The document discusses Oracle Semantic Technologies for storing and querying RDF data. It provides an overview of how RDF data is stored and organized in Oracle databases using ID triples and URI mapping tables. It describes how the SEM_MATCH SQL function allows querying RDF data using a SPARQL-like syntax. Optimization techniques for SEM_MATCH queries include indexes and materialized views. The core entities in the Oracle Semantic Store include semantic networks, models, rulebases, and entailments. Functionality includes bulk loading, incremental loading, SPARQL querying, and built-in or user-defined inference rules.

1. ‫أكاديمية الحكومة اإللكترونية الفلسطينية‬
The Palestinian eGovernment Academy
www.egovacademy.ps
Tutorial II: Data Integration and Open Information Systems
Session 11
Oracle Semantic Technologies
Dr. Mustafa Jarrar
University of Birzeit
mjarrar@birzeit.edu
www.jarrar.info
PalGov © 2011 1

2. About
This tutorial is part of the PalGov project, funded by the TEMPUS IV program of the
Commission of the European Communities, grant agreement 511159-TEMPUS-1-
2010-1-PS-TEMPUS-JPHES. The project website: www.egovacademy.ps
Project Consortium:
Birzeit University, Palestine
University of Trento, Italy
(Coordinator )
Palestine Polytechnic University, Palestine Vrije Universiteit Brussel, Belgium
Palestine Technical University, Palestine
Université de Savoie, France
Ministry of Telecom and IT, Palestine
University of Namur, Belgium
Ministry of Interior, Palestine
TrueTrust, UK
Ministry of Local Government, Palestine
Coordinator:
Dr. Mustafa Jarrar
Birzeit University, P.O.Box 14- Birzeit, Palestine
Telfax:+972 2 2982935 mjarrar@birzeit.eduPalGov © 2011
2

3. © Copyright Notes
Everyone is encouraged to use this material, or part of it, but should
properly cite the project (logo and website), and the author of that part.
No part of this tutorial may be reproduced or modified in any form or by
any means, without prior written permission from the project, who have
the full copyrights on the material.
Attribution-NonCommercial-ShareAlike
CC-BY-NC-SA
This license lets others remix, tweak, and build upon your work non-
commercially, as long as they credit you and license their new creations
under the identical terms.
PalGov © 2011 3

4. Tutorial Map
Topic h
Intended Learning Objectives
Session 1: XML Basics and Namespaces 3
A: Knowledge and Understanding
Session 2: XML DTD‟s 3
2a1: Describe tree and graph data models.
Session 3: XML Schemas 3
2a2: Understand the notation of XML, RDF, RDFS, and OWL.
Session 4: Lab-XML Schemas 3
2a3: Demonstrate knowledge about querying techniques for data
models as SPARQL and XPath. Session 5: RDF and RDFs 3
2a4: Explain the concepts of identity management and Linked data. Session 6: Lab-RDF and RDFs 3
2a5: Demonstrate knowledge about Integration &fusion of Session 7: OWL (Ontology Web Language) 3
heterogeneous data. Session 8: Lab-OWL 3
B: Intellectual Skills Session 9: Lab-RDF Stores -Challenges and Solutions 3
2b1: Represent data using tree and graph data models (XML & Session 10: Lab-SPARQL 3
RDF). Session 11: Lab-Oracle Semantic Technology 3
2b2: Describe data semantics using RDFS and OWL. Session 12_1: The problem of Data Integration 1.5
2b3: Manage and query data represented in RDF, XML, OWL. Session 12_2: Architectural Solutions for the Integration Issues 1.5
2b4: Integrate and fuse heterogeneous data. Session 13_1: Data Schema Integration 1
C: Professional and Practical Skills Session 13_2: GAV and LAV Integration 1
2c1: Using Oracle Semantic Technology and/or Virtuoso to store Session 13_3: Data Integration and Fusion using RDF 1
and query RDF stores. Session 14: Lab-Data Integration and Fusion using RDF 3
D: General and Transferable Skills
2d1: Working with team. Session 15_1: Data Web and Linked Data 1.5
2d2: Presenting and defending ideas. Session 15_2: RDFa 1.5
2d3: Use of creativity and innovation in problem solving.
2d4: Develop communication skills and logical reasoning abilities. Session 16: Lab-RDFa 3
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5. Module ILOs
After completing this module students will be
able to:
- Using Oracle Semantics Technology to store and
query RDF stores.
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6. Introduction
Source: Oracle.com
• Oracle Semantic Technologies enables you to:
– store RDF data and ontologies,
– query RDF data,
– perform ontology-assisted query of relational data,
– use supplied or user-defined “inferencing”.
Inference Query
User-defined
OWL Subset
Query RDF/ Ontology-assisted
RDF/S
OWL data and query of relational
ontologies data
Store RDF/OWL Relational
Bulk Load data and data
ontologies
Incremental
Database
load & DML
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7. Querying RDF data using Oracle
• Oracle introduced an SQL-based scheme to query RDF
data.
• They introduced an SQL table function called
“SEM_MATCH” which is part of Oracle‟s Semantic
Technologies.
• SEM_MATCH takes a SPARQL-like syntax as arguments,
and returns a table of results that can be further queried
using SQL.
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8. How RDF Data is stored in Oracle.
• The physical organization of RDF data is a bit different
from its logical organization as a single <S,P,O> table.
RDF triples are stored after normalization in two tables:
– IdTriples(ModelID, subjectID, propertyID, objectID)
(triples in the identifier format)
– URIMap(UriID, UriValue)
(uri to identifier mapping)
• The core implementation of RDF_MATCH query translates
to a self-join query on IdTriples table.
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9. Query Optimization
• Optimization of SEM_MATCH queries on RDF data:
– Depends on Oracle‟s RDBMS optimizer to efficiently
speed up the execution of the query.
– Uses of a set of B-tree indexes and materialized views
(e.g. the subject-property matrix described previously).
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10. Architectural Overview
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11. Core Entities in Oracle Database Semantic Store
Source: Oracle.com
Sem. Network Dictionary and
data tables for storage and
management of asserted and
inferred RDF triples. OWL and
RDFS rule bases are preloaded.
Model: A model holds an RDF
graph (set of S<P<O triples).
Rulebase: A rulebase is a set of
rules used for inferencing.
Entailments: An entailment stores
triples derived via inferencing.
Application Table: Contains a
column of type sdo_rdf_triple_s,
associated with an RDF model, to
allow DML and access to RDF
triples, and storing ancillary
values.
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12. Core Functionality: Load / Query / Inference
Source: Oracle.com
• Load
– Bulk load
– Incremental load
• Query and DML
– SPARQL
(from Java/endpoint/Oracle)
• Inference
– Native support for OWL 2 RL,
SNOMED (OWL 2 EL subset),
OWLprime, OWLSIF, RDFS++.
– Named Graph Local Inference
– User-defined rules
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13. Architectural Overview: Interfaces
Note that there are three interfaces for Oracle
Semantic Technologies:
• SQL-based (SQL and PL/SQL)
• Java-based:
– Jena (Using Jena adapter from Oracle).
– Sesame (Using Jena adapter from Oracle).
• SPARQL Endpoints:
– Joseki
– OpenRDF Workbench
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14. Architectural Overview
Source: Oracle.com
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15. Installation and Configuration of Oracle Database
Semantic Technologies
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16. Installation and Configuration (1)
• Load the PL/SQL packages and jar file
– cd $ORACLE_HOME/md/admin
– As sysdba
– SQL> @catsem
• Create a tablespace for semantic network
create bigfile tablespace semts
datafile '?/dbs/semts01.dat' size 512M reuse
autoextend on next 512M maxsize unlimited
extent management local
segment space management auto;
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17. Installation and Configuration (2)
• Create a temporary tablespace
create bigfile temporary tablespace semtmpts
tempfile ‘?/dbs/semtmpts.dat'
size 512M reuse
autoextend on next 512M maxsize unlimited
EXTENT MANAGEMENT LOCAL;
ALTER DATABASE DEFAULT TEMPORARY TABLESPACE semtmpts;
• Create an undo tablespace
CREATE bigfile UNDO TABLESPACE semundots
DATAFILE ‘?/dbs/semundots.dat' SIZE 512M REUSE
AUTOEXTEND ON next 512M maxsize unlimited
EXTENT MANAGEMENT LOCAL ;
ALTER SYSTEM SET UNDO_TABLESPACE=semundots;
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18. Installation and Configuration (3)
• Create a semantic network to enable semantic data
management:
– As sysdba
– SQL> exec sem_apis.create_sem_network(„semts‟);
• Create Semantic Model
– As scott (or other)
– SQL> create table test_tpl(id number, triple sdo_rdf_triple_s);
– SQL> exec sem_apis.create_sem_model(„test‟,‟test_tpl‟,‟triple‟);
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19. Loading RDF Triples
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20. Loading Semantic Data: APIs
• Incremental DMLs (small number of changes)
• SQL: Insert Recommended for
very small number
• SQL: Delete of triples
• Java API (Jena): GraphOracleSem.add, delete
• Java API (Sesame): OracleSailConnection.addStatement,
removeStatements
Recommended for
very large number
of triples
• Bulk Loader (large number of changes)
• PL/SQL: sem_apis.bulk_load_from_staging_table(…)
• Java API (Jena): OracleBulkUpdateHandler.addInBulk(…),
prepareBulk
• Java API (Sesame): OracleBulkUpdateHandler.addInBulk,
prepareBulk ...
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21. PL/SQL Bulk Loader
• STEP 1: Load data into Staging Table using SQL*Loader:
(a) Create a staging table:
CREATE TABLE stage_table
(
RDF$STC_sub varchar2(4000) not null,
RDF$STC_pred varchar2(4000) not null,
RDF$STC_obj varchar2(4000) not null,
RDF$STC_sub_ext varchar2(64),
RDF$STC_pred_ext varchar2(64),
RDF$STC_obj_ext varchar2(64),
RDF$STC_canon_ext varchar2(64)
) COMPRESS Tablespace TS_Name;
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22. PL/SQL Bulk Loader
• STEP 1: Load data into Staging Table using SQL*Loader:
(b) Load into Staging Table
sqlldr userid=testuser/testuser
control=bulkload.ctl data=dblp.nt
direct=true skip=0 load=6000000
discardmax=0 bad=d0.bad discard=d0.rej
log=d0.log errors=100000000
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23. PL/SQL Bulk Loader
• STEP 1: Load data into Staging Table using SQL*Loader:
(b) Load into Staging Table (from cmd)
sqlldr userid=testuser/testuser
control=bulkload.ctl data=dblp.nt
direct=true skip=0 load=6000000
discardmax=0 bad=d0.bad discard=d0.rej
log=d0.log errors=100000000
Control file where Maximum number of Input Data (the path
we specify the name rows to bulk-load. to the input data
of the staging table Delete to remove file)
in the DB. limitations.
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24. PL/SQL Bulk Loader
• STEP 2: Create a semantic model and run bulk load from
staging table API:
– Create SEM Model (if not created already):
CREATE TABLE myrdf_tpl (id number, triple
SDO_RDF_TRIPLE_S) COMPRESS nologging tablespace
semts;
exec sem_apis.create_sem_model(‘myrdf',‘myrdf_tpl',
'triple');
– Bulk Load:
grant select on stage_table to mdsys;
grant insert on myrdf_tpl to mdsys;
exec sem_apis.bulk_load_from_staging_table(‘myrdf’,
‘scott‘, stage_table‘, flags=>’PARALLEL_CREATE_INDEX
PARALLEL=4');
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25. After Data is loaded
• Check number of triples in the model and application table
– select count(1) from mdsys.rdfm_<ModelName>;
– select count(1) from <AppTable>;
• Analyze the semantic model if there is enough change to
the model
– exec sem_apis.analyze_model(‘<ModelName>’);
• Analyze the semantic network if there is enough change to
the whole network
– exec sem_perf.gather_stats(true, 4); -- just on value$
-- table
– exec sem_perf.gather_stats(false, 4); -- whole network
• Start Querying
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26. Querying RDF Data using SEM_MATCH
Source: Oracle.com
• SPARQL Query Architecture
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27. SEM_MATCH: Adding SPARQL to SQL
Source: Oracle.com
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28. SEM_MATCH: Adding SPARQL to SQL
Source: Oracle.com
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29. SEM_MATCH: Adding SPARQL to SQL
Source: Oracle.com
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30. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
- The query attribute is required.
rulebases SEM_RULEBASES, is, each can be a null
- The other attributes are optional (that
value).
aliases query attribute is a string literal (or concatenation of
- The SEM_ALIASES,
string literals) with one or more triple patterns, usually
filter VARCHAR2,
containing variables.
index_status VARCHAR2,
EXAMPLE:
‘SELECT ?directorName
options VARCHAR2 ) RETURN ANYDATASET;
WHERE{
:M1 :directedBy ?director .
?director :name ?directorName
}’
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31. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
rulebases SEM_RULEBASES,
- The models attribute identifies the model or models to use.
aliases SEM_ALIASES, which has the following
- Its data type is SEM_MODELS,
definition: TABLE OF VARCHAR2(25).
filter VARCHAR2,virtual model, specify only the name of
- If you are querying a
the virtual model and no other models.
index_status VARCHAR2,
- Name of the model:
SEM_Models (‘ model_name '),
options VARCHAR2 ) RETURN ANYDATASET;
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32. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
rulebases SEM_RULEBASES,
aliases rulebases attribute identifies one or more rulebases
- The SEM_ALIASES,
whose rules are to be applied to the query.
filter you are querying a virtual model, this attribute must be
- If VARCHAR2,
null.
index_status VARCHAR2,
- A rulebase is an object that can contain rules, and a rule is
optionsobject that can be applied to draw inferences from
an VARCHAR2 ) RETURN ANYDATASET;
semantic data.
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33. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
rulebases SEM_RULEBASES,
aliases SEM_ALIASES, ff a m i l y _r brulebase. This ruleasays
- EXAMPLE: creates a rulebase named
named g r a n d p a r e n t _r u l e into the a m i l y _r b
, and then inserts rule
filter VARCHAR2, of a child who is the parent of a child, that
that if a person is the parent
person is a grandparent of.
index_statusP IVARCHAR2, E ( ' f a m i l y _r b ' ) ;
E X E C U T E S E M _A S . C R E A T E _R U L E B A S
I N S E R T I N T O m d s y s . s e m r _f a m i l y _r b V A L U E S (
optionsn dVARCHAR2 ) RETURN ANYDATASET;
' g r a p a r e n t _r u l e ' ,
'(?x :parentOf ?y) (?y :parentOf ?z)',
NULL,
'(?x :grandParentOf ?z)',
S E M _A L I A S E S ( S E M _A L I A S ( ' ' , ' h t t p : / / w w w . e x a m p l e . o r g / f a m i l y / '
)));
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34. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
rulebases SEM_RULEBASES,
aliases SEM_ALIASES,
filter VARCHAR2, one or more namespaces, in addition to the
- The aliases attribute identifies
default namespaces, to be used for expansion of qualified names in the
index_status VARCHAR2,
query pattern.
- The following default namespaces are used:
options a rVARCHAR2 m l)s .RETURN/ r ANYDATASET;
('or df', 'http://x n oracle.com df/')
( ' o r a g e o' , ' h t t p : / / x m l n s . o r a c l e . c o m /rdf/ geo/' )
('owl', 'http://www.w3.org/2002/07/owl#')
('rdf', 'http://www.w3.org/1999/02/22-rdf-syntax-ns#')
('rdfs', 'http://www.w3.org/2000/01/rdf-schema#')
('xsd', 'http://www.w3.org/2001/XMLSchema#')
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35. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
rulebases SEM_RULEBASES,
aliases SEM_ALIASES,
filter VARCHAR2,
index_status VARCHAR2,
-The filter attribute identifies any additional selection criteria.
options VARCHAR2 ) should be a string in the form of a
- If this attribute is not null, it RETURN ANYDATASET;
WHERE clause without the WHERE keyword.
- For example: '(h >= 6)' to limit the result to cases where the height
of the grandfather's grandchild is 6 or greater
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36. SEM_MATCH Table Function Arguments
SEM_MATCH( index_status attribute lets you query semantic data even
- The
when the relevant entailment does not have a valid status.
query- VARCHAR2, the query returns an error if the entailment
If this attribute is null,
modelsdoes not have a valid status. If this attribute is not null, it must be
SEM_MODELS,
the string INCOMPLETE or INVALID.
rulebases SEM_RULEBASES, precomputed triples that
- An Entailment is an object containing
can be inferred from applying a specified set of rulebases to a
aliases SEM_ALIASES,
specified set of models.
filter VARCHAR2,
index_status VARCHAR2,
options VARCHAR2 ) RETURN ANYDATASET;
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37. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
rulebases SEM_RULEBASES,
aliases options attribute identifies options that can affect
- The SEM_ALIASES,
the results of queries.
filter VARCHAR2, as keyword-value pairs.
- Options are expressed
index_status VARCHAR2,
options VARCHAR2 ) RETURN ANYDATASET;
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38. SEM_MATCH Table Function Arguments
SEM_MATCH(
query VARCHAR2,
models SEM_MODELS,
- For more details about using the SEM_MATCH
rulebases SEM_RULEBASES, consult
Function and the its different arguments,
Oracle Semantic Technologies Developer’s Guide:
aliases SEM_ALIASES,
http://www.oracle.com/technetwork/database/options/semantic
-tech/documentation-087054.html
filter VARCHAR2,
index_status VARCHAR2,
SEM_MATCH DOSUMENTATION:
http://download.oracle.com/docs/cd/E11882_01/appdev.112/e25609/sdo_rdf_concepts.htm#CHDJACII
options VARCHAR2 ) RETURN ANYDATASET;
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39. Examples
2007
Sicko
Michael Moore
directedBy
M1 D1 Emmy Awards
P1 1995
M2 2009
Capitalism
C1 USA
1995
Washington DC
directedBy
M3 D2
actedIn P2 Oscars
Brave Heart Mel Gibson 1996
2007 Nadine Labaki
directedBy
M4 D3 C2 Lebanon
actedIn
Beirut
Caramel location name
P3 Stockholm Festival
Exercises: C3
Sweden 2007
(1) What is the name of director D3? Stockholm
(2) What is the name of the director of the movie M1?
(3) List all the movies who have directors from the USA and their directors.
(4) List all the names of the directors from Lebanon who have won prizes and the
prizes they have won. PalGov © 2011 39

40. Examples
• Q1: What is the name of director D3?
Select directorName
From
Table(
SEM_MATCH(
‘SELECT ?directorName
WHERE {:D3 :name ?directorName}’,
SEM_MODELS(‘movies_model’), null, null, null,
null, null)
)
);
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41. Examples
• Q2: What is the name of the director of the movie M1?
Select directorName
From
Table(
SEM_MATCH(
‘SELECT ?directorName
WHERE{
:M1 :directedBy ?director .
?director :name ?directorName}’,
SEM_MODELS(‘movies_model’), null, null, null,
null, null)
)
);
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42. Examples
• Q3: List all the movies who have directors from the USA
and their directors.
Select movie, director
From
Table(
SEM_MATCH(
‘Select ?movie ?director
Where {?movie :directedBy ?director.
?director :country ?country.
?country :name ‘USA’}’ ,
SEM_MODELS(‘movies_model’), null, null, null,
null, null)
)
); PalGov © 2011 42

43. Examples
• Q4: List all the names of the directors from Lebanon who
have won prizes and the prizes they have won.
Select directorName, prize
From
Table(
SEM_MATCH(
‘Select ?directorName ?prize
Where { ?director :name ?directorName.
?director :country ?c.
?c :name ‘Lebanon’.
?director :hasWonPrizeIn ?prize}’,
SEM_MODELS(‘movies_model’), null, null, null,
null, null)
)); PalGov © 2011 43

44. Data Integration and Open Information Systems (Tutorial II)
The Palestinian e-Government Academy
January, 2012
Tutorial II: Data Integration and Open Information Systems
Practical Session
PalGov © 2011 44

45. Practical Session
PART1: Given the previous movies example and the accompanying four
queries, do the following:
(1) Write the data graph using any suitable RDF syntax (N3, or Turtle). Note:
Avoid using XML syntax as it might need additional effort to bulk-load.
(2) Bulk Load your RDF file into Oracle.
(3) Write the four queries accompanying the example using the SEM_MATCH
function and execute them over the bulk-loaded file.
PART2: Given the RDF graph of Practical Session I and II (also included in the
next slide), do the following:
(1) Write the data graph using any suitable RDF syntax (N3, or Turtle) and bulk-
load it into Oracle.
(2) Write the following queries using the SEM_MATCH function and execute them
over the bulk-loaded file:
• List all the authors born in a country which has the name Palestine.
• List the names of all authors with the name of their affiliation who are born in a
country whose capital‟s population is14M. Note that the author must have an
affiliation.
• List the names of all books whose authors are born in Lebanon along with the name
of the author. PalGov © 2011 45

46. Practical Session
Palestine
7.6K
Said
Capital Name
CN1 CA1 Jerusalem
AU1
BK1
CU Colombia University
Author Viswanathan 14.0M
BK2 AU2
India
Capital Name New Delhi
Wamadat CN2 CA2
Name
Author Naima
BK3 AU3 Lebanon
2.0M
The Prophet Gibran
CN3 CA3
BK4 Beirut
Author
AU4
This data graph is about books. It talks about four books (BK1-BK4).
Information recorded about a book includes data such as; its author,
affiliation, country of birth including its capital and the population of
its capital.
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47. Practical Session - Instructions
• Each student should work alone.
• In part 2 of this practical session, the student is strongly recommended
to write two additional queries, execute them on the data graph, and
hand them along with the required queries.
• In part 2 of this practical session, the student is encouraged to compare
the results of the queries with those from Practical Session I and II.
• Each student must expect to present and discuss his/her queries at
class and compare them with the work of other students.
• The final delivery should include for every part of the practical session:
(i) A link to the RDF file, (ii) a snapshot of the table where the file was
bulk-loaded, (iii) A snapshot of every query and its results. These must
all be delivered in a report form in PDF format.
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48. References
• http://www.oracle.com
• Anton Deik, Bilal Faraj, Ala Hawash, Mustafa Jarrar: Towards Query
Optimization for the Data Web - Two Disk-Based algorithms: Trace
Equivalence and Bisimilarity.
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49. Thank you!
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