Knowledge Enabled Information and Services Science Semantic Web: Promising technologies, Current...
-
date post
19-Dec-2015 -
Category
Documents
-
view
213 -
download
0
Transcript of Knowledge Enabled Information and Services Science Semantic Web: Promising technologies, Current...
Knowledge Enabled Information and Services Science
Semantic Web: Promising technologies, Current Applications & Future Directions
Australia, July-August 2008
Amit P. [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 of pages - text, manually created links - extensive navigation
2007
1997
Web of databases - dynamically generated pages - web query interfaces
Web of services - data = service = data, mashups - ubiquitous computing
Web of people - social networks, user-created content - GeneRIF, Connotea
Web as an oracle / assistant / partner - “ask to the Web” - using semantics to leverage text + data + services + people
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
• 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
Semantic Web Enablers and Techniques
Knowledge Enabled Information and Services Science
Open Biomedical Ontologies
Open Biomedical Ontologies, http://obo.sourceforge.net/
Many ontologies exist
Knowledge Enabled Information and Services Science
Drug Ontology Hierarchy (showing is-a relationships)
owl:thing
prescription_drug
_ brand_na
me
brandname_unde
clared
brandname_comp
osite
prescription_drug
monograph_ix_cla
ss
cpnum_ group
prescription_drug
_ property
indication_
property
formulary_
property
non_drug_
reactant
interaction_proper
ty
property
formulary
brandname_indivi
dual
interaction_with_prescriptio
n_drug
interaction
indication
generic_ individua
l
prescription_drug_ generic
generic_ composit
e
interaction_ with_non_ drug_react
ant
interaction_with_monograph_ix_class
Knowledge Enabled Information and Services Science
N-Glycosylation metabolic pathway
GNT-Iattaches GlcNAc at position 2
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
GNT-Vattaches GlcNAc at position 6
UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021
N-acetyl-glucosaminyl_transferase_VN-glycan_beta_GlcNAc_9N-glycan_alpha_man_4
Knowledge Enabled Information and Services Science
WWW, EnterpriseRepositories
METADATA
EXTRACTORS
Digital Maps
NexisUPIAP
Feeds/Documents
Digital Audios
Data Stores
Digital Videos
Digital Images. . .
. . . . . .
Create/extract as much (semantics)metadata automatically as possible;
Use ontlogies to improve and enhanceextraction
Information Extraction for Metadata Creation
Knowledge Enabled Information and Services Science
Metadata and Ontology: Primary Semantic Web enablers
Shallow semantics
Deep semantics
Expr
essi
vene
ss,
Rea
soni
ng
Knowledge Enabled Information and Services Science
Automatic Semantic Metadata Extraction/Annotation
Knowledge Enabled Information and Services Science
Characteristics of Semantic Web
SelfDescribing
Machine &Human
Readable
Issued bya TrustedAuthority
Easy toUnderstand
ConvertibleCan be
Secured
The Semantic Web:XML, RDF & Ontology
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
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
Active Semantic Electronic Medical Records (ASEMR)
Knowledge Enabled Information and Services Science
Demonstration
Knowledge Enabled Information and Services Science
Opportunity: exploiting clinical and biomedical data
text
Health Information
Services
Elsevier iConsult
Scientific Literature
PubMed300 Documents
Published Online each day
User-contributed Content (Informal)
GeneRifs
NCBI Public Datasets
Genome, Protein DBs
new sequencesdaily
Laboratory Data
Lab tests, RTPCR,
Mass spec
Clinical Data
Personal health history
Search, browsing, complex query, integration, workflow, analysis, hypothesis validation, decision support.
binary
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
Entrez Gene
ReactomeKEGG
HumanCyc
GeneOntology HomoloGene
Genome and pathway information integration
• pathway
• protein
• pmid
• pathway
• protein
• pmid
• pathway
• protein
• pmid
• GO ID • HomoloGene
ID
Knowledge Enabled Information and Services Science
BioPAXontology
EntrezKnowledge
Model(EKoM)
Knowledge Enabled Information and Services Science
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
Gene-Pathway Data Integration–Understanding the Genetic-basis of Nicotine DependenceCollaborators: NIDA, NLM
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
gene
GO
PubMed
Gene name
OMIM
Sequence
Interactions
Glycosyltransferase
Congenital muscular dystrophy
Link between glycosyltransferase activity and congenital muscular dystrophy?
Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
Knowledge Enabled Information and Services Science
In a Web pages world…
Congenital muscular dystrophy,type 1D
(GeneID: 9215)
has_associated_disease
has_molecular_function
Acetylglucosaminyl-transferase activity
Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
Knowledge Enabled Information and Services Science
With the semantically enhanced data
MIM:608840Muscular dystrophy,
congenital, type 1D
GO:0008375
has_associated_phenotype
has_molecular_function
EG:9215LARGE
acetylglucosaminyl-transferase
GO:0016757glycosyltransferase
GO:0008194isa
GO:0008375acetylglucosaminyl-
transferase
GO:0016758
From medinfo paper.Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07
SELECT DISTINCT ?t ?g ?d { ?t is_a GO:0016757 . ?g has molecular function ?t . ?g has_associated_phenotype ?b2 . ?b2 has_textual_description ?d .FILTER (?d, “muscular distrophy”, “i”) . FILTER (?d, “congenital”, “i”) }
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
Glycoprotein Fraction
Glycopeptides Fraction
extract
Separation technique I
Glycopeptides Fraction
n*m
n
Signal integrationData correlation
Peptide Fraction
Peptide Fraction
ms data ms/ms data
ms peaklist ms/ms peaklist
Peptide listN-dimensional arrayGlycopeptide identificationand quantification
proteolysis
Separation technique II
PNGase
Mass spectrometry
Data reductionData reduction
Peptide identificationbinning
n
1
Knowledge Enabled Information and Services Science
Storage
Standard FormatData
Raw Data
Filtered Data
Search Results
Final Output
Agent Agent Agent Agent Biological Sample Analysis
by MS/MS
Raw Data to
Standard Format
DataPre-
process
DB Search
(Mascot/Sequest)
Results Post-
process
(ProValt)
O I O I O I O I O
Biological Information
SemanticAnnotationApplications
Semantic Web Process to incorporate provenance
Knowledge Enabled Information and Services Science
830.9570 194.9604 2
580.2985 0.3592
688.3214 0.2526
779.4759 38.4939
784.3607 21.7736
1543.7476 1.3822
1544.7595 2.9977
1562.8113 37.4790
1660.7776 476.5043
parent ion m/z
fragment ion m/z
ms/ms peaklist data
fragment ionabundance
parent ionabundance
parent ion charge
ProPreO: Ontology-mediated provenance
Mass Spectrometry (MS) Data
Knowledge Enabled Information and Services Science
<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”/><fragment_ion m-z=“784.3607” abundance=“21.7736”/><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>
OntologicalConcepts
ProPreO: Ontology-mediated provenance
Semantically Annotated MS Data
Knowledge Enabled Information and Services Science
Problem – Extracting relationships between MeSH terms from PubMed
Biologically active substance
LipidDisease or Syndrome
affects
causes
affectscauses
complicates
Fish Oils Raynaud’s Disease???????
instance_of instance_of
UMLS Semantic Network
MeSH
PubMed9284 documents
4733 documents
5 documents
Knowledge Enabled Information and Services Science
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 – Over 16 million abstract– Abstracts annotated with one or more MeSH terms
T147—effect T147—induce T147—etiology T147—cause T147—effecting T147—induced
Knowledge Enabled Information and Services Science
Method – Parse Sentences in PubMed
SS-Tagger (University of Tokyo)
SS-Parser (University of Tokyo)
(TOP (S (NP (NP (DT An) (JJ excessive) (ADJP (JJ endogenous) (CC or) (JJ exogenous) ) (NN stimulation) ) (PP (IN by) (NP (NN estrogen) ) ) ) (VP (VBZ induces) (NP (NP (JJ adenomatous) (NN hyperplasia) ) (PP (IN of) (NP (DT the) (NN endometrium) ) ) ) ) ) )
• Entities (MeSH terms) in sentences occur in modified forms• “adenomatous” modifies “hyperplasia”• “An excessive endogenous or exogenous stimulation” modifies
“estrogen”• Entities can also occur as composites of 2 or more other entities
• “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
TOP
NP
VP
S
NPVBZ
induces
NPPP
NPINof
DTthe
NNendometrium
JJadenomatous
NNhyperplasia
NP PP
INby
NNestrogenDT
the
JJexcessive ADJP NN
stimulation
JJendogenous
JJexogenous
CCor
MeSHIDD004967MeSHIDD006965 MeSHIDD004717
UMLS ID
T147
ModifiersModified entitiesComposite Entities
Knowledge Enabled Information and Services Science
• What can we do with the extracted knowledge?
• Semantic browser demo
Knowledge Enabled Information and Services Science
PubMed
Complex Query
SupportingDocument setsretrieved
Migraine
Stress
Patient
affects
isaMagnesium
Calcium Channel Blockers
inhibit
Keyword query: Migraine[MH] + Magnesium[MH]
Evaluating hypotheses
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 abouttreatment found duringthe 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. 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.
2. 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.
3. 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
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.
5. Cartic Ramakrishnan, Krzysztof J. Kochut, and Amit Sheth, "A Framework for Schema-Driven Relationship Discovery from Unstructured Text", Intl Semantic Web Conference, 2006, pp. 583-596
6. 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 Science50
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?
Example Scenario: Sensor Data Fusion and Analysis
Knowledge Enabled Information and Services Science
Sensor Data Pyramid
Raw Sensor (Phenomenological) Data
Feature Metadata
Entity Metadata
Ontology Metadata
Expr
essi
vene
ss
Data
Information
Knowledge
Data Pyramid
Knowledge Enabled Information and Services Science52
What is Sensor Web Enablement?
http://www.opengeospatial.org/projects/groups/sensorweb
Knowledge Enabled Information and Services Science
GeographyML (GML)
TransducerML (TML)
Observations &
Measurements (O&M)
Information Model for Observations and Sensing
Sensor and Processing Description Language
Real Time Streaming Protocol
Common Model for Geographical
Information
SensorML (SML)
Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
SWE Components - Languages
Knowledge Enabled Information and Services Science
CatalogService
SOS
SAS
SPS
Clients
Sensor Observation
Service: Access Sensor
Description and Data
Sensor Planning Service:
Command and Task Sensor
Systems
Sensor Alert Service Dispatch Sensor Alerts to registered Users
Discover Services, Sensors,
Providers, Data
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.
SWE Components – Web Services
Knowledge Enabled Information and Services Science55
Semantic Sensor Web
Knowledge Enabled Information and Services Science56
Data
• Raw Phenomenological Data
Semantic Sensor Data-to-Knowledge Architecture
Information
• Entity Metadata
• Feature Metadata
Knowledge
• Object-Event Relations
• Spatiotemporal Associations
• Provenance/Context
Feature Extraction and Entity Detection
Data Storage(Raw Data, XML, RDF)
Semantic Analysis and Query
Sensor Data Collection
Ontologies• Space Ontology
• Time Ontology
• Domain Ontology
SemanticAnnotation
Knowledge Enabled Information and Services Science
57
Ontology & Rules
• Weather
• Time
• Space
OracleSensorDB
Get Observation
Describe Sensor
Semantic Sensor Observation Service
Collect Sensor Data
BuckeyeTraffic.org
Get Capabilities
Semantic Annotation Service
S-SOS Client
SWE Annotated SWE
HTTP-GET Request
O&M-S or SML-S Response
Semantic Sensor Observation Service
Knowledge Enabled Information and Services Science
Standards Organizations
OGC Sensor Web Enablement• SensorML
• O&M
• TransducerML
• GeographyML
Web Services• Web Services Description
Language
• REST
National Institute for Standards and Technology
• Semantic Interoperability Community of Practice
• Sensor Standards Harmonization
W3C Semantic Web• Resource Description
Framework
• RDF Schema
• Web Ontology Language
• Semantic Web Rule Language
• SAWSDL*
• SA-REST
• SML-S
• O&M-S
• TML-S
Sensor Ontology
* SAWSDL is now a W3C Recommendation
Sensor Ontology
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
Undergraduate Graduate
rdfs:subClassOfrdfs:subClassOf
Student1
rdf:type : [2004, 2008]rdf:type : [2002, 2004]
rdf:type[?, ?]
Temporal InferencingInterval Union: (Student1, rdf:type, Student) : [2002, 2008]
1. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”. ESWC 2005: 93-107
Associate temporal label with a statement that represents the valid time of the statement
(Student1, rdf:type, Graduate) : [2004, 2008]
Knowledge Enabled Information and Services Science
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
Contexts Linking Non-Spatial Entities to Spatial Entities
ResidencyBattle Participation
E1:Soldier
Knowledge Enabled Information and Services Science
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
?Person?Symptom
Chemical_X
?Military_Event?Location_1
Enemy_Group_Y
?Location_2
?Enemy
participated_in
has_symptom
induces
located_at
spotted_at
member_of
How close are these locations in space?
How are these eventsrelated in time?
SELECT ?pFROM 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
Politician_123
Committee_456
District_789
Polygon_1
Linear_Ring_1
NAD83
-85.32 34.1, -85.33 34.2, …, -85.32 34.1
on_committee : [1990, 2000]
represents : [1984, 1992]
located_at : [1990, 2000]
uses_crs : [-∞, + ∞]
exterior : [-∞, + ∞]
lrPosList : [-∞, + ∞]
SELECT ?c, %s, #t1WHERE { <Politician_123> on_committee ?c #t1 . <Politician_123> represents ?d #t2 . ?d located_at %s #t3 }
Maps to single URI
Maps to a set of triplesMaps to a time interval
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 made of people” (Ross Mayfield)“Web 2.0 is about systems that harness collective intelligence.”
(Tim O’Reilly)The Semantic Web focuses on artificial agentsThe relationship web combines the skills of humans and machines
Knowledge Enabled Information and Services Science
Going places …
Formal
Social,Informal
Implicit
Powerful