Bertram Ludäscher LUDAESCH@SDSC.EDU Department of Computer Science & Engineering University of California, San Diego CSE-291:Ontologies in Data and.

Bertram Ludäscher LUDAESCH@SDSC.EDU
Department of Computer Science & Engineering University of California, San Diego CSE-291:Ontologies in Data and Process Integration Spring 2004 Bertram Ludäscher

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

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

Ontologies in Data and Information Integration

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?” addall.com ? Information Integration “One-World” Mediation protloc = NCMIR, excel + images morphometry (measurement) = NCMIR, excel + txt +images neurotrans (stimulate then electrical responses, probes) = RDB, SENSELAB, Yale CaBP (chemical structure, PDB links, function of CaBP, found-in...) = Web, Vanderbilt U Expasy (Protein-info as Sequence data) = Web, Europe amazon.com A1books.com half.com barnes&noble.com

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? ? Information Integration “Multiple-Worlds” Mediation protloc = NCMIR, excel + images morphometry (measurement) = NCMIR, excel + txt +images neurotrans (stimulate then electrical responses, probes) = RDB, SENSELAB, Yale CaBP (chemical structure, PDB links, function of CaBP, found-in...) = Web, Vanderbilt U Expasy (Protein-info as Sequence data) = Web, Europe Realtor Demographics School Rankings Crime Stats

A Neuroscientist’s Information Integration Problem
Biomedical Informatics Research Network 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 ? Information Integration protloc = NCMIR, excel + images morphometry (measurement) = NCMIR, excel + txt +images neurotrans (stimulate then electrical responses, probes) = RDB, SENSELAB, Yale CaBP (chemical structure, PDB links, function of CaBP, found-in...) = Web, Vanderbilt U Expasy (Protein-info as Sequence data) = Web, Europe protein localization (NCMIR) neurotransmission (SENSELAB) sequence info (CaPROT) morphometry (SYNAPSE)

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

Some BIRNing Data Integration Questions
Biomedical Informatics Research Network 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?”

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 -graphic is a very simplified case of heterogeneous integration -could be much more complex -semantic typing allows attributes that look dissimilar to be matched

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) Syntax Structure Semantics System aspects reconciling S4 heterogeneities “gluing” together multiple data sources bridging information and knowledge gaps computationally

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, ...) Si can be queried define virtual (or materialized) integrated views V over S1 ,..., Sk using database query languages (SQL, XQuery,...) questions become queries Qi against V(S1,..., Sk)

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

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

“All models are wrong, but some models are useful!”
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!”

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

Glossary (wordreference.com)
ontology noun 1 (Philosophy) the branch of metaphysics that deals with the nature of being 2 (Logic) the set of entities presupposed by a theory taxonomy noun 1 a 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 way 2 the science or practice of classification [ETYMOLOGY: 19th Century: from French taxonomie, from Greek taxis order + -nomy] thesaurus noun (plural: -ruses, -ri [-raı]) 1 a book containing systematized lists of synonyms and related words 2 a dictionary of selected words or topics 3 (rare) a treasury[ETYMOLOGY: 18th Century: from Latin, Greek: treasure]

Glossary (wordreference.com)
concept noun 1 an idea, esp. an abstract idea example: the concepts of biology 2 (Philosophy) a general idea or notion that corresponds to some class of entities and that consists of the characteristic or essential features of the class 3 (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 predicate 4 [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 adjective 1 [when postpositive, often foll by on or upon] dependent on events, conditions, etc., not yet known; conditional 2 (Logic) (of a proposition) true under certain conditions, false under others; not necessary 3 (in systemic grammar) denoting contingency (sense 4) 4 (Metaphysics) (of some being) existing only as a matter of fact; not necessarily existing 5 happening by chance or without known cause; accidental 6 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]

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

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…)

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

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]

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

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?

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.

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. .

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]

Sharing data  Sharing meaning
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 The WWW has made data available: Ready publication An infrastructure for retrieving and representing documents An infrastructure for accessing data Next Step is semantic interoperation: Understanding what the data means Linking in insightful ways Automated support for integration Sharing data  Sharing meaning Ontologies are often used as controlled vocabularies To share and integrate information you must describe it. Ontology: From knowledge representation and philosophy A rigorous and explicit conceptualisation of knowledge Linked to words to render the concepts Shared controlled vocabulary Used for: Complex and expressive conceptual descriptions of data Subject classifications Reasoning and inferring new knowledge Sharing & exchanging knowledge Much of the biological data is self-described marked up text (pre-dating XML), and hence ontologies for disambiguating database entries and annotation is accepted as standard practice. Disambiguating content. [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

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: “Jaguar“ Concept Ogden, C. K. & Richards, I. A "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]

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 Ontology Description exchange symbol, e.g. via protocols Symbol ‘‘JAGUAR“ Formal Semantics Internal models Formal models commit commit Concept Meaning Triangle MA1 MA2 HA1 HA2 commit Ontology commit a specific domain, e.g. animals Things

An explicit description of a domain
animal rodent cow cat mouse eats dog domestic vermin 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 Concepts: aka class, set, type, predicate Relations and attributes: aka slots [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

Concepts Primitive concepts: Defined 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]

What is a concept? Different communities have different notions on what a concept means: Formal concept analysis (see talk about formal concepts Description Logics (see They talk about concept labels ISO-704:2000 – Terminology Work: (see Often the classical notion of a frame in AI or a class in OO modeling is seen as equivalent to a concept.

Formal Concept Analysis (FCA)
[Sowa, Concept Lattice

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 animal rodent cow cat mouse eats dog domestic vermin [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

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 animal rodent cow cat mouse eats dog domestic vermin felix tom mickey jerry [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

Light and Heavy expressivity
A matter of rigour and representational expressivity 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 [Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]

A semantic continuum Implicit Informal Formal Formal
[Mike Uschold, Boeing Corp] A semantic continuum 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

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

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.)

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 ... “smart discovery & querying” via multiple, independent concept hierarchies (controlled vocabularies) data at different description levels can be found and processed Genesis Fabric Composition Texture

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

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.

Controlled vocabulary
AGROVOC: Agricultural Vocabulary

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

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

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 this paper Architecture (cf. US cyberinfrastructure, UK e-Science)

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

Promoter Identification in Kepler [SSDBM’03]
Problems Many components (web serivces) are NOT designed to fit! “The problem P that X solves is simple, and X doesn’t solve it well” Semantically meaningful connections are structurally incompatible Approach Distinguish structural type and semantic type Structural type: e.g. XML Schema Semantic type: e.g. OWL expressions Exploit the (optional!) semantic type as much as possible

A Very Simple Scientific Workflow
S1 (life stage property) S2 (mortality rate for period) P4 Simple conceptual workflow, connecting two services

S1 (life stage property) S2 (mortality rate for period) P4 observations Phase Observed Eggs Instar I Instar II Instar III Instar IV Adults 44,000 3,513 2,529 1,922 1,461 1,300 Population samples for life stages of the common field grasshopper [Begon et al, 1996]

S1 (life stage property) S2 (mortality rate for period) P4 life stage periods observations Phase Observed Period Phases Nymphal {Instar I, Instar II, Instar III, Instar IV} Eggs Instar I Instar II Instar III Instar IV Adults 44,000 3,513 2,529 1,922 1,461 1,300 Periods of development in terms of phases Population samples for life stages of the common field grasshopper [Begon et al, 1996]

S1 (life stage property) S2 (mortality rate for period) [(nymphal, 0.44)] P4 k-value for each period of observation life stage periods observations Phase Observed Period Phases Nymphal {Instar I, Instar II, Instar III, Instar IV} Eggs Instar I Instar II Instar III Instar IV Adults 44,000 3,513 2,529 1,922 1,461 1,300 Periods of development in terms of phases Population samples for life stages of the common field grasshopper [Begon et al, 1996]

Scientific Workflows A scientific workflow consists of a network of connected services … A service can be any software component (including a web service or even a data source) … Each service (optionally) takes input and (optionally) produces output

S1 (life stage property)
Scientific Workflows SEEK adopts a Ptolemy II “workflow” model: A service is called an actor Each actor has zero or more input and output ports (and possibly parameters) Data flows through a workflow based on connections made from output to input ports (ignored here: different models of computation, directors, …) P2 P3 P5 P1 S1 (life stage property) S2 (mortality rate for period) P4

Service Reusability A scientist wishes to connect two (independent) services Source Service Desired Connection Target Service Ps Pt

Service Reusability In Ptolemy II/Kepler (and in web services), input and output ports (message parts) have structural types (XML Schema) Structural Type Ps Structural Type Pt Source Service Desired Connection Target Service Ps Pt

Service Reusability Unless “designed to fit,” independent services are structurally incompatible  Generally, the source output type will not be a subtype of the target input type Incompatible Structural Type Ps Structural Type Pt (⋠) Source Service Desired Connection Target Service Ps Pt

Service Reusability A transformation mapping () is required to connect the services … artificially creating subtype compatibility If such a  exists, the services are “structurally feasible” Incompatible Structural Type Ps Structural Type Pt (⋠)  (≺) (Ps) Source Service Desired Connection Target Service Ps Pt

Service Reusability SEEK annotates services with semantic types for discovery and interoperability of services Ontologies (OWL) Compatible (⊑) Semantic Type Ps Semantic Type Pt Source Service Desired Connection Target Service Ps Pt

Service Reusability Services can be semantically compatible, but structurally incompatible Ontologies (OWL) Compatible (⊑) Semantic Type Ps Semantic Type Pt Incompatible Structural Type Ps Structural Type Pt (⋠)  (≺) (Ps) Source Service Desired Connection Target Service Ps Pt

Example Structural Types (XML)
structType(P2) structType(P3) root population = (sample)* elem sample = (meas, lsp) elem meas = (cnt, acc) elem cnt = xsd:integer elem acc = xsd:double elem lsp = xsd:string root cohortTable = (measurement)* elem measuremnt = (phase, obs) elem phase = xsd:string elem obs = xsd:integer <population> <sample> <meas> <cnt>44,000</cnt> <acc>0.95</acc> </meas> <lsp>Eggs</lsp> </sample> … <cohortTable> <measurement> <phase>Eggs</cnt> <obs>44,000</acc> </measurement> … P2 P3 P5 P1 S1 (life stage property) S2 (mortality rate for period) P4

Example Semantic Types
Portion of SEEK measurement ontology appliesTo MeasContext hasContext 0:* 1:1 hasProperty itemMeasured MeasProperty Observation Entity 0:* 1:* Ecological Property Accuracy Qualifier Spatial Location hasLocation Abundance Count LifeStage Property 1:1 hasValue 1:1 hasCount Numeric Value 1:1

Portion of SEEK measurement ontology appliesTo MeasContext Same in OWL, a description logic standard (here, Sparrow syntax): Observation subClassOf forall hasContext/MeasContext and forall hasProperty/MeasProperty and exists itemMeasured/Entity. MeasContext subClassOf exists appliesTo/Entity and atmost 1/appliesTo. EcologicalProperty subClassOf Entity. LifeStageProperty subClassOf EcologicalProperty. AbundanceCount subClassOf EcologicalProperty and exists hasLocation/SpatialLocation and atMost 1/hasLocation and exists hasCount/NumericValue and atMost 1/hasCount. hasContext 0:* 1:1 hasProperty itemMeasured MeasProperty Observation Entity 0:* 1:* Ecological Property Accuracy Qualifier Spatial Location hasLocation Abundance Count LifeStage Property 1:1 hasValue 1:1 hasCount Numeric Value 1:1

Semantic types for P2 and P3 MeasContext Observation hasContext appliesTo LifeStage Property 1:1 1:1 semType(P3) itemMeasured Abundance Count hasCount Number Value 1:1 1:1 1:1 ⊑ hasValue hasProperty semType(P2) Accuracy Qualifier Semtypes over a common (or interlinked) ontology. Subsumption reasoning needed. 1:1 P2 P3 P5 P1 S1 (life stage property) S2 (mortality rate for period) P4

Semantic types for P2 and P3 MeasContext semType(P3) subClassOf Observation and exists hasContext/(MeasurementContext and exists appliesTo/LifeStageProperty and atMost 1/appliesTo) and exists itemMeasured/AbundanceCount and atMost 1/itemMeasured. semType(P2) subClassOf Observation and exists hasContext/(MeasurementContext and exists appliesTo/LifeStageProperty and atMost 1/appliesTo) and exists itemMeasured/AbundanceCount and atMost 1/itemMeasured and exists hasProperty/AccuracyQualifier and atMost 1/hasProperty. Observation hasContext appliesTo LifeStage Property 1:1 1:1 semType(P3) itemMeasured Abundance Count hasCount Number Value 1:1 1:1 1:1 ⊑ hasValue hasProperty semType(P2) Accuracy Qualifier 1:1 P2 P3 P5 P1 S1 (life stage property) S2 (mortality rate for period) P4

The Ontology-Driven Framework
Define semantic registration mappings (“semantic views”) to connect structural and semantic types Use registration mappings to (semi-) automate transformation, based on derived structural correspondences Depending on the ontologies and registration mappings, it may not be possible to find an appropriate  … (since the correspondence is often under-specified)

Ontologies (OWL) Compatible (⊑) Semantic Type Ps Semantic Type Pt Registration Mapping (Input) Registration Mapping (Output) Structural Type Ps Structural Type Pt Source Service Target Service Ps Desired Connection Pt

Registration Example (simple XPaths)
structType(P2) <population> <sample> <meas> <cnt>44,000</cnt> <acc>0.95</acc> </meas> <lsp>Eggs</lsp> </sample> … root population = (sample)* elem sample = (meas, lsp) elem meas = (cnt, acc) elem cnt = xsd:integer elem acc = xsd:double elem lsp = xsd:string /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo

structType(P2) <population> <sample> <meas> <cnt>44,000</cnt> <acc>0.95</acc> </meas> <lsp>Eggs</lsp> </sample> … root population = (sample)* elem sample = (meas, lsp) elem meas = (cnt, acc) elem cnt = xsd:integer elem acc = xsd:double elem lsp = xsd:string /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo Each sample is an instance of the semantic type

structType(P2) <population> <sample> <meas> <cnt>44,000</cnt> <acc>0.95</acc> </meas> <lsp>Eggs</lsp> </sample> … root population = (sample)* elem sample = (meas, lsp) elem meas = (cnt, acc) elem cnt = xsd:integer elem acc = xsd:double elem lsp = xsd:string /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo Each sample’s cnt represents the itemMeasured object

structType(P2) <population> <sample> <meas> <cnt>44,000</cnt> <acc>0.95</acc> </meas> <lsp>Eggs</lsp> </sample> … root population = (sample)* elem sample = (meas, lsp) elem meas = (cnt, acc) elem cnt = xsd:integer elem acc = xsd:double elem lsp = xsd:string /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo Each sample’s cnt’s value represents the hasCount value of the corresponding itemMeasured object

structType(P3) <cohortTable> <measurement> <phase>Eggs</cnt> <obs>44,000</acc> </measurement> … root cohortTable = (measurement)* elem measuremnt = (phase, obs) elem phase = xsd:string elem obs = xsd:integer /cohortTable/measurement == semType(P3) /cohortTable/measurement/obs == semType(P3).itemMeasured /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo … similary for P3 .. … .

Ontologies (OWL) Compatible (⊑) Semantic Type Ps Semantic Type Pt Registration Mapping (Input) Registration Mapping (Output) Structural Type Ps Structural Type Pt Correspondence Source Service Target Service Ps Desired Connection Pt

Correspondence Example
Source-side semantic registration mapping /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo Target-side semantic registration mapping /cohortTable/measurement == semType(P3) /cohortTable/measurement/obs == semType(P3).itemMeasured /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo population cohortTable sample * measurement * meas obs cnt xsd:integer xsd:integer phase acc xsd:string xsd:double lsp xsd:string

Source /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo Target /cohortTable/measurement == semType(P3) /cohortTable/measurement/obs == semType(P3).itemMeasured /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo We want to “compose” the registrations to obtain structural correspondences population cohortTable sample * measurement * meas obs cnt xsd:integer xsd:integer phase acc xsd:string xsd:double lsp xsd:string

Source /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo /population/sample == semType(P2) Target /cohortTable/measurement == semType(P3) /cohortTable/measurement/obs == semType(P3).itemMeasured /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo /cohortTable/measurement == semType(P3) population cohortTable semType(P2) subtype_of semtype(P3) sample * measurement * meas obs cnt xsd:integer xsd:integer phase acc xsd:string xsd:double lsp These fragments correspond xsd:string

Source /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo /population/sample/meas/cnt == semType(P2).itemMeasured Target /cohortTable/measurement == semType(P3) /cohortTable/measurement/obs == semType(P3).itemMeasured /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo /cohortTable/measurement/obs == semType(P3).itemMeasured population cohortTable sample * measurement * meas obs cnt xsd:integer xsd:integer phase acc xsd:string xsd:double lsp These fragments correspond xsd:string

Source /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount Target /cohortTable/measurement == semType(P3) /cohortTable/measurement/obs == semType(P3).itemMeasured /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount population cohortTable sample * measurement * meas obs cnt xsd:integer xsd:integer phase acc xsd:string xsd:double lsp These fragments correspond xsd:string

Source /population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured /population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount /population/sample/meas/acc == semType(P2).hasProperty /population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue /population/sample/lsp/text() == semType(P2).hasContext.appliesTo /population/sample/lsp/text() == semType(P2).hasContext.appliesTo Target /cohortTable/measurement == semType(P3) /cohortTable/measurement/obs == semType(P3).itemMeasured /cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo /cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo population cohortTable sample * measurement * meas obs cnt xsd:integer xsd:integer phase acc xsd:string xsd:double lsp These fragments correspond xsd:string

Ontologies (OWL) Compatible (⊑) Semantic Type Ps Semantic Type Pt Registration Mapping (Input) Registration Mapping (Output) Structural Type Ps Structural Type Pt Correspondence (Ps) Generate Source Service Transformation Target Service Ps Pt Desired Connection

Example Result (XQuery)
Based on the structural correspondences and certain assumptions, we derive the transformation XQuery: <cohortTable> { for $s in /population/sample return <measurement> { for $c in $s/meas/cnt return <obs>{$c/text()}</obs> } { for $l in $s/lsp return <phase>{$l/text()}</phase> } </measurement> } </cohortTable>

Assumptions Made (or why this may not work for you…)
Common XPath prefixes refer to the same element Elements in correspondences have compatible cardinalities source is equivalent or stricter than target (e.g., + is stricter than *) Primitive data types are compatible Counterexample: A(b1, b2, b3) mapped to A’(b) would generate 3 A’s: A’1(b1), A’2(B2), A’3(b3) --- which may not be desired

Bertram Ludäscher LUDAESCH@SDSC.EDU Department of Computer Science & Engineering University of California, San Diego CSE-291:Ontologies in Data and.

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Bertram Ludäscher LUDAESCH@SDSC.EDU Department of Computer Science & Engineering University of California, San Diego CSE-291:Ontologies in Data and.

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Presentation on theme: "Bertram Ludäscher LUDAESCH@SDSC.EDU Department of Computer Science & Engineering University of California, San Diego CSE-291:Ontologies in Data and."— Presentation transcript:

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