (Principled) Conceptual Abstraction

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Conceptual abstraction an example

• The domain ontology A has body region with the
  intended meaning of loosely specified part of
  the body that can be cut, filled, etc.
• The domain ontology B has body region with the
  intended meaning of region of the body at which
  body parts are located
• There is a metonymy acting on body region in A,
  whose intended meaning concerns body parts
  located at some region, although they are denoted
  by referring to the region itself (the intended
  meaning in B)
• Hence, the metonymic name should be distinguished
  from the plain name, and correctly related to it
• The distinction between objects (body parts) and
  regions, and the notion of a localization
  relation holding between objects and regions are
  both necessary to make the metonymy clear, and
  cannot be found in the specifications given in A
  or B. They have to be found in some generic theory

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Ontology integration conceptual issues

• Ontology integration is generally speaking
  the construction of an ontology C that formally
  specifies the union of the vocabularies of two
  other ontologies A and B
• To be sure that A and B can be integrated at some
  level, C has to commit to both A's and B's
  conceptualizations. In other words, the intension
  of the concepts in A and B should be mapped to
  the intension of C's concepts
• Unfortunately, this cannot be realized using only
  the conceptual relations specified in A and B for
  local tasks (for a specific context). The
  methodological principle adopted here is that
  generic ontologies reused from the philosophical,
  linguistic, mathematical, AI literature must
  found the comparison of different intensions. Our
  approach may be called principled conceptual
  integration

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Aspects of integration

• Three aspects of an ontology are taken into
  account
• the intended models of the conceptualizations of
  its vocabulary
• the domain of interest of such models, i.e. the
  'topic' of the ontology
• the namespace of the ontology
• The most interesting case is when A and B are
  supposed to commit to the conceptualization of
  the same domain of interest or of two overlapping
  domains. In particular, A and B may be

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Some integration cases for the same topic

• Alternative ontologies the intended models of
  the conceptualizations of A and B are different
  (they partially overlap or are completely
  disjoint) while the domain of interest is
  (mostly) the same. This is a typical case that
  requires integration different descriptions of
  the same topic are to be integrated
• Truly overlapping ontologies both the intended
  models of the conceptualizations of A and B and
  their domains of interest have a substantial
  overlap. This is another frequent case of
  required integration descriptions of strongly
  related topics are to be integrated
• Equivalent ontologies with vocabulary mismatches
  the intended models of the conceptualizations of
  A and B are the same, as well as the domain of
  interest, but the namespaces of A and B are
  overlapping or disjoint. This is the case of
  equivalent theories with alternative vocabularies

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Ontological integration operational issues

• Depending on the amount of change necessary to
  the operational integration of A and B, different
  levels of interoperability can be distinguished
• Mediation it requires no changes to A and B, but
  only mapping relations that describe the
  equivalence (partial or total) of A's and B's
  elements to C's elements. This may result in weak
  interoperability, since usually the intended
  models of A and B overlap only some concepts
  from A may not have a correspondent in B, and
  vice-versa. This is the design choice for some
  recent information brokering architectures.
  However, such architectures, have a weak
  commitment towards a principled way of conceptual
  integration, possibly for its additional cost
• Alignment it requires some change to fill the
  biggest gaps of A and B respect to an ideal C
  that completely integrates A and B. Therefore,
  alignment requires at least a partial conceptual
  integration. It may support a limited
  interoperability for example, deep inferences
  may be excluded
• Unification it may require a major
  reorganization of A and B, which are
  'harmonized'. Unification intervenes on the
  inferential features of the systems, and consists
  in a complete operational integration everything
  can be made in one system, can be made in the
  other. It results in the most complete
  interoperability but requires a complete
  conceptual integration as well. From the
  conceptual viewpoint, unification consists in the
  adoption of C as a standard in the systems using
  A or B

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Ontology integration practical issues

• Lack of hierarchies
• Ambiguous hierarchies
• Informality
• Lack of modularity
• Polysemy
• Uncertain semantics
• Prototypical descriptions
• Ontological opaqueness
• Lack of a (minimal) set of axioms
• Confusing lexical clues
• Awkward naming policy
• 'Remainder' partitions
• 'Exception' partitions
• Terminological cycles
• Meta-level soup
• Low maintenance capabilities

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Ontologies some desiderata

• An explicit taxonomy with subsumption among
  concepts
• Semantic explicitness of links
• Modularity of namespace
• A stratified design of the modules
• Absence of polysemy within a module
• Disjointness of concepts within a module and
  within the top-level
• A proper interface between the ontology namespace
  and one or more sets of lexical realizations
• Linguistically meaningful naming policy
  (cognitive transparency)
• Rich documentation
• Some minimal axiomatization to detail the
  difference among sibling concepts
• Explicit linkage to concepts and relations from
  generic theories
• Meta-level assignments to distinguish among the
  formal primitives assigned to concepts
• Languages and implementations that support the
  previous needs as well as the possibility of
  collaborative modeling

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The ONIONS Methodology

• ONIONS implementation is meant to provide
  extensive axiomatization, clear semantics, and
  ontological depth to a domain terminology
• Extensive axiomatization is obtained through a
  conceptual analysis of the terminological sources
  and their representation in a logical language
  with a rigorous semantics
• Ontological depth is obtained by reusing a
  library of generic ontologies, on which the
  axiomatization depends. Such library may include
  multiple choices among partially incompatible
  ontologies. In particular, we suggest the
  importance of mereology or theory of parts,
  topology or theory of wholes, connexity and
  boundaries, morphology, or theory of form and
  congruence, localization, or theory of regions,
  time theory, actors, or theory of participants in
  a process, dependence theory, and the theory of
  environmental niches

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The main steps (I)

• 0. Semantically opaque hierarchies and lists are
  pre-processed in order to create clean
  taxonomies
• 1. All concepts, relations, templates, rules, and
  axioms from a source ontology are represented in
  the ONIONS formalisms, currently Loom,
  Ontolingua, and OKBC
• 2. When available, plain text descriptions are
  analyzed and axiomatized (text formalization)
• 3. The union of such products is integrated by
  means of a set of generic ontologies. This is the
  most characteristic activity in ONIONS, which can
  be briefly described as follows

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II

• 3.1. For any set of sibling concepts in a
  taxonomy, the conceptual difference between each
  of them is inferred, and such difference is
  formalized by axioms that reuse the relations and
  concepts already in the library. If no concept is
  available to represent the difference, new
  concepts are added to the library
• 3.2. For any set of polysemous senses of a term,
  different concepts are stated and placed within
  the library according to their topic and to the
  available modules. (Polysemy occurs when two
  concepts with overlapping or disjoint intended
  models have the same name.)
• 3.3. Often, polysemous senses of a term - as well
  as different 'alternative' concepts - are
  metonymically related. For example
  process/outcome (as in inflammation),
  region/object (as in body region), etc.
  Alternatives must be properly defined by making
  it explicit the relationship between them e.g.
  "has-product" for inflammation, "location" for
  body-region
• 3.4. When stating new concepts, the relations
  necessary to maintain the consistency with the
  existing concepts are instantiated. If conflicts
  arise with existing theories, a more general
  theory is searched which is more comprehensive.
  If this is impracticable, an alternative theory
  is created

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III

• 3.5. Relevant integration cases. Since ONIONS
  requires the use of generic theories to
  axiomatize alternative theories, the integration
  of a concept C from an ontology O is performed by
  comparing C with the concepts D1,,n already
  present in the evolving ontology library L, whose
  ontology set M1,,n contains at least a
  significant subset of generic ontologies and the
  set of domain ontologies at that state in the
  evolution of L. The following cases appear
  relevant to the methodology
• 3.5.1. C's name is polysemous in O (internal
  polysemy). Iterate 3.2 3.4
• 3.5.2. C's name is homonym with the name of a Di.
  (Homonymy occurs when both the intended models
  and the domains of two concepts with the same
  name are disjoint.) Homonyms must be
  differentiated by modifying the name, or by
  preventing the homonyms to be included in the
  same module namespace
• 3.5.3. C's name is synonym with the name of a Di.
  (Synonymy is the converse of homonymy and occurs
  when two concepts with different names have both
  the same intended model and the same domain.)
  Synonyms must be preserved, or included in the
  set of lexical realizations related to the
  concept
• 3.5.4. C is subsumed by some Di in L, but it has
  no total mapping on any Dj in L. The gap in L
  must be filled by adding C as a subconcept of Di

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IV

• 3.5.5. C is an intersection between two concepts
  Di and Dj in L. Solved by distinguishing types
  and roles, or different defining elements
• 3.5.6. C has an alternative concept Di in L (same
  domain, but overlapping or disjoint intended
  models)
• 3.5.6.1. If C metonymically depends on Di, C is
  properly related to Di
• 3.5.6.2. If C and Di are different viewpoints on
  the same domain of interest, both concepts are
  kept if the case, they are included in separate
  modules
• 3.5.6.3. If the intended model of C is finer than
  Di's, Di is substituted with C
• 3.5.6.4. If the intended model of C is coarser
  than Di's, C is ignored (but track of it is kept
  for mapping between sources)

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V

• 4. The library of generic, intermediate, and
  domain ontologies should be stratified, say
  domain modules should include intermediate
  modules - that should include generic modules -
  so that each set of modules can be plugged or
  unplugged from its more general set without
  affecting the coherence of the entire library
• 5. The source ontologies are explicitly mapped to
  the integrated ontology, in order to allow
  interoperability. The only admitted mappings are
  equivalent and coarser equivalent. Formally for
  any source ontology SO and an ontology IO that is
  supposed to result (also) from the integration of
  SO, for any concept Ci in SO, there is a Di in IO
  such that CiI DiI (equivalence of possible
  interpretations), or there is a disjunctive
  concept (or Di Dj) in IO such that CiI DiI ?
  DjI (equivalence of possible interpretations to a
  disjunction of concepts i.e. to a union of
  finer concepts)
• 5.1. Partial mappings must have been already
  resolved through the methodology if any, some
  step in the integration procedure must be
  iterated

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Ambiguous hierarchies
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A principled formalization

• (defconcept ununited-fracture
• is-primitive (and fracture
• (some morphology
• (and bone
• (or (some embodies malunion)
• (not integral))))
• (some dependently-postdates
  fracture)
• (all interpretant clinical-condition)))

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Some UMLS concepts pertaining the intersection
Amino Acid, Peptide, or Protein Carbohydrate

• (hamster oviduct-specific glycoprotein)
• (Par j I)
• ((Man)6(GlcNAc)2Asn)
• (Zn(2)-IAA)
• (collapsing factor)
• (BDV 18K glycoprotein)
• (SI-gene-associated glycoprotein, Nicotiana)
• (FdI allergen)
• (sca gene product)
• (EPV20 protein)
• (lubricin)
• (Pluritene)
• (Par h 1 allergen)
• (Wnt11 gene product)
• (I-D-Gal-BSA)
• (mannose-bovine serum albumin conjugate)
• (acrosome granule lysin)
• (sulfatide activator)
• (vaccinia virus A34R protein)

gt More than 118,000 UMLS concepts (25) are
classified under an intersection
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Ontological analysis of the intersection

• (defconcept Amino Acid, Peptide, or Protein
  Carbohydrate
• "834 instances. This conjunct includes two
  sibling types.
• A protein containing a carbohydrate."
• annotations ((Sugg.Name "carbohydrate-containin
  g-protein")
• (onto-status integrated))
• is-primitive (and protein
• (some has-component carbohydrate))
• context substances)

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Morphologies

• Names of anatomical morphologies are often
  polysemous
• Both a condition and the function that caused the
  condition ("inflammation", "ulcer", "fracture",
  "wound", "hyperplasia")
• Both an object and the function that produced the
  object ("neoplasm", "hemorrhage")
• Both an object O and the condition created in
  another object O' by O ("obstruction")
• For example "the fracture has been caused by a
  fall" vs. "the fracture is transverse" "the
  obstruction occurred in the jejunum" vs. "the
  obstruction has been removed"
• Conceptual analysis puts into evidence other
  issues concerning morphologies
• The dependence between a morphological condition,
  a function, and the related organ. For example,
  an "ulcer" (as a condition) of a stomach implies
  that the stomach embodies an ulceration function
  (an ulcer as a function)
• The mereological import of morphologies some are
  featured by an organ, some only by a part of an
  organ. For instance, an "ectopic heart" is wholly
  ectopic, but an "ulcerated stomach" is only
  partly ulcerated

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Morphologies analyzed

• a property ("color", "consistency", "thickness",
  "size", "number", "shape")
• a condition
• a topologically relevant condition
• an alteration of connection
• that creates a configuration (a new property) in
  an object ("fracture", "wound")
• in the holey interior of an object
  ("obstruction")
• between several objects ("fusion")
• an alteration of the boundary between an object
  holey interior and the object complement
• creating a configuration in the boundary
  ("cavitation", "ulcer")
• producing a substance flow ("hemorrhage",
  "ulcer")
• an abnormal placement ("dislocation", "ectopia",
  "absence")
• a form alteration condition ("deformity",
  "hyperplasia", "hypoplasia")
• a condition involving the alteration of several
  properties ("inflammation", "eruption")
• an abnormal, foreign object ("mass", "neoplasm",
  "calculus", "obstruction")

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Expliciting relations
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Medical source ontologies

• The UMLS top-level (1998 edition 132 "semantic
  types", 91 "relations", and 412 "templates"),
• The Snomed-III top-level (510 "terms" and 25
  "links"),
• GMN top-level (708 "terms"),
• The Icd10 top-level (185 "terms"), and
• The GALEN Core Model v.5h (2,730 "entities", 413
  "attributes" and 1,692 axioms), etc.
• The 1998 edition of the UMLS Metathesaurus
  (476,000 "concepts", 93,000 explicit templates,
  and 599,000 thesaurus-like templates)

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The current ON9.2 library
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The current top-level
40
Tool for representation
ONTOLINGUA Tool for representation and
classification LOOM Tool
for intermediate representation and
interchange OKBC Tool for
browsing and editing
ONTOSAURUS
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(No Transcript)
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Results

• ON9.2 integration of the medical top levels
  within a library of generic theories. It includes
  a set of 50 modules with about 1,500 concepts. It
  is available in both Ontolingua and Loom
  languages
• Explicitation of the Metathesaurus terminological
  knowledge intersections of UMLS semantic types,
  relations defined by sources (IS_A and other
  relations)
• Integration of the Metathesaurus intersections
  within ON9.2
• Contextualization of the Metathesaurus
• An integrated model of clinical guidelines

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What is a Domain Reference Ontology?

• An ontology usable to build new ontologies in a
  domain, or to plug existing ontologies in it
• Our research in medical conceptual structures
  aims at defining a Medical Reference Ontology
  (library)
• The current research in environmental metadata
  could be reconsidered as the construction of an
  Environmental Reference Ontology
• We are confident that our methodology is suitable
  to this task without substantial revision
• Warning at first sight, conceptual heterogeneity
  in environment seems harder than medicine

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• "Es gibt nichts praktischers als eine gute
  Theorie"
• (Ludwig von Boltzmann)

45

• "Es gibt nichts praktischers als eine gute
  Theorie"
• "There is nothing more practical than a good
  theory"
• (Ludwig von Boltzmann)

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References

• for generalities, the library, and conceptual
  investigations
• Gangemi A, Pisanelli DM, Steve G, "An overview of
  the ONIONS project Applying ontologies to the
  integration of medical terminologies", Data and
  Knowledge Engineering, 31 (1999), 183-220
• for the investigation of the UMLS
• Pisanelli DM, Gangemi A, Steve G, "An Ontological
  Analysis of the UMLS Metathesaurus", Journal of
  American Medical Informatics Association, vol. 5
  (symposium supplement), 1998
• for the pre-processing of informal terminological
  repositories
• Steve G, Gangemi A, Pisanelli DM, "Integrating
  Medical Terminologies with ONIONS Methodology",
  in Kangassalo H, Charrel JP (eds.) Information
  Modelling and Knowledge Bases VIII, Amsterdam,
  IOS Press 1997
• for the integration of clinical guidelines
• Pisanelli DM, Gangemi A, Steve G, "Toward a
  Standard for Guideline Representation an
  Ontological Approach", Journal of American
  Medical Informatics Association, vol. 6
  (symposium supplement), 1999