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Oracle Database Semantic Technologies Tutorial

Oracle Semantic Technologies Agenda

•Introduction •Tutorial

Oracle Database Semantic Data Store

•A feature of Oracle Spatial 11g Option for Oracle Database 11g

Enterprise Edition

•Requires Partitioning and Advanced Compression Options •An open and persisted

RDF data model and analysis platform

for semantic applications •An RDF Data Model with inferencing (RDFS, OWL and user-defined rules) •Performs SQL-based access to triples and inferred data •Combines SQL query of relational data with RDF graphs and ontologies •Supports large graphs (billion+ triples) •Easily extensible by 3 rd party tools/apps

Only Oracle Database 11g...

•Scalability- Trillions of triples •Availability- tens of thousands of users •Security- protect sensitive business data •Performance- timely load, query & inference •Accessibility- to enterprise applications •Manageability- leverage IT resources =Oracle Database Strengths

Has an open, persistent, analytic semantic data

management platform

Oracle Customer Examples

•Enterprise Information Integration •Hutchinson 3G Austria •Large Public Dataset for Data Integration •Uniprot dataset at the Swiss Institute of Bioinformatics •Data Integration •Yale University •Stanford University •University of Cincinnati •Bio-surveillance •University of Texas at Houston •Re-use of Legacy Data •Pharmaceutical companies

Edit &

Transform

•RDF/OWL Data

Management

•SQL & SPARQL Query •Inferencing •Semantic Rules •Scalability &

Security

•Graph

Visualization

•Link Analysis •Statistical

Analysis

•Faceted Search •Pattern Discovery •Text Mining

Load, Query

& Inference

Applications &

Analysis

Semantic Data Management Workflow

Other Data

FormatsRSS, emailTransaction

Systems

Data

Sources

Unstructured

Content

•Entity

Extraction &

Transform

•Ontology

Engineering

•Categorization •Custom

Scripting

Oracle Spatial 11g Semantic Capabilities

•RDF/OWL data •Ontologies & rule basesRelational data Query

RDF/OWL

data and ontologies INFER STORE

Ontology-

Assisted

Query of

Enterprise Data

QUERY RDF/S User defined

Batch-

Load OWL Bulk- Load Incr. DML

Semantic Data Management Tasks

•Create a new Semantic Network •Create a RDF/OWL model •Load RDF/OWL data •Optionally create user-defined rulebases •Infer new RDF/OWL data •Query RDF/OWL data •Perform Ontology-assisted Query

Tutorial Agenda

•RDF Concepts •Using Oracle Database 11g as a Semantic

Data Store

•Jena Adaptor •System Metadata and Views •National Cancer Institute Ontology Case Study •Best Practices

RDF Concepts

•Originally created to encode metadata such as 'author', 'date', etc. for web resources. •It has become popular to relate things in the real-world such as people, places, concepts, schemas •The basic unit of information (fact) is represented as triple •Triples together form a graph, connecting pieces of data subject object predicate:John "32"^^xsd:decimal :age :Mary :friendOf

Triples

Each triple represents a statement of a relationship between the things denoted by the nodes that it links. Each triple has three parts:

1.A subject

2.An object

, and

3.A predicate

(also called a property ) that denotes a relationship. The direction of the arc is significant: it always points toward the object.

The nodes

of an RDF graph are its subjects and objects. URIs A Uniform Resource Identifier (URI), is a compact string of characters used to identify or name a resource . The main purpose of this identification is to enable interaction with representations of the resource over a network, typically the World Wide Web , using specific protocols . URIs are defined in schemes defining a specific syntax and associated protocols.

RDF Triples

•RDF triple represents a directed labeled edge •RDF triple setrepresents a directed labeled graph # uniprot:... rdf:type :Protein # other attributes

Hand_Fracture

Arm_Fracture

:subClassOf

RDF Data Model

•RDF terms (values) can be of three types •URIs<...> •Blank Nodes _:x •Literals "John", "2004-12-21T11:22:33-05:00"^^xsd:dateTime, "0010"^^xsd:integer •Positional constraints •Predicate must be a URI •Subject must be a URI or a blank node •Object must be a URI, blank node, or literal •Set property •Value-point based equivalence •URI

Ontology

A systematic arrangement of all of the important

categories of objects or concepts which exist in some field of discourse, showing the relations between them

The Gene Ontology project provides a controlled

vocabulary to describe gene and gene product attributes in any organism

The Protein Ontology (PO) provides a unified

vocabulary for capturing declarative knowledge about protein domain and to classify that knowledge to allow reasoning

Inferencing: Rules and Rulebases

Inferencingis the ability to make logical deductions based on rules A ruleis an object that can be applied to draw inferences from semantic data.

A rule consists of:

An IF side pattern for the

antecedents An optional filter condition that further restricts the subgraphs

A THEN side pattern for the

consequents For example, the rule that a chairperson of a conference is also a reviewer of the conference could be represented as follows: ('chairpersonRule', -- rule name '(?r :ChairPersonOf ?c)', -- IF side pattern

NULL, -- filter condition

'(?r :ReviewerOf ?c)', -- THEN side pattern

Rules Index

Contains precomputed triples

Inferred from model(s) and rulebase(s)

Mandatory when using SEM_MATCH (for each

rulebase-model combination)

Use SEM_APIS.CREATE_RULES_INDEX to

create a rules index

View under MDSYS is created (SEMI_rules-index-

name)BEGIN

SEM_APIS.CREATE_RULES_INDEX(

'rdfs_rix_family',quotesdbs_dbs14.pdfusesText_20
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