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On Spatial OntologiesGEOINFO 2004
Stefano Spaccapietra
Ecole Polytechnique Fédérale de Lausanne (EPFL)
School of Information and Communication Science
http://lbd.epfl.ch
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Preamble Suppose you are a salesperson, who wishes to find a Ms. Cook
you met at a trade conference last year. You don’t remember her first name but you remember she worked for one of your clients and her daughter is a student of your alma mater.
An intelligent search agent can search the Web and ignore pages relating to cooks, cookies, Cook Islands, etc. find pages of companies your clients are working for follow links to or find private home pages check whether a daughter is still in school match with students from your alma mater.
... Only if you already have the Semantic Web
© Volker Haarslev
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Preamble 2nd For foundational work on spatial ontologies
Andrew Frank, Ontology for Spatio-temporal Databases, in "Spatio-Temporal Databases - The Chorochronos Approach", LNCS 2520, Koubarakis, Sellis et al. (Eds.), Springer, 2003, pp.9-77
COSIT Conferences
For ongoing research on ontologies in general Semantic Web Conferences and Journal Conferences & Workshops in AI ODBASE Conferences ER Conferences
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Outline Why ontologies?
Ontologies and interoperability
Characteristic features of ontologies
Logic-based approaches to ontologies
On spatial ontologies
In favor of hybrid formalisms (CM + DL) and hybrid systems (DBMS + DL reasoner)
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Changing Focus of IT
20th Century: Data Processing
21st Century: Data Exchange
The fundamental issue has become Mutual Understanding
-->> Explicit Semantics
-->> Ontologies (not XML)
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Geo Interoperability Goal: Improve sharing of geographical sources
Web: maps, path finding, proximity search, geographic web services designed for end users for professionals: incompleteness of data, inconsistency among data
sources, high potential for misunderstanding for web agents: meaningless (poor usability)
Automatic merging of geodata sources misunderstanding, inconsistent sources
A shared machinable description of the semantics is needed machinable => formal
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Ontology (à la Ian Horrocks)
“An explicit specification of a conceptualisation"
Ontology: a means to share information and to achieve semantic interoperability between humans and computers
An ontology is an engineering artifact: It is constituted by a specific vocabulary used to describe a certain reality,
plus a set of explicit assumptions regarding the intended meaning of the
vocabulary.
An ontology describes a formal specification of a certain domain: Shared understanding of a domain of interest Formal and machine manipulable model of a domain of interest
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An Ontology is ...
somewhere (could be centralized or distributed) some set of (definitely not necessarily a partition) somehow related terms (ontology = language definition) whose use has to some extent been agreed upon preferably with some explanation of their meaning
Ontologies are also objects of interest (Universe of Discourse), e.g. for ontology engineering
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Without ontologies ... How do I know how to interpret:
Where do you come from ? (domain ambiguity) Geneva (the airport I started from) ? Lausanne or Switzerland (the place I’m living in) ? France (the country I am a citizen of) ? Milano (the place I was born) ?
I’ll have a cup of coffee (context dependent)
Would you consider paying 10’000 US$ to buy a bad painting ? (term ambiguity)
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
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Simple case: common, shared ontology
Needs services to define, store, retrieve, update, … the ontology
Ontological Agreement
information exchange
manages
Ontologyuses usesadministrator
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Cooperative Systems Autonomous ontologies
OntologyB
information exchange
MediationOntology
OntologyA Mediator
A B
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
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What's in an Ontology?Ontologies typically include:
Terms (names) for the important concepts in the domain Participant is a concept whose members are a kind of Person Author is a concept whose members are exactly those persons who write
published papers StudentParticipant is a concept whose members are exactly those
participants whose position is "student"
formally, these sentences are expressed as axioms defining the new concepts:- Participant is defined as a subconcept of Person - Author is defined as a restriction of Person based on the write role associated to persons- StudentParticipant is defined as a restriction of Person based on the position role associated to persons
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Person
Participant
StudentParticipant
Author
writes
position
writes PublishedPaper
position "student"
Visualization (Protégé)
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What's in an Ontology? Background knowledge (general rules)
Constraints on the domain StudentParticipants pay a reduced registration fee
StudentParticipants must have a supervisor
No individual can be both a Reviewer and an Author for the same paper
Instances/Individuals Stefano : Participant
Stefano : Author
TBox
ABox
Terminological axioms
Assertional axioms
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Taxonomic Ontologies
sophisticated dictionary/thesaurus organized collection of terms some semantic links (synonymy, etc.) generalization/specialization hierarchy
example: Wordnet
They provide a reference vocabulary
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Wordnet
entity, physical thing
object, physical object
living thing, animate thing
organism, being
animal, animate being, beast, brute, creature, fauna
.... mammal
placental mammal, eutherian
ungulate, hoofed animal
odd-toed ungulate, perissodactyl
equine, equid
horse, Equus, caballus
saddle horse, riding horse..
Horse : a solid hoofed herbivorous quadrupeddomesticated since prehistoric times
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Descriptive Ontologies concepts are worth a description
beyond how to denote them (terms)
which characteristic properties?
which characteristic relationships?
They provide description of information that may
be available on the concept information to align existing data structures patterns to define new specialized ontologies
--> same as conceptual modeling?
Lake
name (1:n)depth f( ) (1:1)harbors (0:n)beaches (0:n)incomingRiver (0:n)outgoing riverislands (0:n)........
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Ontologies and DB Similarities, but important differences
DB target data management, for a given organization prescribe how the world of interest is (Closed World
Assumption)
Ontologies target data description, for the largest community describe what is known about the real world (Open World
Assumption)
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Ontology Design In a web services perspective ...
Ontology design is collaborative Ontology design is incremental
==> needs reasoning services to check consistency of the specifications to accurately integrate new knowledge to infer all inferable knowledge
Formal reasoning: Logics
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CWA / OWA reasoning DL:
Parent = instances of Person that have at least one child john: Parent
=> john is an instance of Person john has at least one role hasChild (unknown)
DB: john: Parent rejected
Person
hasChild
Parent ChildparentOf 1:n
childOf 0:n
DBDL
Parent
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CWA / OWA reasoning DL: john has at most 2 children
john has a child: peter john has a child: mary john has a child: paul
=> (paul should be the same as peter) or (paul should be the same as mary) or (mary should be the same as peter)
Open world assumption + no unique name assumption
(but implemented reasoners have unique name assumption)
DB: john has at most 2 children john has a child: peter john has a child: mary john has a child: paul => rejected
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Consistency checking Satisfiability
Parent (Person hasChild.Person) Woman (Person Female) Mother (Female Parent)
GayMother (Woman Mother)
GayMother can never be satisfied (instantiated)
A concept C is satisfiable iff there exists an interpretation I such that CI ≠ Ø (I is called a model of C)
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Subsumption reasoning concept: Participant
role: speaks (Participant, Language)
InternationalParticipant (Person speaks.Language = English speaks.Language = Portuguese)
LocalParticipant (Person speaks.Language = Portuguese)
Also at the instance level:Gilberto: LocalParticipant => Gilberto: InternationalParticipant
Participant
InternationalParticipant
LocalParticipant
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Subsumption reasoning concept: Person
role: hasAge (Person, Integer)
Adult (Person hasAge > 30)
Senior (Person hasAge > 60)
NOT POSSIBLE
Person
Adult
Senior
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Subsumption reasoning concept: Person
Parent (Person hasChild.Person)
Woman (Person Female)
Mother (Parent Female)
subsumption can be reduced to satisfiability
Female
Woman
Person
Parent
Mother
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Inference: Case reasoning Does John have a female friend loving a male person?
John
LOVES
Male Human (Female)
Bill: Male
FRIEND
Andrea Susan: Female
LOVES
FRIEND
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Logics First Order Logic (FOL) is not decidable
Description Logics are decidable subsets of FOL no free variables axioms: knowledge representation and reasoning problem: scalability of DL reasoners
Horn-Logic is another decidable subsets of FOL only one negation rules: deduction
F-Logic: a Horn-Logic supporting frames
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Description Logics
Decidable subsets of FOL (no free variables) Designed for knowledge representation and ontological
reasoning
Many variants (different compromises between expressive
power, decidability, and complexity of reasoning)
Very popular with the AI-Ontology community
Focuses on axiomatic description of concepts and roles (T-
box), but also allows description of instances (A-box) DAML + OIL, OWL, Racer, Fact, Protégé, ...
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DL: basic constructs Concept
Human, Animal
Role (oriented binary relationship) cyclic roles may be symmetric, transitive a role may have an inverse
Generalization hierarchies : concepts and roles
Domain of values
Human Animal
hasChild
hasPet STRINGhasName
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Concept constructors intersection, union, complement
Man (Human Male)
Female Human (Male)
existential and universal quantifiers hasChild.Man
{x | (y) (hasChild(x,y) -> Man(y)}
AllSonsFather (Man hasChild.Man )
minimum and maximum cardinality≥ 1hasChild
Father (Man ≥ 1hasChild )
Man
isa
Human Male
hasChild
Female
AllSonsFather
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Author
writes
Visualization
Committee
Paper
Conference
for
forchairs
chairedByParticipant
hasMember
GoodConference
registers
Person
RegisteredAuthor
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DL examples Primitive concepts
Person, Committee, Paper, Conference
Defined concepts Participant Person registers.Conference Author Person writes.Paper RegisteredAuthor Author registers.Conference GoodConference Conference chairedBy.{Iochpe}
Constraints a committee has at least 10 members
Committee ≥ 10 hasMember.Person
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Horn-Logic Decidable subsets of FOL: only one negation
Rules: a formalism for deduction (≠ axioms)
Powerful: support recursive rules
Mostly relational-based (e.g., Datalog)
reviewer(P,C) :– pcMember(P,C)
reviewer(P,C) :– delegates(Px,P), reviewer(Px,C)
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F-Logic Rule language, designed for deduction Object-oriented expressiveness
person [ name => personName, firstNames =>> personFirstname,address@(type) => personAddress,isMemberOf =>> committee,
chairs =>> committee ]
Rule: P:chairman :– P:person[chairs–>>C]
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Assessment DLs
open world assumption automatic consistency checking automatic placement of new concepts good for distributed asynchronous coordination
counter-intuitive poor expressiveness poor readability poor query languages poor scalability
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Assessment (2nd)
Horn-Logic and F-Logic close world assumption (mostly) no need for consistency checking
no automatic placement of new concepts (no subsumption reasoning)
can serve as an implementation platform for DL
(cf. DIP project)
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Practical Achievements Description Logics
OWL: Ontology Web Language (successor to DAML+OIL, RDF, ...) RACER : a reasoning system for OWL (implements the SHIQ Logic) Interface Tools: Protégé, OntoEdit, ..........
Horn-Logics: deductive DBMS
F-Logic: Florid, Ontobroker, Flora
Outsiders KAON: an ontology and semantic web framework, allowing the design
and management of ontologies, but with limited reasoning capabilities DOGMA: an ontology engineering framework based on the ORM (Object-
Role-Modeling) conceptual model, with no reasoning capabilities
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What about geodata? Towards spatial ontologies
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Geo-data = data + geo Classification:
is this water extent a lake or lagoon or pond or … ? Is a PhD student a Student or a Staff member ? Is an invited professor a Faculty or not ?
Fuzzy Boundaries: were does the forest stop ? When does a fœtus start to be a Person ? When does a car turn into a wreck ?
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Geo-data = data + geo (ctd.)
Contextual boundaries: depends on whether you see a lake or a marsh Salary amount depends on whether you see a person as an
Employee or as a Tax-Payer
Is a building a feature or an object ? Are leather seats a feature of your car or an object ?
I have a hard time finding something in Geo-DBthat has no counterpart in non-Geo-DB
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Ontologies of space, ... Ontologies of Space
Generic constructs for the description of the characteristics of space Discrete view: point, line, polygon, pointSet, lineSet, polygonSet... Continuous view: function: spatial extent ––> range of values
(weather measures, altitude, vegetation, soil type, ...) Spatial relationships: inside, intersect, adjacent, disjoint, equal... Open Geospatial Consortium & ISO: GML
Ontologies of Time definitions for: instant, interval, duration, ... ISO Temporal SQL
Ontologies of Space and Time definitions for: moving point, moving and deforming area, ... research community
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Geo-application domains Ontologies of geographical domains
cadastre water management utility networks roads, traffic, and transportation ........
Without spatial extent Taxonomy of terms for water resource management or ... Similar to other ontologies for non-geographic domains
With explicit description of the spatial extent=> Spatio-temporal ontology
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Spatio-temporal Ontologies (alike spatio-temporal DB)
Ontological concepts and instances localized in space and in time Description of where/when the concept/instance is valid
Localizing concepts "soccer" in the USA, is equivalent to "football" in Europe "fat lady" is a kind of Chinese pottery from the Han period "snow": a unique, generic concept in Brazil
a dozen of more specialized concepts in the North Pole area: fresh snow, iced snow, melting snow...
a 100m wide stream: a small river in Brazil, a large river in Europe
Localizing instances Stefano's office is located at EPFL
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Evolution of Ontologies Terms
Terms + properties+ reasoning
Terms+properties+reasoning+space+time
Taxonomic Ontologies
Descriptive Ontologies
ST Descriptive Ontologies
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Logics and Space Theoretical Approach:
Extend DLs with spatial (and temporal) concrete domains
e.g., polygons (Haarlsev)
Pragmatic Approach:Combine DLs with GIS (Wessel)
DL
space
GISDL
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Extending DLs concrete domains of polygons
all objects have an associated polygon
hierarchy of topological relationship
define concept restrictions: SwissLake Lake hasArea.g_inside(Switzerland)
define topological roles: isSInside (hasArea)(hasArea).strictly_inside
topological reasoning (but no values) strictly_inside => g_inside g_inside (A,B) g_inside (B,C) => g_inside (A,C)
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Topology Hierarchy Binary predicates for qualitative spatial reasoning
(RCC theory)spatially_related
t_inside s_inside
s_overlapping
equals_containst_contains
g_inside g_overlappingg_contains
disjointconnected
touching
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© Volker HaarslevSatisfiability with spatial constraints Paradise cottage
fishingCottage Cottage isTouching.River mosquitoFreeForest Forest isConnected. River paradiseCottage fishingCottage isGInside.Forest
isGInside.mosquitoFreeForest
.... a nice dream
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Combining DLs and GIS Goal: scalability
Extensional component holds instances instances are split into thematic part (RACER) and geometry
part (ad-hoc GIS)
Intensional component DL reasoning
Query component hybrid language
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Our proposal Modeling-oriented (rather than reasoning-oriented) approach to
ontologies
Use Conceptual Modeling for expressiveness and understandability MADS spatio-temporal conceptual data model Enhance the capabilities of conceptual models to support reasoning
symmetric and transitive cyclic relationships derived objects membership predicates ......
Use Logic-based approaches for reasoning
Hybrid System DBMS: scalability DL-reasoners: inferencing
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MADS MADS : a spatio-temporal conceptual model (complex objects,
n-ary relationships with attributes, generalization hierarchies, multi-instantiation, spatio/temporal and contextual features) spatial objects: geometry attribute spatial attributes spatial data types: Point, Line, OrientedLine, Area, PointSet, LineSet,
OrientedLineSet, AreaSet, Geo spatial relationships space-varying attributes (functions)
The MADS framework includes a visual schema editor and a visual query editor (MurMur EEC/IST Project)
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Waterside Water Body
River Lake
UndergroundSect LandSect. UnderLakeSect.
from to 0:n0:10:n
1:1
0:n1:n
1:1
Flow
Under RL
Village Town
Built Up Area
District
County 1:n
1:1
0:n
0:n
1:1
In
Of
RiverSection
A MADS Spatial Schema
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Lake
namedepth f( )harbors (0:n) name capacity location master name phone addressbeaches (0:n) name location length
Object type example
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Rationale for hybrid DB+DL systems Descriptive ontologies require
Rich models to enable building representations as close as possible of human perception
Support for the precise definition of concepts in relation to other concepts
Storage and transactions management mechanisms (security, concurrency, reliability) to realistically manage large sets of instances
Both open world and closed world reasoning Query languages for schema exploration, reasoning on the
schema, and querying of instances
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Problem: Dissimilarities Inheritance links
DB currently do not provide a way to express what is the specialization criterion that defines a sub-class: no possibility of automatically positioning an instance in a sub-class
Multi-instantiation Default rules are different: DL: by default any two concepts may share instances MADS: by default two types do not share instances
Spatio-temporal information DL: very limited support MADS: good support: discrete and continuous views, ST relationships
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Defined Concepts Goal: support the precise definition of concepts in
relation to other concepts
DL: "defined concepts" by a logic formula defined concepts are managed in the same way as primitive
concepts users may insert instances in defined concepts a: StudentParticipant => a.position= "student"
DB: views and derived objects are the only way to define new subsets of instances
Views are not part of the schema Views are not instantiable by users MADS: derived spatial relationships and derived attributes
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Instances
Instance management principles Open / closed world assumption Oid / names (DL: no unique name assumption)
Instance management efficiency DBMS have been designed to provide storage and transactions
management mechanisms (security, concurrency, reliability) DL does not have such facilities
Constraints: DL formulae can express constraints at both levels the schema (Tbox) level the instances level
john has at most 2 children But formulae are used for inference and not for constraining data (OWA)
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Conclusion Generic spatial data exchange can become a reality as part of
the Semantic Web
Web Services require access to ontologies to become Semantic Web Services
Needed Spatial Ontologies include: Ontologies of space and time Ontologies of geographical domains Spatio-Temporal Ontologies
Ontologies need enhanced conceptual models to fulfill spatial ontology requirements
It is a long way to go
The DB community has a major role to play
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More R&D themes Ontology alignment, fusion, merge, integration, ...
Ontology engineering
Ontology modularization
Contextual ontologies
Ontology use: query languages & APIs
Ontology "catalogues" (ontologies of ontologies)
Reverse engineering: from legacy data to ontologies
....
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Summary
According to Ralph Güting's rule: a serious researcher is a researcher whose talks always include formulae
From a DB perspective: Stefano is no serious researcher
From an ontology perspective: there is still hope that Stefano qualifies as a serious researcher
Which one is preferable depends on what you are looking for
