1. A Framework to Support Spatial, Temporal and Thematic Analytics over Semantic Web Data
Matthew Perry
Ph.D. Dissertation Defense
Kno.e.sis Center, Wright State University
Introduce committee and thank everyone
Give dissertation title
Quick overview: Interesting work in SW area with new querying paradigms based on graph structure of SW data models … focus on relationships. We look at how to incorporate spatial and temporal data with this querying paradigm. Dissertation shows how to exploit this graph-centric data model for a more flexible modeling approach for STT data and for corresponding new query operators.
First clarify some of our terminology … what we mean by (1) spatial, temporal and thematic dimensions and (2) semantic analytics
Committee: Dr. Amit Sheth (advisor), Dr. T.K. Prasad, Dr. Soon M. Chung,
Dr. Christopher Barton (EES WSU), Dr. Kate Beard (SISE U. of Maine)

2. Three Dimensions of Information
Thematic Dimension: What
Spatial Dimension: Where
Fred Smith moved into the house at 244 Elm Street on November 16, 2007
Temporal Dimension: When
First of all, we will clarify what we mean by spatial, temporal and thematic dimensions of information (three dimensions of information).
Look at the following statement … This is describing an event or set of relationships.
Thematic Dimension – What (thematic entity Fred Smith, thematic relation moving, thematic entity house)
Who/What is thematically related (company he works for, who owns the house, etc.)
Spatial Dimension – Where (location of the house/event -- point, line, polygon)
What is spatially related (houses nearby, nearest highway, etc.)
Temporal Dimension – When did this happen (time point or interval)
What is temporally related – What happed at the same time, just before, etc.

3. Background

4. Ontology What is an ontology? Types of ontologies?
Agreed-upon formalization 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
Schema
e.g. Student attends University
First of all clarify what we mean by ontology.
Agreed-upon formalization of concepts and relationships in the real-world. Idea is that through this agreement applications can have a shared interpretation of data (machine understandable)  Semantic Web
Many types of ontologies – general purpose (TAP, SUMO etc.) domain specific – biology (GO, NCI Cancer ontology) geography – Ordinance Survey (Hydrology)
We can categorize the parts of an ontology Classes (Person, Company), Relationships (works-for connecting Person to Company), Attributes (Name of Person, literal values – strings, numbers)
These three things can be thought of as a Schema in DB terms
Also there are instances – Facts
Data
e.g. ‘Matt’ attends ‘Wright State University’

5. Representing ontologies and instance data
W W W Consortium (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
Graph-based data model
Relationships are first class objects
Along with this vision of Semantic Web and Ontologies, we need a standard way to represent ontologies so they are machine processable
W3C has defined RDF as a standard language for representing semantic data
Major concepts
URI – unique identifiers for resources
Graph-based
Rels are first class object … contrast again with relational model and implicit relationships

6. Subject Predicate Object
rdfs:Class
Directed Labeled Graph
knoesis:Person
rdf:Property
rdfs:Literal
Statement (triple):
<knoesis:Politician_123> <knoesis:gives> <knoesis:Speech_456> .
Subject Predicate Object
rdfs:range
knoesis:Speech
rdfs:domain
knoesis:Politician
knoesis:name
Statement (triple):
<knoesis:Politician_123> <knoesis:name> “Hillary Clinton” .
Subject Predicate Object
rdfs:range
knoesis:gives
rdfs:domain
Defining Properties:
<knoesis:gives> <rdf:type> <rdf:Property> .
Subject Predicate Object
Defining Properties (domain and range):
<knoesis:gives> <rdfs:domain> <knoesis:Politician> .
<knoesis:gives> <rdfs:range> <knoesis:Politician> .
Subject Predicate Object
Illustration of Major concepts of RDF:
Slide shows sample RDF data … schema on top … instance data on the bottom
Defining Classes:
<knoesis:Person> <rdf:type> <rdfs:Class> .
Subject Predicate Object
Defining Class/Property Hierarchies:
<knoesis:Politician> <rdfs:subClassOf> <knoesis:Person> .
Subject Predicate Object
knoesis:gives
knoesis:Politician_123
knoesis:Speech_456
name
“Hillary Clinton”
rdf:type
rdfs:subClassOf
statement

7. RDF(S) Inferencing Rule:
rdfs:Class
RDF(S) Inferencing
knoesis:Person
rdf:Property
rdfs:Literal
Rule:
(x, rdf:type, y) and (y, rdfs:subClassOf, z) 
(x, rdf:type, z)
rdfs:range
knoesis:Speech
rdfs:domain
knoesis:Politician
Asserted:
(knoesis:Politician_123, rdf:type, knoesis:Politician)
(knoesis:Politician, rdfs:subClassOf, knoesis:Person)
knoesis:name
rdfs:range
knoesis:gives
rdfs:domain
Illustration of Major concepts of RDF
Also inferencing rules … standard interpretation of rdf/rdfs relationships.
Means if you see statements of a given pattern than you can infer the existence of other statements … so add them
Example rdf:type / rdfs:subClassOf
Infer:
(knoesis:Politician_123, rdf:type, knoesis:Person)
knoesis:gives
knoesis:Politician_123
knoesis:Speech_456
name
“Hillary Clinton”
rdf:type
rdfs:subClassOf
statement

8. Implementation Scheme Experimental Evaluation Query Language Support
Outline
Background
Motivation
Related Work
STT Modeling Approach
STT Query Operators
Implementation Scheme
Experimental Evaluation
Query Language Support
Remainder of the Presentation is organized as follows 8

9. Aggregated RDF Instance Base
SemDis Project
author_of
Semantic Analytics: Searching, browsing and analyzing semantically meaningful connections among named entities where an ontology provides the context or domain semantics
E2:Paper
E5:Person
author_of
author_of
author_of
author_of
E1:Reviewer
E3:Paper
E7:Submission
friend_of
author_of
E4:Person
E6:Person
friend_of
What do we need?
Data model that represents relationships explicitly as first class objects
Ability to model semantics of the relationships
Tools for efficient storage and querying of these relationships
How is entity1 (Reviewer) related to entity2 (Submission) ?
Now to give some context to this work, mention the project it’s a part of.
Semantic Discovery – discovering complex relationships on the SW
New Paradigm for searching – moving from simple, traditional retrieval of items or entities to finding out how entities are related
Important aspect here is focus on relationships – what we see as the major benefit of SW data models – named relationships as first class objects e.g. contrasted with relational model where relationships are implicit in key constraints
This slide gives the big picture of the SemDis architecture.
Large RDF (SW data) instance base, extracted/scraped from multiple heterogeneous sources and corresponding to schema information
Idea is that the user uses our tools to analyze connections/relationships between entities in this instance base.
For example – conflict of interest – reviewer and author – aggregated bibliography data and social network data
Refer to this process as Semantic Analytics – give definition
Aggregated RDF Instance Base
Ontology Schemas
XML
TEXT
RDBMS
HTML

10. An Example: Battlefield Intelligence
has_symptom
Chemical_X
?Symptom
induces
?Person
participated_in
?Location_1
?Military_Event
located_at
How are these events
related in time?
How close are these
locations in space?
?Enemy
spotted_at
?Location_2
member_of
Enemy_Group_Y
Consider this motivating example.
Get the picture – battle field intelligence monitoring soldiers/activities for possible indications of chemical/biological weapons exposure
We will illustrate how the st aspects will help in understanding the relationships.
May pose the following query: -- read it – explain importance of spatial aspects
Will show such a query expressed with our system – please ignore details of syntax for now
Interesting part about this query is as follows:
1 – thematic relation
2 – thematic relation
3 – implicit spatial relation
SELECT ?p
FROM TABLE(spatial_eval(‘(?p has_symptom ?s)(Chemical_X induces ?s)
(?p participated_in ?m)(?m located_at ?l1)’, ‘?l1’,
‘(?e member_of Enemy_Group_y)’); )(?e spotted_at ?l2)’, ‘?l2’,
‘geo_distance(distance=2 unit=mile)’);

11. Application Areas: Semantics + Space and Time
Semantic Sensor Web
Web-accessible sensor networks and archived sensor data that can be discovered and accessed using standard application protocols and application program interfaces.1
Event Web
Event Web organizes data in terms of events and experiences and allows access from users perspectives. For each event, Event Web collects and organizes audio, visual, textual, and other data to provide people an environment for experiencing the event from their perspective. … Unlike events, hypertext has no notion of time, space or semantic structures other than often ad-hoc hyperlinks.2
Would like to also mention some potential application areas for this work.
In both of these applications we have the need to manage large amounts of SW data with heavy spatial and temporal components.
Semantic Sensor Web
Event Web
Botts, M., Percivall, G., Reed, C., and Davidson, J. (2007). OGC Sensor Web Enablement: Overview and High Level Architecture (OGC ). Technical Report, Open Geospatial Consortium.
Jain, R. (2008). EventWeb: Developing a Human-centered Computing System. IEEE Computer, 41(2):42–50.

12. Shortcomings of State of the Art
Objective
Goal
Enable Semantic Analytics over thematic, spatial and temporal dimensions
Shortcomings of State of the Art
Current GIS technology does not support complex thematic analytics operations
Current Semantic Analytics technology does not support spatial and temporal relationship analysis
Shortcomings with current state of the art make this challenging
1 – traditional models do not represent thematic entities/rels as first class objects – hard to analyze rels
2 – As discussed earlier work in analyzing relationships has focused on thematic rels – ST not supported – note implicit nature of these rels
3 – additional benefit – no support in general even in simpler queries – benefits to data integration, etc.

13. Contributions
An ontology-based spatiotemporal modeling approach using temporal RDF
A formalization of a set of spatial, temporal and thematic query operators for the proposed modeling approach
A SQL-based implementation of the proposed query operators (storage, indexing, inferencing, query processing)
An extension of the SPARQL RDF query language: SPARQL-ST
A detailed performance study using large synthetic and real-world RDF datasets

14. Broad Differences from Related Work
ST Modeling
Represent thematic entities as first class objects rather than directly attached attributes of spatial objects
Provide many-to-many mapping between thematic and spatial objects
ST Querying
Utilize thematic relationships to connect entities to spatial regions in a variety of ways (contexts)
Analyze ST properties of a given entity w.r.t. different contexts
Dynamic binding of objects to ST properties
ST Data on Semantic Web
Focus on relationship-centric nature of RDF data for analytical queries
Implicit relationships (e.g., distance)
Look at query language aspects and performance issues
Only system supporting both spatial and temporal
Before we get into details, I will briefly give broad differences with related work. This is detailed in the written dissertation.
ST Modeling … note many-to-many is source of increased flexibility
ST SW … note qlang done in context of SPARQL

15. Related Work (Ontologies and GIS)
A vehicle to facilitate Interoperability
Fonseca et al. (2002), Agarwal (2005)
Geospatial Ontologies
Fundamental ontology of space vs. Geography domain ontology
My Work
Upper-level ontology serves as fundamental ontology of space
Allows for integration of domain ontologies
Goes beyond modeling and allows analysis

16. Related Work (Spatiotemporal Models)
Three Domain Model - Yuan (1994, 1996)
Models semantics, space, time separately
Relies on direct connections from thematic entities to spatial entities
Object-oriented and Event-based Models -Worboys and Hornsby (2004)
Models the concept of a setting and a situate function
General Modeling appraoches
Spatiotemporal ER model – Tryfona and Jensen (1999, 2000)
Spatiotemporal UML – Price et al. (2002)

17. Related Work (Storing and Querying RDF)
RDBMS – based
Schema-aware – Sesame w/ PostgreSQL, Vertical Partitioning (Abadi, et al. 2007)
Schema-oblivious – Jena, Oracle RDF
Hybrid – RDF Suite
Native – Use lower-level structures
Redland – Hash Tables
Yars – B+ Trees
My approach
Use Schema-oblivious with additional structures for ST data
Must handle temporal aspects with RDFS inferencing

18. Related Work (ST Data and SW)
Only system to support Temporal RDF and spatial objects
Temporal RDF
Formally defined – Gutierrez, et al. (2005, 2007)
tGRIN – Pugliese et al. (2008)
Simpler queries and inferencing
Qualitative spatial and temporal reasoning
OWL-Time – Hobbs and Pan (2004)
Specialized Spatial Reasoners – Smart et al. (2007)

19. STT Modeling Approach Give basic overview of approach:
-Upper-level ontology to model spatial objects and their connections to non-spatial objects
-Temporal RDF graphs – give temporal dimension to the relationships

20. Upper-level Ontology modeling Theme and Space
Continuant
Occurrent
Dynamic_Entity
Spatial_Occurrent
Non-Spatial_Occurrent
Named_Place
occurred_at
located_at
Spatial_Region
The first component of our modeling approach is an upper-level ontology outlining the basic classes and relationships for the thematic and spatial domains. The idea is to give a minimal model that we can use to build our query operators. This was done by surveying various related work in st ontologies to form this basic model.
Enough for query operators but general enough to incorporate nearly any domain model
Gml-based
Ontology
rdfs:subClassOf
property
Continuant: Concrete and Abstract Entities – persist over time
Occurrent: Events – happen and then don’t exist
Named_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)
Non-Spatial_Occurrent: Event w/out concrete location (e.g., law becomes active)
occurred_at: Links Spatial_Occurents to their geographic locations
located_at: Links Named_Places to their geographic locations
Spatial_Region: Records exact spatial location (geometry objects,
coordinate system info)

21. Example Domain Ontology
To clarify these concepts consider the example Domain ontology that has been integrated with the upper-level ontology described previously.
We can see the subclasses of Dynamic Entity / Named Place, Spatial Occurrent
Also note the start and end times on edges
This military scenario will serve as a running example for the presentation

22. Temporal RDF: Incorporating Temporal Information
Student
rdfs:subClassOf
rdfs:subClassOf
Graduate
Undergraduate
rdf:type
[?, ?]
rdf:type : [2004, 2008]
rdf:type : [2002, 2004]
Student1
Associate temporal label with a statement that represents the valid time of the statement
(Student1, rdf:type, Graduate) : [2004, 2008]
Example temporal RDF graph
Student1 was and undergraduate student and the enrolled in graduate program
Note need for temporal aspects in rdf inferencing … we need to account for this in our system
Temporal Inferencing
Interval Union: (Student1, rdf:type, Student) : [2002, 2008]
1. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”. ESWC 2005:

23. Temporal RDF Serialization
Instant
Statement
CalendarClockInterval
inCalendarClockDataType C
XMLSchema Datetime
object
begins
predicate B
Instant
temporal
ends
subject
XMLSchema Datetime A
inCalendarClockDataType
Temporal triples can be modeled/serialized with standard RDF syntax … utilizes RDF reification and existing OWL time ontology.
RDF Reification
OWLTime
NEW

24. STT Query Operators

25. Querying in the STT dimensions
Define a notion of context based on a graph pattern
Query about entities w.r.t. a given context
Associate spatial region with an entity w.r.t. a context
Associate temporal interval with an entity w.r.t. a context
How are entities related in space and time w.r.t. a given context
First bullet is the goal
Note that we will clarify notion of context with the next slide.

26. Georeferenced Coordinate
Contexts Linking Non-Spatial Entities to Spatial Entities
E2:Soldier
E4:Address
lives_at
located_at
located_at
lives_at
E6:Address
E1:Soldier
Georeferenced Coordinate
Space
(Spatial Regions)
E1:Soldier
occurred_at
E7:Battle
assigned_to
participates_in
E8:Military_Unit
This slide illustrates a key contribution of this approach.
Consider using thematic contexts to connect non-spatial entities (e.g., Dynamic Entities) to Spatial Entities
Describe the picture
Look at Solder E1
One context could be residency another could be battle participation
Traditionally we have a direct 1-to-1 mapping between entities and spatial properties, but here we are using indirect connections to achieve a many-to-many mapping, can analyze st properties of a given entity w.r.t. multiple contexts
Ex – who/what is near in this context what is relationship between spatial properties of same entity and different contexts e.g. employment vs. residency
E8:Military_Unit
participates_in
assigned_to
E5:Battle
occurred_at
Residency
Battle Participation
E3:Soldier
Named Places
Spatial Occurrents
Dynamic Entities

27. Context Definition Graph Pattern: recursive definition
Basis: a tuple from (UL U VN) X (U U VN) X (UL U VN) is a graph pattern (triple pattern)
Recursion: if P1 and P2 are graph patterns, then (P1 AND P2) is a graph pattern
Formal definition of context.
Notion of a graph pattern – a graph pattern is essentially a collection of triples where the subjects, predicates and/or objects have been replaced with variables. Result of a graph pattern query is a mapping between variables and URIs such that if you substitute the mapped URIs for the variables you get triples actually present in the RDF graph.
Semantics1 of a graph pattern are defined in terms of a function [[.]], which takes a graph
pattern and returns a set of mappings where a mapping μ : VN  RT is a function from
VN to RT
1. Perez, J., Arenas, M., Gutierrez, C.: Semantics and Complexity of SPARQL. ISWC 2006

28. Context Definition
A spatial context is a 2-tuple (GP, v)
where:
1) GP is a graph pattern
2) v var(GP) is a variable in GP identifying a
Spatial_Region instance
Example:
(‘(?x assigned_to ?y) (?y participates_in ?z)
(?z occurred_at ?s)’, ‘?s’)

29. Spatial Operators
spatial_extent ((GP, v))G  {(μ, s)}
Given:
a spatial context (GP, v), a temporal RDF graph G
Find:
{(μ, s) | μ [[GP]]TRIPLES(G) and s = geom(μ(v))}
Example: What are the properties of the 101st Airborne Division w.r.t. battle participation?
ANS  spatial_extent(‘(<101st Airborne Div> participates_in ?x)
(?x occurred_at ?s)’, ‘?s’)G

30. Spatial Operators
spatial_restrict ((GP, v), sf($s))G  {(μ, s)}
Given:
a spatial context (GP, v), a spatial formula sf defined over
S and a variable $s, a temporal RDF graph G
Find:
{(μ, s) | μ [[GP]]TRIPLES(G) and s = geom(μ(v)) and
sf evaluates to true for $s = s}
Example: Which military units have spatial extents that are within 20 miles of (48.45 N, E)?
ANS  spatial_restrict(‘(?x participates_in ?y), (?y occurred_at ?s)’,
‘?s’, distance($s, point(48.45 N, E)) < 20 miles)G

31. Spatial Operators
spatial_eval ((GP1, v1), (GP2, v2), sf($s1, $s2))G  {(μ1, s1, μ2, s2)}
Given:
a spatial context (GP1, v1), a spatial context (GP2, v2), a spatial
formula sf defined over S and variables $s1, $s2, a temporal RDF
graph G
Find:
{(μ1, s1, μ2, s2) | μ [[GP1]]TRIPLES(G) and μ [[GP2]]TRIPLES(G)
and s1 = geom(μ1(v2)) and s2 = geom(μ2(v2)) and sf evaluates to
true for $s1 = s1 and $s2 = s2}
Example: Which military unit’s operational area overlaps the operational area of the 3rd Armored Division?
ANS  spatial_restrict(‘(?x1 participates_in ?y1), (?y1 occurred_at ?s1)’, ‘?s1’,
‘(<3rd Armored Div> participates in ?y2) (?y2 occurred_at s2)’, ‘?s2’,
overlap-bdy-intersect($s1, $s2))G

32. Temporal Operators Initial Definitions:
For each statement e = (s, p, o) TRIPLES(G),
let temporal(e) = {t | (s, p, o) : [t] G}
For a set of time points T’ T,
let contig_intervals(T’) = {[ti, tj] | for all t T : (if ti t and t tj then t T’)
and ti-1 T’ and tj+1 T’}
Example
Suppose:
T = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
T’ = {2, 3, 4, 7, 8}
Then:
contig_intervals (T’) = {[2, 4], [7,8]}

33. Temporal Operators
Given a set of temporal triples E = {e1, e2, …, en},
we define the interval extension of E, int_extension(E) as the set:
contig_intervals(temporal(e1)) X
contig_intervals(temporal(e2)) X …
contig_intervals(temporal(en))
Example
Suppose:
E = {e1, e2, e3},
contig_intervals(temporal(e1)) = {[2, 4], [7, 8]},
contig_intervals(temporal(e2)) = {[1, 5], [7, 9]},
contig_intervals(temporal(e3)) = {[4, 5]}
Then:
int_extension(E) = {{[2, 4], [1, 5], [4, 5]}, {[2, 4], [7, 9], [4, 5]},
{[7, 8], [1, 5], [4, 5]}, {[7, 8], [7, 9], [4, 5]}}

34. Temporal Operators
Given a set of time intervals I = {[s1, t1], [s2, t2], …, [sn, tn]}
smin = min1<=i<=n si, smax = max1<=i<=n si
tmin = min1<=i<=n ti, tmax = max1<=i<=n ti
Intersect(I) = [smax, tmin], or NULL if tmin < smax
Range(I) = [smin, tmax]
Intersect
[6, 12]
assigned_to:[3, 12]
assigned_to:[6, 20]
Soldier#123
Platoon#456
Soldier#789
Range
[3, 20]

35. Temporal Operators temporal_extent (GP, IT)G  {(μ, i)} Given:
a graph pattern GP, an interval type IT {intersect, range},
a temporal RDF graph G
Find:
{(μ, i) | μ [[GP]]TRIPLES(G) and
i intersect/range(int_extension(μ(GP))) }
Example: Find all pairs of soldiers who were members of the 101st Airborne Division at the same time and return the times of joint membership?
ANS  temporal_extent(‘(?x assigned_to <101st Airborne Div>)
(?y assigned_to <101st Airborne Div>)’,
‘intersect’)G

36. Temporal Operators temporal_restrict (GP, IT, tf($t))G  {(μ, i)}
Given:
a graph pattern GP, an interval type IT {intersect, range},
a temporal formula tf defined over I and a variable $t,
a temporal RDF graph G
Find:
{(μ, i) | μ [[GP]]TRIPLES(G) and i int/range(int_extension(μ(GP)))
and tf evaluates to true for $t = i}
Example: Which members of the 3rd Armored Division participated in battles during September 1944?
ANS  temporal_restrict(‘(?x assigned_to <3rd Armored Div>)
(<3rd Armored Div> participates_in ?y)’, ‘intersect’,
during($t, [09:01:1944, 09:31:1944]) = true)G

37. Temporal Operators
temporal_eval (GP1, IT1, GP2, IT2, tf($t1, $t2))G  {(μ1, i1, μ2, i2)}
Given:
a graph pattern GP1, a graph pattern GP2, an interval type IT1 {intersect, range},
an interval type IT2 {intersect, range}, a temporal formula tf defined over I
and variables $t1, $t2, a temporal RDF graph G
Find:
{(μ1, i1, μ2, i2) | μ [[GP1]]TRIPLES(G) and μ [[GP2]]TRIPLES(G)
and i1 int/range(int_closure(μ1(v1))) and i2 int/range(int_closure(μ2(v2)))
and tf evaluates to true for $t1 = i1 and $t2 = i2}
Example: Which speeches by President Roosevelt were given during a military event
ANS  spatial_restrict(‘(<President Roosevelt> gives ?x)’, ‘intersect’,
‘(?y participates_in ?z)’, ‘intersect’,
during($t1, $t2) = true)G

38. Implementation Scheme

39. Overview Extended ORDBMS (Oracle 10g) Challenges RDF(S) Inferencing
Defined storage and indexing scheme
User-defined functions for temporal RDFS inferencing
User-defined functions for querying
Challenges
Thematic relationships can be directly stated but spatial and temporal relationships require additional computation
Spatial and temporal properties of subgraphs aren’t known until query execution time … challenging to index
RDF(S) Inferencing
If statements have an associated valid time, this must be taken into account when performing inferencing
(x, rdfs:subClassOf, y) : [1, 4] AND (y, rdfs:subClassOf, z) : [3, 5]  (x, rdfs:subClassOf, z) : [3, 4]
This slide gives an overview of our implementation approach.
Challenges in comparison to our other work on semantic analytics and to traditional st querying
Also have to handle aspects of RDF(S) inferencing with temporal triples – example

40. Existing Oracle Technology
Semantic Technology Component
Storage Structures for RDF(S) Data (non-spatial, non-temporal)
RDFS Inference Procedures (non-temporal)
SQL-based Querying (non-spatial, non-temporal)
Spatial Component
Spatial Types – SDO_GEOMETRY
Implementation of Spatial_Region
Spatial Indexing
Spatial Operators
Implementation builds upon existing Oracle Technologies

41. SQL-based Querying Approach
SQL Table Functions
SELECT X, Y
FROM TABLE (Table_Func(…));
X Y Z
a b c
d e f
… … …
Initial implementation uses SQL table functions .. Benefits compared to new query language .. No data transformation, integrate easily with legacy relational data
Use table function in same way as db table name, describe illustration

42. Spatial Functions
spatial_extent (graphPattern VARCHAR, spatialVar VARCHAR,
ontology RDFModels, <geom SDO_GEOMETRY>,
<spatialRelation VARCHAR>)
return AnyDataSet;
spatial_eval (graphPattern VARCHAR, spatialVar VARCHAR,
graphPattern2 VARCHAR, spatialVar2 VARCHAR,
spatialRelation VARCHAR, ontology RDFModels)
return AnyDataSet;
We defined 4 table functions – 2 spatial and 2 temporal
The first is spatial_extent designed to retrieve spatial region for a given thematic context and allows optional filtering of results based on spatial predicate.
List inputs and optional params – returns variable bindings
Go through example

43. Temporal Functions
temporal_extent (graphPattern VARCHAR, intervalType VARCHAR,
ontology RDFModels, <start DATE>, <end DATE>,
<temporalRel VARCHAR>)
return AnyDataSet;
temporal_eval (graphPattern VARCHAR, intervalType VARCHAR,
graphPattern2 VARCHAR, intervalType2 VARCHAR,
temporalRel VARCHAR, ontology RDFModels)
return AnyDataSet;
2 temporal operators. 1st is designed to construct temporal interval for a context instance and optionally filter the results based on a temporal predicate.

44. Storage Scheme Temporal Indexing Procedure Load RDF Data with Oracle
This slide shows the storage scheme for our spatial and temporal RDF data. The existing schema for Oracle RDF is shown on the left. Triples are stored after normalization in 2 tables RDFValues stores URIs and a generated id and RDFTriples stores subj, prop, obj id triples along with other housekeeping info. Any triples inferred through RDFS semantics are materialized in an InferredTriples table that has the same columns as RDFTriples.
Our additional indexing structures are shown on the left.
Mention that ST RDF can be serialized as plain RDF. Reification, etc.
To load the data users first load the RDF with Oracle Semantic Data Store.
We provide a procedure to build a spatial index. Ontology is queried to retrieve all instances of Spatial Region. Get id and create geometry object and store in SpatialData table.
Also provide a procedure for Temporal indexing. Reads the reifications and creates the Temporal Triples table where we have subj, prop, obj and start time end time. Stored as date-time values.
This is a bit more complicated because we have to handle RDFS inferencing and temporal labels for inferred triples.
Spatial Indexing Procedure
Thematic Indexes (on TemporalTriples)
(subj_id, prop_id, obj_id)
(prop_id, subj_id, obj_id)
(obj_id, prop_id, subj_id)

45. RDFS Inferencing Rules
Temporal Inferencing
RDFS Inferencing Rules
(x, rdf:type, y) AND (y, rdfs:subClassOf, z)  (x, rdf:type, z)
(x, p, y) AND (p, rdfs:domain, a)  (x, rdf:type, a)
(x, p, y) AND (p, rdfs:range, b)  (y, rdf:type, b)
(x, p, y) AND (p, rdfs:subPropertyOf, z)  (x, z, y)
Example:
(x, participates_in, e):[2, 5]
(y, participates_in, e):[3, 7]
(z, participates_in, e):[6, 9]
By rule 3: (e, rdf:type, event):[2, 9]
We will illustrate the inferencing with the following example.
We are not concerned with evolution of ontology schema over time. What we are concerned with are inferences that affect instance-level statements. These come from the following rules.
With each of these it turns out that the interval for the inferred statement is the union of the intervals of the asserted statements.
Consider the example of an event e .. Go through example
Interval Union

46. Temporal Inferencing Algorithm
create table asserted_temporal_triples (subj, prop, obj, start_date, end_date)
perform schema-level inferencing
perform instance-level inferencing
sort redundant_triples by (subj_id, prop_id, obj_id, start)
make a single pass and merge overlapping intervals for same statement
insert updated triples and intervals into final temporal_triples table
asserted_temporal_triples
(x, participates_in, e):[2, 5]
(y, participates_in, e):[3, 7]
(z, participates_in, e):[6, 9]
redundant_triples
(e, rdf:type, event):[2, 5]
(e, rdf:type, event):[3, 7]
(e, rdf:type, event):[6, 9]
temporal_triples
(e, rdf:type, event):[2, 9]

47. Query Function Implementation
Oracle Extensibility Framework
ODCITable Interface
Start()
Prepare SQL query over TemporalTriples and SpatialData tables – Base Query
Fetch()
Retrieve row from base query and do additional processing (e.g., construct int/range intervals)
Close()
Final cleanup (e.g., close DB cursors)
The query operators were implemented using oracle’s extensibility framework. Note this is not unique to oracle other DBs have similar extensibility frameworks.
To create table functions, you implement ODCITable Interface. Has a start(), fetch() and close() method.
Start() – generally you prepare a SQL query against some underlying structures and parse the query
Fetch() – usually this method retrieves a result from prepared query and returns it or does other processing and returns altered result
Kernel calls fetch() for a desired number of rows repeatedly until all rows are returned
Close() – method does housekeeping after function releases resources, etc.

48. Temporal Filtering Example
[2, 7]
[1, 5]
[4, 8] 1. 2. 3.
Intersection:
Range:
[4, 5]
[1, 8]
Partial Filter on Each Edge: during (3, 6)
Intersection: (start <= 6) and (end >= 3)
Intersection: (start > 3) and (end < 6)

49. Experimental Evaluation

50. Evaluation Environment Objective Dataset
Oracle 10g R2 on 64-bit Solaris 9
Four 1.8 GHz Ultra Sparc IV processors 8GB RAM
512 MB buffer cache, 512 MB pga_aggregate_target
Objective
Test scalability w.r.t (1) dataset size, (2) query complexity
Dataset
Synthetic RDF Graph1 – Historical Battlefield Analysis (SynHist)
Spatial: US Census block group polygons
Temporal: random intervals
Real-world Data – Political Domain (GovTrack)
Spatial: US Census congressional district polygons
Temporal: given in data
Matthew Perry "TOntoGen: A Synthetic Data Set Generator for Semantic Web Applications", AIS SIGSEMIS Bulletin Volume 2 Issue 2 (April - June) 2005, pp

51. Dataset Characteristics

52. Scalability w.r.t. Dataset Size

53. Scalability w.r.t. Dataset Size

54. Scalability w.r.t. Graph Pattern Size

55. Scalability w.r.t. Graph Pattern Size

56. Scalability of Spatiotemporal Queries

57. Query Language Support

58. SPARQL SPARQL-ST Overview
W3C recommended query language for RDF data (as of Jan. 15, 2008)
Graph pattern-based queries (subgraph match)
SPARQL-ST
Spatial variables
Temporal variables
Spatial filter expressions
Temporal filter expressions

59. Intro to SPARQL Basic Query: SELECT ?b, ?p
WHERE { ?b rdf:type usbill:HouseBill .
?b usbill:sponsor ?p } b p
<
<
<
<
Filtered Query:
SELECT ?b
WHERE { ?b rdf:type usbill:HouseBill .
?b rdfs:label ?l .
FILTER (regex(?l, “handgun”)) }

60. SPARQL-ST: Spatiotemporal Graph Pattern
Sets of Terms:
UL = URIs U Literals U = URIs RT = RDF Terms
Sets of Variables:
VN = Variables
VS = Spatial Variables
VT = Temporal Variables
Triple Pattern: 3-tuple from (UL VN) x (U VN) x (UL VN)
Spatial Triple Pattern: 3-tuple from (UL VN VS) x (U VN ) x (UL VN VS)
Spatiotemporal Triple Pattern:
4-tuple from (UL VN VS) x (U VN ) x (UL VN VS) x (VT)
Spatiotemporal graph patterns are constructed from triple patterns and/or spatial triple patterns and/or spatiotemporal triple patterns

61. SPARQL-ST Mappings SELECT ?c, %s, #t1
WHERE { <Politician_123> on_committee ?c #t1 .
<Politician_123> represents ?d #t2 .
?d located_at %s #t3 }
Maps to single URI
Maps to a time interval
Maps to a set of triples
Committee_456
on_committee :
[1990, 2000]
uses_crs :
[-∞, + ∞]
NAD83
Politician_123
Polygon_1
exterior : [-∞, + ∞]
represents :
[1984, 1992]
Linear_Ring_1
located_at : [1990, 2000]
District_789
lrPosList : [-∞, + ∞]
, , …,

62. SPARQL-ST by Example
Find all politicians who were senators of Ohio at the same time and return the times of joint senatorship.
SELECT ?s1, ?s2, intersect(#t1, #t2, #t3, #t4)
WHERE {
?s1 usgov:hasRole ?a #t1 .
?a usgov:forOffice usgov:senate/oh #t2 .
?s2 usgov:hasRole ?b #t3 .
?b usgov:forOffice usgov:senate/oh #t4 .
FILTER (?s1 != ?s2) }

63. SPARQL-ST by Example
Find all House members who sponsored a bill after April 2, 2008
SELECT ?p, ?b
WHERE {
?p usgov:hasRole ?r #t1 .
?r usgov:forOffice ?o #t2 .
?o usgov:isPartOf usgov:congress/house #t3 .
?p usgov:sponsor ?b #t4 .
TEMPORAL FILTER (
after(intersect(#t1, #t2, #t3, #t4),
interval(04:02:2008, 04:02:2008, MM:DD:YYYY))) }

64. SPARQL-ST by Example
Find all politicians that represent areas within 100 miles of the district represented by Nancy Pelosi.
SELECT ?n
WHERE {
?p foaf:name ?n .
?p usgov:hasRole ?r .
?r usgov:forOffice ?o .
?o usgov:represents ?q .
?q stt:located_at %g .
?a foaf:name “Nancy Pelosi” .
?a usgov:hasRole ?b .
?b usgov:forOffice ?c .
?c usgov:represents ?d .
?d stt:located_at %h .
SPATIAL FILTER (distance(%g, %h) <= 100 miles) }

65. SPARQL-ST by Example
Find all politicians representing congressional districts within a given geographical area at any time in March 2006
SELECT ?p
WHERE {
?p usgov:hasRole ?r #t1 .
?r usgov:forOffice ?o #t2 .
?o usgov:represents ?c #t3 .
?c stt:located_at %g #t4 .
SPATIAL FILTER (inside(%g, GEOM(POLYGON ((
, , ,
, ))) ))
TEMPORAL FILTER (
anyinteract(intersect (#t1, #t2, #t3, #t4),
interval(03:01:2008, 03:31:2008, MM:DD:YYYY))) }

66. Conclusions
Showed how the relationship-centric nature of the RDF data model can extend the state-of-the-art in modeling and querying STT data
Modeling
Many-to-many mapping between thematic and spatial objects (formalized as a context)
Querying
Support spatial and temporal relationships in graph pattern queries
More complex thematic aspects than traditional STT querying
Proposed SPARQL-ST to integrate with current standards
Implementation
Good scalability on large synthetic and real-world datasets
Only system for spatial and temporal RDF
Future Work
Semantic Associations
Sensor Web and Event Web applications

67. Publications Dissertation Related
Matthew Perry, Amit Sheth, Farshad Hakimpour, Prateek Jain. “Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data", Second International Conference on Geospatial Semantics (GeoS ‘07), Mexico City, MX, November 29 – 30, 2007
Matthew Perry, Farshad Hakimpour, Amit Sheth. "Analyzing Theme, Space and Time: An Ontology-based Approach", Fourteenth International Symposium on Advances in Geographic Information Systems (ACM-GIS '06), Arlington, VA, November , 2006
Matthew Perry and Amit Sheth. “A Framework for Spatial, Temporal and Thematic Analytics over Semantic Web Data”, submitted to VLDB Journal
Matthew Perry and Amit Sheth. “SPARQL-ST: Extending SPARQL for Spatial and Temporal Queries”, in preparation.

68. Publications Other STT Related Proposals
Amit Sheth and Matthew Perry. “Traveling the Semantic Web through Space, Time and Theme”, IEEE Internet Computing, Volume 12, Issue 2, March/April 2008
Farshad Hakimpour, Boanerges Aleman-Meza, Matthew Perry, Amit Sheth. "Data Processing in Space, Time, and Semantics Dimensions", Terra Cognita Directions to the Geospatial Semantic Web, in conjunction with the Fifth International Semantic Web Conference (ISWC '06), Athens, GA, November 6, 2006
Matthew Perry, Amit Sheth, Ismailcem Budak Arpinar. "Geospatial and Temporal Semantic Analytics", To appear in Encylopedia of Geoinformatics, Hassan A. Karimi (Ed), Idea-Group Inc., 2008
Farshad Hakimpour, Boanerges Aleman-Meza, Matthew Perry, Amit Sheth. "Spatiotemporal-Thematic Data Processing in Semantic Web", To appear in The Geospatial Web, Springer-Verlag, May, 2007
Proposals
Amit Sheth (PI), T.K. Prasad. “Spatial, Temporal and Thematic Analysis of Semantic Web Data” NSF-Small

69. Publications SemDis Related Semantics and Databases
Matthew Perry, Maciej Janik, Cartic Ramakrishnan, Conrad Ibanez, Ismailcem Budak Arpinar, Amit Sheth. "Peer-to-Peer Discovery of Semantic Associations", Second International Workshop on Peer-to-Peer Knowledge Management (P2PKM '05), San Diego, CA, July 17, 2005
Cartic Ramakrishnan, William Milnor, Matthew Perry, Amit Sheth. "Discovering Informative Connection Subgraphs in Multi-relational Graphs", SIGKDD Explorations Special Issue on Link Mining, Volume 7, Issue 2, December 2005
Matthew Perry "TOntoGen: A Synthetic Data Set Generator for Semantic Web Applications", AIS SIGSEMIS Bulletin Volume 2 Issue 2 (April - June) 2005, pp. 46 – 48
Matthew Perry and Eric Stiles. "SEMPL: A Semantic Portal", Thirteenth International World Wide Web Conference (WWW '04), New York, NY, May 17-22, 2004
Semantics and Databases
Matthew Perry, Souripriya Das, Melliyal Annamalai, Eugene Inseok Chong, Zhe Wu, Jagannathan Srinivasan. “Semantic Similarity based Top-k Queries over Resources categorized using a Taxonomy”, Submitted to VLDB 2009

70. Questions?