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Analyzing Theme, Space and Time: An Ontology-based
ApproachMatthew Perry, Farshad Hakimpour, Amit Sheth
14th International Symposium on Advances in Geographic Information Systems, Arlington, VA, Nov. 10 – 11, 2006
Acknowledgement: NSF-ITR-IDM Award #0325464 ‘SemDIS: Discovering Complex Relationships in the Semantic Web’
Outline
• Background• Motivation• Goals and Assumptions• Modeling Approach• Query Operators• Conclusions
• What is an ontology?– Agreed-upon formalization (or conceptual model) of
concepts and relationships in the real world
• Types of ontologies?– General-purpose vs. Domain ontologies
• Parts of an ontology– Classes – types or logical groups of objects– Relationships – how objects relate to each other– Attributes –features and characteristics of objects– Instances – members of Classes who have Attributes and
participate in Relationships
Background (ontologies and RDF/S)
Schema e.g. Student attends University
Data e.g. ‘Matt’ attends ‘University of Georgia’
Representing ontologies and instance data• W3C standards
– Resource Description Framework (RDF)• Language for representing information about
resources• Resources are identified by Uniform Resource
Identifiers (URIs) – globally-unique• Common framework for expressing information
allows exchange and reuse without loss of meaning
rdfs:Class
lsdis:Politician
lsdis:Speech
rdf:Propertyrdfs:Literal
lsdis:gives
lsdis:name
rdfs:domain
rdfs:domainrdfs:range
rdfs:range
lsdis:Politician_123
lsdis:Speech_456
“Franklin Roosevelt”
lsdis:gives
name
lsdis:Person
rdf:typerdfs:subClassOfstatement
Defining Properties (domain and range):<lsdis:gives> <rdfs:domain> <lsdis:Politician> .<lsdis:gives> <rdfs:range> <lsdis:Politician> . Subject Predicate Object
Statement (triple):<lsdis:Politician_123> <lsdis:gives> <lsdis:Speech_456> . Subject Predicate Object
Defining Class/Property Hierarchies:<lsdis:Politician> <rdfs:subClassOf> <lsdis:Person> . Subject Predicate Object
Labeled Graph
Statement (triple):<lsdis:Politician_123> <lsdis:name> “Franklin Roosevelt” . Subject Predicate ObjectDefining Classes:
<lsdis:Person> <rdf:type> <rdfs:Class> . Subject Predicate Object
Defining Properties:<lsdis:gives> <rdf:type> <rdf:Property> . Subject Predicate Object
From …..
Finding things
To …..
Finding out about things
Relationships!
Semantic Discovery*
* http://lsdis.cs.uga.edu/projects/semdis
From thematic analytics to spatio-temporal, thematic (STT) analytics (Ex: Bioterrorism)
E5:Terrorist
E6:Attack
E4:Chemical
E10:Doctor
E9:Base
E8:Soldier
E7:Platoon
E1:Soldier
E3:Disease
E2:Symptom
E14:Battle
E12:Platoon
E13:Soldier
E11:Location
assigned_to
participated_in
member_of
carried_out
spotted_at
stationed_at
member_of
sign_ofparticipated_in
causes
member_of
used_in
exhibits
Near in Space
After the Battle [4, 6]
[8, 10]
[0, 10]
[3, 5]
[0, 2]
[0, 2]
Spotted Before and Close in Time
Goals and Contributions of this work• Define a Domain-independent Ontology which
integrates Spatial and Thematic Knowledge– Allows exploiting the flexibility and extensibility of
Semantic Web data models– Can deal with incompleteness of information on the web
• Incorporate temporal metadata into this model• Identify and formalize basic spatial and temporal
relationship-based query operators which complement current thematic operators of SemDis
Assumptions / Design Decisions• Interested in Relationship Analysis• Do not address issues of scale and
granularity at this time• Exact serialization/representation of
spatial geometry is not a primary concern– RDF XML Literal Type (GML)– Ontology mirroring GML/OGC specification
Upper-level Ontology modeling Theme and Space
OccurrentContinuant
Named_PlaceSpatial_OccurrentDynamic_Entity
Spatial_Region
Occurrent: Events – happen and then don’t existContinuant: Concrete and Abstract Entities – persist over timeNamed_Place: Those entities with static spatial behavior (e.g. building)Dynamic_Entity: Those entities with dynamic spatial behavior (e.g. person)Spatial_Occurrent: Events with concrete spatial locations (e.g. a speech)Spatial_Region: Records exact spatial location (geometry objects,
coordinate system info)
occurred_at located_at
occurred_at: Links Spatial_Occurents to their geographic locationslocated_at: Links Named_Places to their geographic locations
rdfs:subClassOfpropertyFinal Classification of Domain
Classes depends upon the intendedapplication
OccurrentContinuant
Named_Place
Spatial_OccurrentDynamic_Entity
PersonCity
Politician
Soldier
Military_Unit
BattleVehicle
Bombing
Speech
Military_Eventassigned_to
on_crew_of
used_in
gives
participates_in
trains_at
Spatial_Region
located_at occurred_at
Upper-level Ontology
Domain Ontology
rdfs:subClassOf used for integrationrdfs:subClassOfrelationship type
Incorporation of Temporal Information• Use Temporal RDF Graphs defined by
Gutiérrez, et al1
• Focuses on absolute time and considers time as a discrete, linearly-ordered domain
• Associate time intervals with statements which represent the valid-time of the statement– Essentially a quad instead of a triple
1. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman: Temporal RDF. ESWC 2005: 93-107
Example Temporal Graph: Platoon Membership
E1:Soldier
E3:Platoon E5:Soldier
E2:Platoon
E4:Soldier
assigned_to [1, 10]
assigned_to [11, 20]
assigned_to [5, 15]
assigned_to [5, 15]
Querying in the STT dimensions• Path Query in the thematic dimension
– Thematic Context
• Associate spatial region with a path• Associate temporal interval with a path• Query operators based on properties and
relationships between associated spatial regions and temporal intervals
Thematic Context• Specifies a type of connection between resources in the thematic
dimension of our ontology
‘John Smith’
‘B24#123’‘Bombing#456
’
on_crew_of used_in
PersonMilitary_Vehicl
eBombing
on_crew_of used_in
Schema
Person.on_crew_of.Military_Vehicle.used_in.BombingPath Template
‘John Smith’.on_crew_of.Military_Vehicle.used_in.Bombing
ei INSTANCES(G) and ei = Ci, ei rdf:type Ci, or ei rdf:type Ci’ and Ci’ rdfs:subClassOf Ci
pi PROPERTIES(G) and pi = Pi or pi rdfs:subPropertyOf Pi
Ci CLASSES(G) INSTANCES(G)Pi PROPERTIES(G)
tc = C1.P1.C2.P2.C3…Cn-1.Pn-1.Cn
pt = e1.p1.e2.p2.e3…en-1.pn-1.en
Thematic Context
Thematic Context Instance
Example: ‘John Smith’.on_crew_of.Military_Vehicle.used_in.Bombing
Example: ‘John Smith’.on_crew_of.‘B-24#123’.used_in.‘Bombing#456’
Thematic Context
Thematic Query (ρ-theme)ρ-theme (G, tc) {pt}
Example: find all Bombing events connected to ‘John Smith’ through a vehicle participation context
ρ-theme (G, ‘John Smith’.on_crew_of.Military_Vehicle. used_in.Bombing)
‘John Smith’.on_crew_of.‘B-24#123’.used_in.‘Bombing#456’‘John Smith’.on_crew_of.‘B-24#123’.used_in.‘Bombing#789’
Result
G = temporal RDF Graph, tc = thematic context, pt = thematic context instance
E1:Soldier
E2:Soldier
E3:Soldier
E5:Battle
E4:Address
E6:Address
E7:Battle
occurred_at
occurred_at
located_at located_at
lives_at
lives_at
assigned_to
E8:Military_UnitE8:Military_Unit
assigned_to
participates_in
participates_in
Georeferenced Coordinate Space
(Spatial Regions)
Dynamic EntitiesSpatial OccurrentsNamed Places
Thematic Contexts Linking Non-Spatial Entities to Spatial Entities
ResidencyBattle Participation
E1:Soldier
Deriving Spatial and Temporal Properties of Entities
ρ-spatial_extent (G, {pt}) {(pt, sr)}
Example: Where were the battles in which the ‘101st Airborne Division’ fought?
ρ-spatial_extent (G, ρ-theme (G, ‘101st Airborne Division’. .participates_in.Battle))
‘101st Airborne Division’.participates_in. ‘Operation Market Garden’, ‘Geom#123’
Result
G = temporal RDF Graph, pt = thematic context instance, sr = spatial region
Temporal Properties of Context Instances
Platoon#456 Soldier#789Soldier#123
assigned_to:[3, 12] assigned_to:[6, 20]
Temporal Intersection [6, 12]
Temporal Range [3, 20]
Deriving Spatial and Temporal Properties of Entities
ρ-temporal_intersect ({pt}) {(pt, [t1, t2])}
Example: Which Soldiers were members of the ‘1st Armored Division’ at the same time?
ρ-temporal_intersect (ρ-theme (G, Soldier.assigned_to.‘1st Armored Division’.assigned_to.Soldier))
‘Fred Smith’.assigned_to.’1st Armored Division’.assigned_to. ‘Bill Jones’, [1941:04:15, 1943:02:25]
Result
pt = thematic context instance, [t1, t2] = temporal interval
Querying Relationships Between Entities
(Thematic Context Instance tp, Temporal Interval [ti, tj], Spatial Region sr)
3 Spatial Relationship Operatorsρ-spatial_locateρ-spatial_evalρ-spatial_find
3 Temporal Relationship Operatorsρ-temporal_restrict
ρ-temporal_evalρ-temporal_find
Identify 6 major Spatiotemporal Relationship Querieswhich can be answered by combining previously defined operators
Spatial Relationships Between Entities• Examples:
– Which Military Units have spatial extents which are within 20 miles of (48.45° N, 44.30° E) in the context of Battle participation?
– Which infantry unit’s operational area overlaps the operational area of the 3rd Armored Division?
Quantitative Relationships Qualitative Relationships
Metric Spatial Function msf : S X S [distance]
Spatial Relationship OperatorsSpatial Predicate sp : S X S B
Qualitative Spatial Function qsf : S X S B [overlaps, meets, inside, …]
Metric Spatial Expression <mse> ::= <msf> <comp>
<comp> ::=
Spatial Predicate <sp> ::= <mse> | <qsf> | <sp> <sp> | <sp> <sp>
Spatial Relationship Operators
ρ-spatial_locate ({(pt, sr)}, sr’, sp) {pt}
ρ-spatial_eval ({(pt, sr)}, {(pt’, sr’)}, sp) {(pt, pt’)}
ρ-spatial_find ({(pt, sr)}, {(pt’, sr’)}) {(pt, pt’, qualitative spatial relationship)}
pt = thematic context instance, sr = spatial region, sp = spatial predicate
Example Spatial Relationship Query
Example: Which Military Units’ operational area overlaps the operational area of the ‘3rd Armored Division’?
S1 ρ-spatial_extent (G, ρ-theme (G, ‘3rd Armored Division’. participates_in.Military_Event))
S2 ρ-spatial_extent (G, ρ-theme (G, Military_Unit. participates_in.Military_Event))
ANS ρ-spatial_eval (S1, S2, overlaps(S1, S2))
Temporal Relationships Between Entities• Examples:
– Which Speeches by President Roosevelt were given within one day of a major battle?
– Who were members of the 101st Airborne during November 1944?
Quantitative Relationships Qualitative Relationships
Temporal Relationship Operators
Temporal Predicate tp : I X I B
Temporal Predicate <tp> ::= <mte> | <qtf> | <tp> <tp> | <tp> <tp>
Metric Temporal Expression <mte> ::= <mtf> <comp>
<comp> ::=
Metric Temporal Function mtf : I X I [elapsed time]
Qualitative Temporal Function qtf : I X I B [before, meets, during, …]
Temporal Relationship Operatorsρ-temporal_restrict ({(pt, [ti, tj])}, [tk, tl], tp) {pt}
ρ-temporal_eval ({(pt, [ti, tj])}, {(pt’, [tk, tl])}, tp) {(pt, pt’)}
ρ-temporal_find ({(pt, [ti, tj])}, {(pt’, [tk, tl])}) {union of convex intervals relationship2}
pt = thematic context instance, [tk, tl] = temporal interval, tp = temporal predicate
2. Ladkin, P.B. The logic of time representation, Ph.D. Dissertation, University of California at Berkley, Berkley, CA, 1987
I1 ρ-temporal_intersect (ρ-theme (G, ‘Franklin Roosevelt’. gives.Speech))
I2 ρ-temporal_intersect (ρ-theme (G, Military_Unit. participates_in.Battle))
ANS ρ-temporal_eval (I1, I2, elapsed time (I1, I2) 1 day)
Example Temporal Relationship Query
Example: Which Speeches by President Roosevelt were given within one day of a major Battle?
S1 ρ-spatial_extent (G, ρ-theme (G, ‘101st Airborne Division’. participates_in.Battle))
S2 ρ-spatial_extent (G, ρ-theme (G, ‘1st Armored Division’. participates_in.Battle))
ANS ρ-temporal_intersect (ρ-spatial_eval (S1, S2, distance (S1, S2) 10 miles)
Spatiotemporal Relationship QueriesExample: When did the 101st Airborne Division come within 10 miles of the 1st Armored Division in the context of Battle participation
Conclusions• Defined Basic Domain-independent ontology
integrating Theme and Space and showed how to incorporate temporal information with temporal RDF graphs
• Novelty centers on utilization of named relationships in the thematic dimension
• Link non-spatial entities with spatial entities at the thematic level in order to analyze their spatial properties
• Allows us to take advantage of the flexibility and extensibility of RDF and helps cope with incompleteness of information on the Web