Transcript knoesis.wright.edu

Semantic Web: Promising technologies,
Current Applications & Future Directions
Australia, July-August 2008
Amit P. Sheth
[email protected]
Thanks Kno.e.sis team and collaborators
Knowledge Enabled Information and Services Science
Outline
• Semantic Web – very brief intro of key
capabilities and technlologies
• Real-world Applications demonstrating
benefit of semantic web technologies
• Exciting on-going research
Knowledge Enabled Information and Services Science
Evolution of the Web
Web as an oracle / assistant /
partner
- “ask to the Web”
- using semantics to leverage
2007
text + data + services + people
Web of people
- social networks, user-created content
- GeneRIF, Connotea
Web of services
- data = service = data, mashups
- ubiquitous computing
1997
Web of databases
- dynamically generated pages
- web query interfaces
Web of pages
- text, manually created links
- extensive navigation
Knowledge Enabled Information and Services Science
What is Semantic Web?
• Associating meaning with data: labeling data so it is more
meaningful to the system and people. Formal description
increases automation. Common interpretation increases
interoperability.
• TBL – focus on data: Data Web (“In a way, the Semantic
Web is a bit like having all the databases out there as one
big database.”)
• Others focus on reasoning and intelligent processing
Knowledge Enabled Information and Services Science
Semantic Web Enablers and Techniques
• Ontology: Agreement with Common Vocabulary &
Domain Knowledge; Schema + Knowledge base
• Semantic Annotation (metadata Extraction): Manual,
Semi-automatic (automatic with human verification),
Automatic
• Reasoning/computation: semantics enabled search,
integration, complex queries, analysis (paths, subgraph),
pattern finding, mining, hypothesis validation, discovery,
visualization
Knowledge Enabled Information and Services Science
Many ontologies exist
Open Biomedical Ontologies
Knowledge Enabled Information and Services Science
Open Biomedical Ontologies, http://obo.sourceforge.net/
Drug Ontology Hierarchy
(showing is-a relationships)
non_drug_
reactant
interaction_
property
formulary_
property
formulary
indication
monograph
_ix_class
prescription
_drug_
property
cpnum_
group
property
indication_
property
brandname_
individual
brandname_
undeclared
prescription
_drug_
brand_name
brandname_
composite
generic_
composite
prescription
_drug
prescription
_drug_
generic
owl:thing
interaction
interaction_
with_prescri
ption_drug
generic_
individual
Knowledge Enabled Information and Services Science
interaction_
with_non_
drug_reactant
interaction_
with_mono
graph_ix_cl
ass
N-Glycosylation metabolic pathway
N-glycan_beta_GlcNAc_9
GNT-I
attaches GlcNAc at position 2
N-acetyl-glucosaminyl_transferase_V
N-glycan_alpha_man_4
GNT-V
attaches
GlcNAc at position 6
UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2
<=>
UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2
UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021
Knowledge Enabled Information and Services Science
Information Extraction for Metadata Creation
WWW, Enterprise
Repositories
Nexis
UPI
AP
Feeds/
Documents
Digital Videos
...
...
Data Stores
Digital Maps
...
Digital Images
Create/extract as much (semantics)
metadata automatically as possible;
Use ontlogies to improve and enhance
extraction
Digital Audios
EXTRACTORS
METADATA
Knowledge Enabled Information and Services Science
Metadata and Ontology: Primary Semantic Web enablers
Deep semantics
Shallow semantics
Knowledge Enabled Information and Services Science
Automatic Semantic Metadata Extraction/Annotation
Knowledge Enabled Information and Services Science
Characteristics of Semantic Web
Self
Describing
Easy to
Understand
Semantic Web:
Machine &
IssuedThe
by
Human
a XML,
Trusted
RDF & Ontology
Readable
Authority
Convertible
Can be
Secured
Adapted from William Ruh (CISCO)
Knowledge Enabled Information and Services Science
Application Example 1:
• Status: In use today
• Where: Athens Heart Center
• What: Use of semantic Web technologies
for clinical decision support
Knowledge Enabled Information and Services Science
Operational since January 2006
Knowledge Enabled Information and Services Science
Active Semantic Electronic Medical Records (ASEMR)
Goals:
• Increase efficiency with decision support
• formulary, billing, reimbursement
• real time chart completion
• automated linking with billing
• Reduce Errors, Improve Patient Satisfaction & Reporting
• drug interactions, allergy, insurance
• Improve Profitability
Technologies:
• Ontologies, semantic annotations & rules
• Service Oriented Architecture
Thanks -- Dr. Agrawal, Dr. Wingeth, and others. ISWC2006 paper
Knowledge Enabled Information and Services Science
Demonstration
Knowledge Enabled Information and Services Science
Opportunity: exploiting clinical and biomedical data
binary
text
Scientific
Literature
Health
Information
Services
PubMed
300 Documents
Published Online
each day
Elsevier
iConsult
NCBI
User-contributed
Content (Informal) Public Datasets
GeneRifs
Genome,
Protein DBs
new sequences
daily
Clinical Data
Personal
health history
Laboratory
Data
Lab tests,
RTPCR,
Mass spec
Search, browsing, complex query, integration, workflow,
analysis, hypothesis validation, decision support.
Knowledge Enabled Information and Services Science
Application Example 2
• Status: Completed research
• Where: NIH/NIDA
• What: Understanding the genetic basis of
nicotine dependence.
• How: Semantic Web technologies (especially
RDF, OWL, and SPARQL) support information
integration and make it easy to create semantic
mashups (semantically integrated resources).
Knowledge Enabled Information and Services Science
Genome and pathway information integration
Reactome
KEGG
•pathway
•pathway
•protein
•protein
•pmid
•pmid
HumanCyc
•pathway
•protein
•pmid
Entrez Gene
•GO ID
•HomoloGene ID
GeneOntology
HomoloGene
Knowledge Enabled Information and Services Science
Entrez
Knowledge
Model
(EKoM)
BioPAX
ontology
Knowledge Enabled Information and Services Science
Gene-Pathway Data Integration–
Understanding the Genetic-basis of Nicotine Dependence
Collaborators: NIDA, NLM
Biological
Significance:
• Understand the
role of genes in
nicotine addiction
• Treatment of drug
addiction based
on genetic factors
• Identify important
genes and use for
pharmaceutical
productions
Knowledge Enabled Information and Services Science
Scenario 3
• Status: Completed research
• Where: NIH
• What: queries across integrated data
sources
– Enriching data with ontologies for integration,
querying, and automation
– Ontologies beyond vocabularies: the power of
relationships
Knowledge Enabled Information and Services Science
Use data to test hypothesis
Link between glycosyltransferase activity and
congenital muscular dystrophy?
Gene name
Interactions
Glycosyltransferase
GO
gene
Sequence
PubMed
OMIM
Congenital muscular dystrophy
Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
Knowledge Enabled Information and Services Science
In a Web pages world…
(GeneID: 9215)
has_associated_disease
Congenital muscular
dystrophy,
type 1D
has_molecular_function
Acetylglucosaminyltransferase activity
Knowledge
Enabled
and Services
Science at HCLS Workshop, WWW07
Adapted
from: Information
Olivier Bodenreider,
presentation
With the semantically enhanced data
SELECT DISTINCT ?t ?g ?d {
?t is_a GO:0016757 .
GO:0016757
?g has molecular functionglycosyltransferase
?t .
?g has_associated_phenotype ?b2 .
isa
?b2 has_textual_description ?d .
FILTER (?d, “muscular distrophy”, “i”) . GO:0008194
FILTER (?d, “congenital”,GO:0016758
“i”)
}
GO:0008375
acetylglucosaminyltransferase
GO:0008375
acetylglucosaminyltransferase
MIM:608840
Muscular dystrophy,
congenital, type 1D
has_molecular_function
LARGE
EG:9215
has_associated_phenotype
From medinfo paper.
Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
Knowledge Enabled Information and Services Science
Scenario 5
• Status: Research prototype and in progress
• Workflow withSemantic Annotation of Experimental
Data already in use
• Where: UGA
• What:
– Knowledge driven query formulation
– Semantic Problem Solving Environment (PSE)
for Trypanosoma cruzi (Chagas Disease)
Knowledge Enabled Information and Services Science
Knowledge driven query formulation
Complex queries can also include:
- on-the-fly Web services execution to retrieve additional data
- inference rules to make implicit knowledge explicit
Knowledge Enabled Information and Services Science
T.Cruzi PSE Query Interface
Figure 4: Semantic annotation of ms scientific data
Knowledge Enabled Information and Services Science
N-Glycosylation Process (NGP)
Cell Culture
extract
Glycoprotein Fraction
proteolysis
Glycopeptides Fraction
1
n
Separation technique I
Glycopeptides Fraction
n
PNGase
Peptide Fraction
Separation technique II
n*m
Peptide Fraction
Mass spectrometry
ms data
ms/ms data
Data reduction
ms peaklist
Data reduction
ms/ms peaklist
binning
Glycopeptide identification
and quantification
N-dimensional array
Peptide identification
Peptide list
Data correlation
Knowledge Enabled Information and Services Science
Signal integration
Semantic Web Process to incorporate provenance
Agent
Biological
Sample
Analysis
by MS/MS
O
Semantic
Annotation
Applications
Agent
Raw
Data to
Standard
Format
I
Data
Preprocess
O
Raw
Data
Agent
I
Standard
Format
Data
(Mascot/
Sequest)
O
Filtered
Data
Agent
DB
Search
I
Search
Results
O
Final
Output
Storage
Biological Information
Knowledge Enabled Information and Services Science
Results
Postprocess
(ProValt)
I
O
ProPreO: Ontology-mediated provenance
parent ion charge
830.9570
194.9604
2
580.2985
0.3592
parent ion m/z
688.3214
0.2526
779.4759
38.4939
784.3607
21.7736
1543.7476
1.3822
fragment ion m/z
1544.7595
2.9977
1562.8113
37.4790
1660.7776
476.5043
parent ion
abundance
fragment ion
abundance
ms/ms peaklist data
Mass Spectrometry (MS) Data
Knowledge Enabled Information and Services Science
ProPreO: Ontology-mediated provenance
<ms-ms_peak_list>
<parameter
instrument=“micromass_QTOF_2_quadropole_time_of_flight_mass_spectrometer”
mode=“ms-ms”/>
<parent_ion m-z=“830.9570” abundance=“194.9604” z=“2”/>
<fragment_ion m-z=“580.2985” abundance=“0.3592”/>
<fragment_ion m-z=“688.3214” abundance=“0.2526”/>
<fragment_ion m-z=“779.4759” abundance=“38.4939”/>
Ontological
<fragment_ion m-z=“784.3607” abundance=“21.7736”/>
Concepts
<fragment_ion m-z=“1543.7476” abundance=“1.3822”/>
<fragment_ion m-z=“1544.7595” abundance=“2.9977”/>
<fragment_ion m-z=“1562.8113” abundance=“37.4790”/>
<fragment_ion m-z=“1660.7776” abundance=“476.5043”/>
</ms-ms_peak_list>
Semantically Annotated MS Data
Knowledge Enabled Information and Services Science
Problem – Extracting relationships
between MeSH terms from PubMed
Biologically
active substance
UMLS
Semantic Network
complicates
affects
causes
causes
Lipid
affects
Disease or
Syndrome
instance_of
instance_of
???????
Fish Oils
Raynaud’s Disease
MeSH
9284
documents
5
documents
Knowledge Enabled Information and Services Science
4733
documents
PubMed
Background knowledge used
• UMLS – A high level schema of the biomedical
domain
– 136 classes and 49 relationships
– Synonyms of all relationship – using variant lookup
(tools from NLM)
– 49 relationship + their synonyms = ~350 mostly verbs
• MeSH
– 22,000+ topics organized as a forest of 16 trees
– Used to query PubMed
• PubMed
T147—effect
T147—induce
T147—etiology
T147—cause
T147—effecting
T147—induced
– Over 16 million abstract
– Abstracts annotated with one or more MeSH terms
Knowledge Enabled Information and Services Science
Method – Parse Sentences in PubMed
SS-Tagger (University of Tokyo)
SS-Parser (University of Tokyo)
• Entities (MeSH terms) in sentences occur in modified forms
• “adenomatous”
modifies
“hyperplasia”
(TOP (S
(NP (NP (DT An)
(JJ excessive)
(ADJP (JJ endogenous) (CC or) (JJ
• “An excessive
endogenous
or exogenous
modifies
exogenous)
) (NN stimulation)
) (PP
(IN by) (NPstimulation”
(NN estrogen)
) ) ) (VP (VBZ
“estrogen”
induces)
(NP (NP (JJ adenomatous) (NN hyperplasia) ) (PP (IN of) (NP (DT
• Entities
can also occur) as
of 2 or more other entities
the)
(NN endometrium)
) ) composites
)))
• “adenomatous hyperplasia” and “endometrium” occur as “adenomatous
hyperplasia of the endometrium”
Knowledge Enabled Information and Services Science
Method – Identify entities and Relationships in
Parse Tree
Modifiers
Modified entities
Composite Entities
TOP
S
VP
NP
VBZ
PP
NP
DT
the
JJ
excessive
JJ
endogenous
IN
by
ADJP
NP
induces
NN
estrogen
NP
NN
stimulation
JJ
adenomatous
CC
or
PP
NN
hyperplasia
IN
of
NP
JJ
exogenous
DT
the
Knowledge Enabled Information and Services Science
NN
endometrium
• What can we do with the extracted
knowledge?
• Semantic browser demo
Knowledge Enabled Information and Services Science
Evaluating hypotheses
Migraine
affects
Magnesium
Stress
inhibit
Patient
isa
Calcium Channel
Blockers
Complex
Query
Keyword query: Migraine[MH] + Magnesium[MH]
PubMed
Supporting
Document
sets
retrieved
Knowledge Enabled Information and Services Science
Workflow Adaptation: Why and How
• Volatile nature of execution environments
– May have an impact on multiple activities/ tasks in the
workflow
• HF Pathway
– New information about diseases, drugs becomes
available
– Affects treatment plans, drug-drug interactions
• Need to incorporate the new knowledge into
execution
– capture the constraints and relationships between
different tasks activities
Knowledge Enabled Information and Services Science
Workflow Adaptation Why?
New knowledge about
treatment found during
the execution of the pathway
New knowledge about drugs,
drug drug interactions
Knowledge Enabled Information and Services Science
Workflow Adaptation: How
• Decision theoretic approaches
– Markov Decision Processes
• Given the state S of the workflow when an
event E occurs
– What is the optimal path to a goal state G
– Greedy approaches rely on local optimization
• Need to choose actions based on optimality across
the entire horizon, not just the current best action
– Model the horizon and use MDP to find the
best path to a goal state
Knowledge Enabled Information and Services Science
Conclusion
• semantic web technologies can help with:
– Fusion of data: semi-structured, structured,
experimental, literature, multimedia
– Analysis and mining of data, extraction,
annotation, capture provenance of data
through annotation, workflows with SWS
– Querying of data at different levels of
granularity, complex queries, knowledge-driven
query interface
– Perform inference across data sets
Knowledge Enabled Information and Services Science
Take home points
• Shift of paradigm: from browsing to
querying
• Machine understanding:
– extracting knowledge from text
– Inference, software interoperation
• Semantic-enabled interfaces towards
hypothesis validation
Knowledge Enabled Information and Services Science
References
1.
2.
3.
4.
5.
6.
•
A. Sheth, S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, and K. Gallagher, Active
Semantic Electronic Medical Record, Intl Semantic Web Conference, 2006.
Satya Sahoo, Olivier Bodenreider, Kelly Zeng, and Amit Sheth, An Experiment in Integrating
Large Biomedical Knowledge Resources with RDF: Application to Associating Genotype and
Phenotype Information
WWW2007 HCLS Workshop, May 2007.
Satya S. Sahoo, Kelly Zeng, Olivier Bodenreider, and Amit Sheth, From "Glycosyltransferase to
Congenital Muscular Dystrophy: Integrating Knowledge from NCBI Entrez Gene and the Gene
Ontology, Amsterdam: IOS, August 2007, PMID: 17911917, pp. 1260-4
Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner , Amit P. Sheth, An
ontology-driven semantic mash-up of gene and biological pathway information: Application to the
domain of nicotine dependence, submitted, 2007.
Cartic Ramakrishnan, Krzysztof J. Kochut, and Amit Sheth, "A Framework for Schema-Driven
Relationship Discovery from Unstructured Text", Intl Semantic Web Conference, 2006, pp. 583596
Satya S. Sahoo, Christopher Thomas, Amit Sheth, William S. York, and Samir Tartir,
"Knowledge Modeling and Its Application in Life Sciences: A Tale of Two Ontologies", 15th
International World Wide Web Conference (WWW2006), Edinburgh, Scotland, May 23-26, 2006.
Demos at: http://knoesis.wright.edu/library/demos/
Knowledge Enabled Information and Services Science
More about the Relationship Web
Relationship Web takes you away from “which document”
could have information I need, to “what’s in the resources”
that gives me the insight and knowledge I need for
decision making.
Amit P. Sheth, Cartic Ramakrishnan:
Relationship Web: Blazing Semantic Trails
between Web Resources. IEEE Internet
Computing July 2007 (to appear) [.pdf]
Knowledge Enabled Information and Services Science
Events: 3 Dimensions – Spatial, Temporal and Thematic
Spatial
Temporal
Thematic
Knowledge Enabled Information and Services Science
Events and STT dimensions
• Powerful mechanism to integrate content
– Describes the Real-World occurrences
– Can have video, images, text, audio all of the same event
– Search and Index based on events and STT relations
• Many relationship types
– Spatial:
• What events happened near this event?
• What entities/organizations are located nearby?
– Temporal:
• What events happened before/after/during this event?
– Thematic:
• What is happening?
• Who is involved?
• Going further
– Can we use What? Where? When? Who? to answer Why? / How?
– Use integrated STT analysis to explore cause and effect
Knowledge Enabled Information and Services Science
Example Scenario: Sensor Data Fusion and Analysis
High-level Sensor
Low-level Sensor
How do we determine if the three images depict …
• the same time and same place?
• the same entity?
• a serious threat?
Knowledge Enabled Information and Services Science
50
Data Pyramid
Sensor Data Pyramid
Ontology
Metadata
Knowledge
Entity Metadata
Information
Feature Metadata
Raw Sensor (Phenomenological) Data
Data
Knowledge Enabled Information and Services Science
What is Sensor Web Enablement?
http://www.opengeospatial.org/projects/groups/sensorweb
Knowledge Enabled Information and Services Science
52
SWE Components - Languages
Sensor and Processing
Description Language
Information Model
for Observations and
Sensing
Observations &
Measurements
(O&M)
GeographyML
(GML)
SensorML
(SML)
TransducerML
(TML)
Common Model for
Geographical
Information
Real Time Streaming
Protocol
Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
Knowledge Enabled Information and Services Science
SWE Components – Web Services
Sensor Planning
Service: Command
and Task Sensor
Systems
Sensor
Observation
Service: Access
Sensor Description
and Data
SOS
Discover Services,
Sensors, Providers,
Data
SPS
SAS
Catalog
Service
Clients
Accessible from various
types of clients from
PDAs and Cell Phones
to high end
Workstations
Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
Knowledge Enabled Information and Services Science
Sensor Alert
Service Dispatch
Sensor Alerts to
registered Users
Semantic Sensor Web
Knowledge Enabled Information and Services Science
55
Semantic Sensor Data-to-Knowledge Architecture
Knowledge
• Object-Event Relations
• Spatiotemporal Associations
Data Storage
(Raw Data, XML, RDF)
Semantic Analysis and Query
• Provenance/Context
Information
• Entity Metadata
Feature Extraction and Entity Detection
• Feature Metadata
Semantic
Annotation
Data
• Raw Phenomenological Data
Sensor Data Collection
Ontologies
• Space Ontology
• Time Ontology
• Domain Ontology
Knowledge Enabled Information and Services Science
56
Semantic Sensor Observation Service
S-SOS Client
BuckeyeTraffic.org
Collect Sensor Data
HTTP-GET
Request
O&M-S or SML-S
Response
Semantic Sensor Observation Service
Get Observation
Oracle
SensorDB
Describe Sensor
Get Capabilities
Ontology & Rules
SWE
Annotated SWE
• Weather
• Time
Semantic Annotation Service
• Space
57
Knowledge Enabled Information and Services Science
Standards Organizations
W3C Semantic Web
• Resource Description Framework
• SML-S
• RDF Schema
• O&M-S
• SAWSDL*
• Web Ontology Language
• TML-S
• SA-REST
• Semantic Web Rule Language
OGC Sensor Web Enablement
• SensorML
Web Services
• Web Services Description Language
Sensor
Ontology
• O&M
• TransducerML
• REST
• GeographyML
Sensor
Ontology
National Institute for Standards
and Technology
• Semantic Interoperability Community
of Practice
• Sensor Standards Harmonization
* SAWSDL is now a W3C Recommendation
Knowledge Enabled Information and Services Science
Current Research Towards STT Relationship Analysis
• Modeling Spatial and Temporal data using SW standards (RDF(S))1
– Upper-level ontology integrating thematic and spatial dimensions
– Use Temporal RDF3 to encode temporal properties of relationships
– Demonstrate expressiveness with various query operators built upon
thematic contexts
• Graph Pattern queries over spatial and temporal RDF data2
– Extended ORDBMS to store and query spatial and temporal RDF
– User-defined functions for graph pattern queries involving spatial
variables and spatial and temporal predicates
– Implementation of temporal RDFS inferencing
– Extended SPARQL for STT queries
1.
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 10 - 11, 2006
2.
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
3.
Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”, ESWC 2005: 93-107
Knowledge Enabled Information and Services Science
Example Domain Ontology
Knowledge Enabled Information and Services Science
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]
Temporal Inferencing
Interval Union:
(Student1, rdf:type, Student) : [2002, 2008]
1. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”. ESWC 2005: 93-107
Knowledge Enabled Information and Services Science
Contexts Linking Non-Spatial Entities to Spatial Entities
E2:Soldier
E4:Address
located_at
lives_at
located_at
lives_at
E6:Address
E1:Soldier
assigned_to
Georeferenced Coordinate
Space
(Spatial Regions)
occurred_at
E7:Battle
participates_in
E8:Military_Unit
E8:Military_Unit
participates_in
E5:Battle
Named Places
assigned_to
occurred_at
Residency
Battle Participation
Spatial Occurrents
KnowledgeDynamic
Enabled Entities
Information and Services Science
E3:Soldier
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
Knowledge Enabled Information and Services Science
An Example: Battlefield Intelligence
has_symptom
?Symptom
?Person
Chemical_X
induces
participated_in
?Military_Event
How are these events
related in time?
spotted_at
?Enemy
?Location_1
located_at
How close are these
locations in space?
?Location_2
member_of
Enemy_Group_Y
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)’);
Knowledge Enabled Information and Services Science
SPARQL-ST – Spatio-Temporal SPARQL
SELECT ?c, %s, #t1
WHERE { <Politician_123> on_committee ?c #t1 .
<Politician_123> represents ?d #t2 .
?d located_at %s #t3 }
Maps to a
time interval
on_committee :
[1990, 2000]
Maps to single URI
Maps to a set of triples
Committee_456
uses_crs :
[-∞, + ∞]
Politician_123
represents :
[1984, 1992]
Polygon_1
District_789
located_at :
[1990, 2000]
NAD83
exterior : [-∞, + ∞]
Linear_Ring_1
lrPosList : [-∞, + ∞]
-85.32 34.1, -85.33 34.2, …, -85.32 34.1
Knowledge Enabled Information and Services Science
The Machine Factor
Formal representation of knowledge
– RDF(S), OWL, etc.
Statistical analysis
– Similarity
– Cooccurrence
– Clustering
Intelligent aggregation of knowledge
– Collaboration/Problem Solving Environments
– Decision support tools
Knowledge Enabled Information and Services Science
Putting the man back in Semantics
The Semantic Web focuses on artificial agents
“Web 2.0 is made of people” (Ross Mayfield)
“Web 2.0 is about systems that harness collective
intelligence.” (Tim O’Reilly)
The relationship web combines the skills of humans and
machines
Knowledge Enabled Information and Services Science
Putting the man back in Semantics
“Web 2.0 is about systems that harness collective intelligence.”
The relationship
webiscombines
the
skills
humansMayfield)
and
machines
The
Semantic
Web focuses
onofartificial
agents
“Web
2.0
made
of
people”
(Ross
(Tim O’Reilly)
Knowledge Enabled Information and Services Science
Going places …
Formal
Powerful
Implicit
Social,
Informal
Knowledge Enabled Information and Services Science