1. Semantic-Web, Triple-Strores, and SPARQL
BCHB697

2. Outline Semantic Web Triple-Stores SPARQL
URIs & Links, Ontologies/RDF/RDFS
Triple-Stores
Semi-structured data, again
Federation
SPARQL
SQL for triple-stores / semantic web
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3. (HTML) Web-pages Designed for humans to read. Page content
Prose, Numbers, Text, Pictures, Layout
Anchors/Links w/ Label
Search engines exploit the text and links to find useful information
PageRank – number of pages that link to you
UniProt XRefs – jump to related information
Still, how to distinguish between:
jaguar & jaguar; stamp & stamp; C4 and C4?
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4. Semantic Web Annotate instances with semantics
Jaguar (big cat) & Jaguar (car)
Stamp (collector's item) & Stamp (one's foot, verb)
C4 (gene) & C4 (explosive)
Annotate instances with:
Semantic information (name, size, value), and
Semantic relationships to other instances
Who manages the semantics?
Anyone with data to describe, but…
…reuse others' work to promote interoperability
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5. authority / data-provider
RESTful web-services
RESTful web-services associate URIs with data-model entities:
Notice how well this maps to database, table, and row
This web-service returns everything about taxonomy id 9606 in XML or JSON format.
protocol
(http,https)
authority / data-provider
entity
identifier
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6. URIs: Universal Resource Identifiers
URIs need not actually represent a web-service
Pure identifiers, even if no machine or server.
Associate semantic properties with URIs
Literal values, or other URIs
protocol
(http,https)
authority / data-provider
entity
identifier
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7. RDF: Resource Description Framework
XML format for describing instances, and their semantic properties – triples!
(subject, predicate, object)
Subject: a URI identifying the resource (instance identifier).
Predicate: a URI indicates the relationship between Subject and Object (property identifier).
Object: a literal value or URI of another resource related to the Subject (property value).
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8. RDF: Resource Description Framework
Conceptually, this is either:
A really tall, thin table, containing the entire database
A graph of nodes (subjects, objects) and edges (predicates)
Regardless, still need a logical data model (at least in your head) to navigate the information.
Predicate
Subject
Object
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9. GlycoConjugate Ontology
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Matthew Campbell

10. Example Triples (TURTLE)
<
a gco:ReferencedProtein ;
gco:glycosylated_at < ... ;
gco:has_protein < ;
gco:has_saccharide_set < ; ... .
<
a gco:Glycosylation_site , faldo:region ;
gco:has_saccharide_set < ;
faldo:ExactPosition < .
<
a faldo:ExactPosition ;
gco:has_amino_acid < ;
faldo:position "153^^xsd:int" .
<
a gco:amino_acid ;
gco:amino_acid "Ser" .
Matthew Campbell
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11. RDF/XML <?xml version="1.0" encoding="utf-8" ?> <rdf:RDF
xmlns:rdf="
xmlns:rdfs="
xmlns:owl="
xmlns:oboInOwl="
xmlns:obo=" >
<rdf:Description rdf:about="
<rdf:type rdf:resource=" />
<rdfs:label rdf:datatype=" subtype N2 (Influenza A virus)</rdfs:label>
<rdfs:comment rdf:datatype=" Requested by=IEDB. Requested by=ImmPort.</rdfs:comment>
<rdfs:subClassOf rdf:resource=" />
<rdfs:subClassOf rdf:nodeID="b " />
<oboInOwl:hasExactSynonym rdf:datatype="
<oboInOwl:hasOBONamespace rdf:datatype="
<oboInOwl:id rdf:datatype="
<obo:IAO_ rdf:datatype=" neuraminidase (Influenza A virus) that is expressed on the surface of Influenza A virus and has similar antigenic properties, i.e., it will be neutralized by a similar set of antibodies. Example: UniProtKB:P06820.</obo:IAO_ >
</rdf:Description>
<rdf:Description rdf:nodeID="b ">
<rdf:type rdf:resource=" />
<owl:onProperty rdf:resource=" />
<owl:someValuesFrom rdf:resource=" />
</rdf:RDF>
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12. RDFS: RDF Schema
Data modeling vocabulary (ontology) for classes and properties
rdfs:Class, rdfs:subClassOf,
rdf:Property, rdfs:subPropertyOf,
rdfs:range, rdfs:domain,
rdfs:Resource, rdfs:Literal,
rdfs:Datatype, rdfs:type,
rdfs:comment, rdfs:label,
rdfs:seeAlso, rdfs:isDefinedBy
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13. Ontology Formal definition of terms and their conceptual definitions:
In particular, classes and their properties
OWL – Web Ontology Language
RDF document with specific classes and properties for defining ontologies
Public, facilitates data-use/reuse
Compare with:
Logical data-model for a relational database
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14. Triple-Stores
Database for storing RDF triples, and efficiently querying them
Constrain subject, predicate, and/or object
Triple-store query as web-service, or dump RDF/XML
Semi-structured, similar to document stores
From one extreme to the other…
Extreme "(de-)normalized form"
Ontology / logical data-model is crucial
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15. Federation / Federated Queries
Triple-stores can easily be concatenated:
…even virtually, with triples staying put.
However, this only makes sense if
Both triple-stores agree on classes, properties
Both triple-stores agree on URIs
NOTE: True for any data-integration project
Done right, federated queries of multiple triple-stores can be executed automatically
…across multiple independent triple-stores
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16. SPARQL SPARQL Protocol and RDF Query Language
SQL-like query language for triples
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17. SPARQL URI Namespaces: Result clause: Query pattern: Placeholders:
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18. SPARQL URI Namespaces: Result clause: Query pattern: Placeholders:
rdfs:type
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19. SPARQL
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20. SPARQL Not quite as expressive as SQL
…but provides a significant subset of its functionality
Properties must be present to be matched
Absence of property values is difficult to query
Primarily use "equality" clauses
Consequences for data-modeling strategies
Multi-values appear as multi-triples
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21. Exercise
Explore the SPARQL endpoint at Uniprot:
Lots of interesting queries here!
Come up with a hypothesis about how the triples are used to represent UniProt entries
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