Bertram Lud ä scher LUDAESCH@SDSC.EDU

1. Department of Computer Science & Engineering University of California, San DiegoCSE-291:Ontologies in Data and Process IntegrationSpring 2004 Bertram Ludäscher LUDAESCH@SDSC.EDU

2. Overview • Introduction to ontologies: • What are ontologies (and some related formalisms”)? • How do we represent ontologies? • What can we do with them/to them? • Introduction to some specific formalisms • Logic, Description Logics, OWL, FCA, TMs, ... • New themes / possible topics (vs. Spring ’03): • querying concept graphs • use of ontologies in query processing (specifically mediation) • process ontologies, capturing procedural knowledge • philosophical approaches • Some guest lectures • “Class Action”: • Theoretical studies: • surveying/comparing/analyzing approaches (based on research literature) • Practical studies, e.g., experiments with reasoning tools and graph querying tools

3. Overview … • Today • Introduction to ontologies • Example application • Description logic (first steps) • Next week: • More on DL, FOL, reasoning, …

4. Ontologies in Data and Information Integration

5. addall.com ? Information Integration barnes&noble.com A1books.com amazon.com half.com An Online Shopper’s Information Integration Problem El Cheapo: “Where can I get the cheapest copy (including shipping cost) of Wittgenstein’s Tractatus Logicus-Philosophicus within a week?” “One-World” Mediation

6. ? Information Integration Crime Stats Demographics Realtor School Rankings A Home Buyer’s Information Integration Problem What houses for sale under $500k have at least 2 bathrooms, 2 bedrooms, a nearby school ranking in the upper third, in a neighborhood with below-average crime rate and diverse population? “Multiple-Worlds” Mediation

7. ? Information Integration sequence info (CaPROT) protein localization (NCMIR) morphometry (SYNAPSE) neurotransmission (SENSELAB) Biomedical Informatics Research Network http://nbirn.net A Neuroscientist’s Information Integration Problem What is the cerebellar distribution of rat proteins with more than 70% homology with human NCS-1? Any structure specificity? How about other rodents? “Complex Multiple-Worlds” Mediation

9. Standard (XML-Based) Mediator Architecture USER/Client Query Q ( G (S1,..., Sk) ) Integrated Global (XML) View G Integrated View Definition G(..) S1(..)…Sk(..) MEDIATOR (XML) Queries & Results (XML) View (XML) View (XML) View wrappers implemented as web services Wrapper Wrapper Wrapper S1 S2 Sk

10. Biomedical Informatics Research Network http://nbirn.net Some BIRNing Data Integration Questions • Data Integration Approaches: • Let’s just share data, e.g., link everything from a web page! • ... or better put everything into an relational or XML database • ... and do remote access using the Grid • ... or just use Web services! • Nice try. But: • “Find the files where the amygdala was segmented.” • “Which other structures were segmented in the same files?” • “Did the volume of any of those structures differ much from normal?” • “What is the cerebellar distribution of rat proteins with more than 70% homology with human NCS-1? Any structure specificity? How about other rodents?”

11. Heterogeneous Data integration • Requires advanced metadata and processing • Attributes must be semantically typed • Collection protocols must be known • Units and measurement scale must be known • Measurement relationships must be known • e.g., that ArealDensity=Count/Area

12. Semantics Structure Syntax • reconciling S4heterogeneities • “gluing” together multiple data sources • bridging information and knowledge gaps computationally System aspects Information Integration Challenges • System aspects: “Grid” Middleware • distributed data & computing • Web Services, WSDL/SOAP, … • sources = functions, files, databases, … • Syntax & Structure: XML-Based Mediators • wrapping, restructuring • XML queries and views • sources = XML databases • Semantics: Model-Based/Semantic Mediators • conceptual models and declarative views • SemanticWeb/KnowledgeGrid stuff: ontologies, description logics (RDF(S), DAML+OIL, OWL ...) • sources = knowledge bases (DB+CMs+ICs)

13. Information Integration from a DB Perspective • Information Integration Problem • Given: data sources S1, ..., Sk (DBMS, web sites, ...) and user questions Q1,..., Qn that can be answered using the Si • Find: the answers to Q1, ..., Qn • The Database Perspective: source = “database” • Si has a schema (relational, XML, OO, ...) • Sican be queried • define virtual (or materialized) integrated views Vover S1 ,..., Skusing database query languages(SQL, XQuery,...) • questions become queries Qi against V(S1,..., Sk)

14. What’s the Problem with XML & Complex Multiple-Worlds? • XML is Syntax • DTDs talk about element nesting • XML Schema schemas give you data types • need anything else? => write comments! • Domain Semantics is complex: • implicit assumptions, hidden semantics • sources seem unrelated to the non-expert • Need Structure and Semantics beyond XML trees! • employ richer OO models • make domain semantics and “glue knowledge” explicit • use ontologies to fix terminology and conceptualization • avoid ambiguities by using formal semantics

15. Integrated-DTD := XML-QL(Src1-DTD,...) Integrated-CM := CM-QL(Src1-CM,...) Ontologies DMs, PMs Logical Domain Constraints No Domain Constraints IF  THEN  IF  THEN  IF  THEN  Structural Constraints (DTDs), Parent, Child, Sibling, ... Classes, Relations, is-a, has-a, ... C1 A = (B*|C),D B = ... C2 R C3 . . .... .... .... XML Elements .... (XML) Objects XMLModels Raw Data Raw Data ConceptualModels Raw Data XML-Based vs. Model-Based Mediation CM ~ {Descr.Logic, ER, UML, RDF/XML(-Schema), …} CM-QL ~ {F-Logic, DAML+OIL, …}

16. Knowledge Representation:Relating Theory to the World via Formal Models Source: John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations “All models are wrong, but some models are useful!”

17. What is an ontology (and what is it good for)? And the answer is ...

18. Glossary (wordreference.com) • ontologynoun1  (Philosophy)  the branch of metaphysics that deals with the nature of being2  (Logic)  the set of entities presupposed by a theory • taxonomynoun1a  the branch of biology concerned with the classification of organisms into groups based on similarities of structure, origin, etc.b  the practice of arranging organisms in this way2  the science or practice of classification [ETYMOLOGY: 19th Century: from French taxonomie, from Greek taxis order + -nomy] • thesaurusnoun(plural:  -ruses, -ri [-raı])1  a book containing systematized lists of synonyms and related words2  a dictionary of selected words or topics3  (rare) a treasury[ETYMOLOGY: 18th Century: from Latin, Greek: treasure]

19. Glossary (wordreference.com) • conceptnoun1  an idea, esp. an abstract ideaexample: the concepts of biology2  (Philosophy)  a general idea or notion that corresponds to some class of entities and that consists of the characteristic or essential features of the class3  (Philosophy) a  the conjunction of all the characteristic features of something b  a theoretical construct within some theory c  a directly intuited object of thought d the meaning of a predicate4  [modifier]  (of a product, esp. a car) created as an exercise to demonstrate the technical skills and imagination of the designers, and not intended for mass production or sale[ETYMOLOGY: 16th Century: from Latin conceptum something received or conceived, from concipere to take in, conceive] • contingent adjective1  [when postpositive, often foll by on or upon]  dependent on events, conditions, etc., not yet known; conditional2  (Logic)  (of a proposition) true under certain conditions, false under others; not necessary3  (in systemic grammar) denoting contingency (sense 4)4  (Metaphysics)  (of some being) existing only as a matter of fact; not necessarily existing5  happening by chance or without known cause; accidental6  that may or may not happen; uncertain • glossary noun (plural:  -ries); an alphabetical list of terms peculiar to a field of knowledge with definitions or explanations. Sometimes called: gloss[ETYMOLOGY: 14th Century: from Late Latin glossarium; see gloss2]

20. 1st Attempt: Ontologies in CS • An ontology is ... • an explicit specification of a conceptualization[Gruber93] • a shared understanding of some domain of interest [Uschold, Gruninger96] • Some aspects and parameters: • a formal specification (reasoning and “execution”) • ... of a conceptualization of a domain (community) • ... of some part of world that is of interest (application) • Provides: • A common vocabulary of terms • Some specification of the meaning of the terms (semantics) • A shared “understanding” for people and machines

21. Ontology as a philosophical discipline • Ontology as a philosophical discipline, which deals with the nature and the organization of reality: • Ontology as such is usually contrasted with Epistemology, which deals with the nature and sources of our knowledge [a.k.a. Theory of Knowledge]. Aristotle defined Ontology as the science of being as such: unlike the special sciences, each of which investigates a class of beings and their determinations, Ontology regards all the species of being qua being and the attributes which belong to it qua being" (Aristotle, Metaphysics, IV, 1). • In this sense Ontology tries to answer to the question: What is being? What exists? (the nature of being, not an enumeration of “stuff” around us…)

22. Some different uses of the word “Ontology” [Guarino’95] 1. Ontology as a philosophical discipline 2. Ontology as a an informal conceptual system 3. Ontology as a formal semantic account 4. Ontology as a specification of a “conceptualization” 5. Ontology as a representation of a conceptual system via a logical theory 5.1 characterized by specific formal properties 5.2 characterized only by its specific purposes 6. Ontology as the vocabulary used by a logical theory 7. Ontology as a (meta-level) specification of a logical theory http://ontology.ip.rm.cnr.it/Papers/KBKS95.pdf

23. Ontologies vs Conceptualizations • Given a logical language L ... • ... a conceptualization is a set of models of L which describes the admittable (intended) interpretations of its non-logical symbols (the vocabulary) • ... an ontology is a (possibly incomplete) axiomatization of a conceptualization. set of all models M(L) logic theories (consistent sets of sentences; closed under logical consequence) ontology conceptualization C(L) [Guarino96] http://www-ksl.stanford.edu/KR96/Guarino-What/P003.html

24. Ontologies vs Knowledge Bases • An ontology is a particular KB, describing facts assumed to be always true by a community of users: • in virtue of the agreed-upon meaning of the vocabulary used (analytical knowledge): • black => not white • ... whose truth does not descend from the meaning of the vocabulary used (non-analytical, common knowledge) • Rome is the capital of Italy • An arbitrary KB may describe facts which are contingently true, and relevant to a particular epistemic state: • Mr Smith’s pathology is either cirrhosis or diabetes

25. Formal Ontology [Guarino’96] • Theory of formal distinctions • among things • among relations • Basic tools • Theory of parthood • What counts as a part of a given entity? What properties does the part relation have? Are the different kinds of parts? • Theory of integrity • What counts as a whole? In which sense are its parts connected? • Theory of identity • How can an entity change while keeping its identity? What are its essential properties? Under which conditions does an entity loose its identity? Does a change of “point of view” change the identity conditions? • Theory of dependence • Can a given entity exist alone, or does it depend on other entities?

26. Ontology: Definition and Scope [Sowa] • 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. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D. An uninterpreted logic, such as predicate calculus, conceptual graphs, or KIF, is ontologically neutral. It imposes no constraints on the subject matter or the way the subject may be characterized. By itself, logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest. http://users.bestweb.net/~sowa/ontology/index.htm

27. Ontology: Definition and Scope [Sowa] • An informal ontology may be specified by a catalog of types that are either undefined or defined only by statements in a natural language. A formal ontology is specified by a collection of names for concept and relation types organized in a partial ordering by the type-subtype relation. Formal ontologies are further distinguished by the way the subtypes are distinguished from their supertypes: an axiomatized ontology distinguishes subtypes by axioms and definitions stated in a formal language, such as logic or some computer-oriented notation that can be translated to logic; a prototype-based ontology distinguishes subtypes by a comparison with a typical member or prototype for each subtype. Large ontologies often use a mixture of definitional methods: formal axioms and definitions are used for the terms in mathematics, physics, and engineering; and prototypes are used for plants, animals, and common household items. . http://users.bestweb.net/~sowa/ontology/index.htm

28. Why develop an ontology? • To make domain assumptions explicit • Easier to change domain assumptions • Easier to understand, update, and integrate legacy data  data integration • To separate domain knowledge from operational knowledge • Re-use domain and operational knowledge separately • A community reference for applications • To share a consistent understanding of what information means. [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

29. What is being shared? Metadata • Data describing the content and meaning of resources and services. • But everyone must speak the same language… Terminologies • Shared and common vocabularies • For search engines, agents, curators, authors and users • But everyone must mean the same thing… Ontologies • Shared and common understanding of a domain • Essential for search, exchange and discovery  Ontologies aim at sharing meaning [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

30. Concept “Jaguar“ Origin and History • Humans require words (or at least symbols) to communicate efficiently. The mapping of words to things is indirect. We do it by creating concepts that refer to things. • The relation between symbols and things has been described in the form of the meaning triangle: Ogden, C. K. & Richards, I. A. 1923. "The Meaning of Meaning." 8th Ed. New York, Harcourt, Brace & World, Inc before: Frege, Peirce; see [Sowa 2000] [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

31. Human and machine communication [Maedche et al., 2002] • ... Human Agent 1 Human Agent 2 Machine Agent 1 Machine Agent 2 exchange symbol, e.g. via nat. language exchange symbol, e.g. via protocols Ontology Description Symbol ‘‘JAGUAR“ Formal Semantics Formal models Internal models commit commit Concept Meaning Triangle MA1 MA2 HA2 HA1 commit Ontology commit a specific domain, e.g. animals Things

32. animal domestic vermin dog cat cow rodent eats mouse An explicit description of a domain • Concepts(class, set, type, predicate) • event, gene, gammaBurst, atrium, molecule, cat • Properties of concepts and relationships between them (slot) • Taxonomy: generalisation ordering among concepts isA, partOf, subProcess • Relationship, Role or Attribute: functionOf, hasActivity location, eats, size [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

33. Concepts • Primitive concepts: • properties are necessary • Globular protein must have hydrophobic core (but a protein with a hydrophobic core need not be a globular protein) • GlobularProtein ⊑ has-a.HydrophobicCore • Defined concepts: • properties are necessary + sufficient • Eukaryotic cells must have a nucleus. • EukaryoticCell  has-a.Nucleus • Every cell that contains a nucleus must be Eukaryotic. [Robert Stevens]

34. What is a concept? Different communities have different notions on what a concept means: • Formal concept analysis (see http://www.math.tu-dresden.de/~ganter/fba.html) talk about formal concepts • Description Logics (see http://dl.kr.org/): They talk about concept labels • ISO-704:2000 – Terminology Work: (see http://www.iso.ch/) • Often the classical notion of a frame in AI or a class in OO modeling is seen as equivalent to a concept.

35. Formal Concept Analysis (FCA) Formal Concept Analysis [Sowa, http://users.bestweb.net/~sowa/misc/mathw.htm] Concept Lattice

36. animal domestic vermin dog cat cow rodent eats mouse An explicit description of a domain • Constraints or axioms on properties and concepts: • value: integer • domain: cat • cardinality: at most 1 • range: 0 <= X <= 100 • oligonucleiotides < 20 base pairs • cows are larger than dogs • cats cannot eat only vegetation • cats and dogs are disjoint • Values or concrete domains • integer, strings • 20, trypotoplan-synthetase [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

37. animal domestic vermin dog cat cow rodent eats mouse An explicit description of a domain • Individuals or Instances • sulphur, trpA Gene, felix • Nominals • Concepts that cannot have instances • Instances that are used in conceptual definitions • ItalianDog = Dog bornIn Italy • Instances • An ontology = concepts+properties+axioms+values+nominals • A knowledge base = ontology+instances felix tom mickey jerry [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

38. Lightweight Concepts, atomic types Is-a hierarchy Relationships between concepts Heavyweight Metaclasses Type constraints on relations Cardinality constraints Taxonomy of relations Reified statements Axioms Semantic entailments Expressiveness Inference systems Light and Heavy expressivity A matter of rigour and representational expressivity [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

39. A semantic continuum [Mike Uschold, Boeing Corp] Pump: “a device for moving a gas or liquid from one place or container to another” (pump has (superclasses (…)) Shared human consensus Semantics hardwired; used at runtime Semantics processed and used at runtime Text descriptions Implicit Informal (explicit) Formal (for humans) Formal (for machines) • Further to the right means: • Less ambiguity • More likely to have correct functionality • Better inter-operation (hopefully) • Less hardwiring • More robust to change • More difficult

40. Some Ontologies and “Ontologies” (coming soon to a project near you)

41. SMART (Meta)data I: Logical Data Views Adoption of a standard (meta)data model => wrap data sets into unified virtual views Source: NADAM Team (Boyan Brodaric et al.)

42. “smart discovery & querying” via multiple, independent concept hierarchies (controlled vocabularies) • data at different description levels can be found and processed SMART Metadata II: Multihierarchical Rock Classification for “Thematic Queries” (GSC) –– or: Taxonomies are not only for biologists ... Genesis Fabric Composition Texture

43. Biomedical Informatics Research Network http://nbirn.net SMART Metadata III:Source Contextualization & Ontology Refinement Focused GEON ontology working meeting last week ... (GEON, SCEC/KR, GSC, ESRI)

44. Gene Ontology http://www.geneontology.org • “a dynamic controlled vocabulary that can be applied to all eukaryotes” • Built by the community for the community. • Three organising principles: • Molecular function, Biological process, Cellular component • Isa and Part of taxonomy – but not good! • ~10,000 concepts • Lightweight ontology, Poor semantic rigour. Ok when small and used for annotation. Obstacle when large, evolving and used for mining.

45. Controlled vocabulary • AGROVOC: Agricultural Vocabulary

46. AN APPLICATION OF ONTOLOGIES:An Ontology-Driven Framework for Data Transformation in Scientific Workflows (from DILS’04) Shawn Bowers Bertram Ludäscher San Diego Supercomputer Center University of California, San Diego

47. Outline • Background (SEEK Project) • Scientific Workflows • The Problem: Reusing Structurally Incompatible Services • The Ontology-Driven Framework • Future Work

48. Outline • Background (SEEK Project) • Scientific Workflows • The Problem: Reusing Structurally Incompatible Services • The Ontology-Driven Framework • Future Work

49. this paper Science Environment for Ecological Knowledge (SEEK) • Domain Science Driver • Ecology (LTER), biodiversity, … • Analysis & Modeling System • Design and execution of ecological models and analysis • End user focus • {application,upper}-ware • Semantic Mediation System • Data Integration of hard-to-relate sources and processes • Semantic Types and Ontologies • upper middleware • EcoGrid • Access to ecology data and tools • {middle,under}-ware Architecture (cf. US cyberinfrastructure, UK e-Science)

50. Outline • The SEEK Project • Scientific Workflows • Focus: analysis & component integration on top of data integration • The Problem: Reusing Structurally Incompatible Services • The Ontology-Driven Framework • Future Work