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ontop: A tutorial

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A tutorial on how to create mappings using ontop, how inference (OWL 2 QL and RDFS) plays a role answering SPARQL queries in ontop, and how ontop's support for on-the-fly SQL query translation enables …

A tutorial on how to create mappings using ontop, how inference (OWL 2 QL and RDFS) plays a role answering SPARQL queries in ontop, and how ontop's support for on-the-fly SQL query translation enables scenarios of semantic data access and data integration.

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  • Mappings are axioms that relate the data in a RDBMs to the vocabulary of the ontology (the classes and the properties), they “connect” the two vocabularies in a sense.In the ont-the-fly approach, the reasoner receives ontology and “mappings”, and answers the user queries by transforming these queries into queries over the source. The reasoner is “connnected to the source” hence, data is always fresh, and not duplicated.
  • The objetive of this section is to learn the very basics of using ontop. We will deal with an SQL database and connect it to Protégé using ontopPro. This will allow us to learn the mapping language, to get familiar with the ontopPro interface and with it’s querying capabilities.
  • Additionally, a browser window will open showing the “administration page for H2”.Note that the page is pointing to your local machine (192.168.0.100 is your own machine, also 127.0.0.1 and localhost). The page being displayed is the H2 administration console. Here you can create new databases, change security preferences of the server, change the “port” of the DB, etc.To create a new database, we simply introduce a connection string pointing to a non-existing database and click connect. In this case, the H2 server has no database, so any database name we use will create a new database which will persist even if we close the database, i.e., it will be available when we start the server again. The username and password will define the “credentials” for an administrator user. You can let them as the they are “username sa with no password” or change them as you wish.Now we use as connection string jdbc:h2:tcp://localhost/test, this will create a new database called test. Click connect.
  • This is the “console view” here you are in a “DB session” connected to the H2 server in localhost and database test. Currently this database has no tables, we are going to create a table where we are going to store the data for an ontology.
  • Cancert type-- false = non-small cell lung cancer (NSCLC), -- true = small cell lung cancer (SCLC)The database has one single table that stores lung cancer information for given patients. The type of cancer stored are NSCLC and SCLC. The stage of the cancer is also recorded. Type and stage are encoded with booleans and integers as follows:-- Stages for NSCLC-- 1 = Stage I-- 2 = Stage II-- 3 = Stage III-- 4 = Stage IIIa-- 5 = Stage IIIb-- 6 = Stage IV-- Stages for SCLC-- 7 = Limited-- 8 = Extensive -- STAGES OF NON-SMALL CELL LUNG CANCER-- -- Stage I: The cancer is located only in the lungs and has not spread to any lymph nodes.-- -- Stage II: The cancer is in the lung and nearby lymph nodes.-- -- Stage III: Cancer is found in the lung and in the lymph nodes in the middle of the chest, also described as locally advanced disease. Stage III has two subtypes:-- -- If the cancer has spread only to lymph nodes on the same side of the chest where the cancer started, it is called stage IIIA.-- If the cancer has spread to the lymph nodes on the opposite side of the chest, or above the collar bone, it is called stage IIIB.-- Stage IV: This is the most advanced stage of lung cancer, and is also described as advanced disease. This is when the cancer has spread to both lungs, to fluid in the area around the lungs, or to another part of the body, such as the liver or other organs.-- -- Small Cell Lung Cancer-- -- Small cell lung cancer accounts for the remaining 15 percent of lung cancers in the United States. Small cell lung cancer results from smoking even more so than non-small cell lung cancer, and grows more rapidly and spreads to other parts of the body earlier than non-small cell lung cancer. It is also more responsive to chemotherapy.-- -- Stages of Small Cell Lung Cancer-- -- Limited stage: In this stage, cancer is found on one side of the chest, involving just one part of the lung and nearby lymph nodes.-- -- Extensive stage: In this stage, cancer has spread to other regions of the chest or other parts of the body.
  • To create the database we execute the following SQL command. The results should be a new table with the structure we defined. You can get the commands from the file named patient-table1.sql to save some time.
  • To insert the data we run INSERT SQL commands. Now we add the two rows we had in the example:
  • To retrieve the data, we use SELECT queries. Note also that the H2 console also allows you to edit the data right there, add new rows and delete rows.
  • SQL is a very powerful query language that allows you to retrieve data in very selective ways, to do computations on the data, etc. etc. Moreover, SQL databases can do all this on top of very large datasets at very high speed. Traditional OWL reasoners can only handle limited amounts of data.
  • Here we are using the prefix : http://example.org/
  • If the connection says “OK” it means Protégé and ontop were able to connect to the H2 server that is running, and that it was able to see the “test” db that we just created.
  • When adding the class, note that you must add “terms/Patient” and not just “Patient” to get the correct URL. Remember that the base URI of the ontology is http://example.org/ creating the class “terms/Patient” will generate the right class URI. You can see the full URI of a class in the class tree by “hovering” over the class node.Note we are using upper case in this case
  • This dialog is allows you to create or edit mappings (to edit just double click an existing mapping). You should write the “target” and “source” of the mapping, as well as the ID for the mapping (any unique name to identify the mapping). You may also test your SQL query directly in the dialog.
  • The mapping appears in the mapping manager. Double clicking on it allows you to edit it.
  • Note that URI’s of the individuals are constructed as we described in the template of the mapping, just replacing the placeholders with the actual values from the DB.
  • The queryPREFIX : <http://example.org/>PREFIX terms: <http://example.org/terms/>SELECT * WHERE { ?p a terms:Patient .}
  • The result is no longer numeric ID’s in the database. The results are URI’s constructed in the way that you wrote in the mapping by replacing the “column references” with the actual values obtained from the database (the values in each row)
  • Remember to “synchronize” the reasoner, since changes to ontology or mappings are not automatically transferred to Quest.
  • Mappings :db1/{PATIENTID} a :terms/Patient ; :terms/id {PATIENTID} ; :terms/name {NAME} ; :terms/type {TYPE} ; :terms/stage {STAGE} .Remember to “synchronize” the reasoner, since changes to ontology or mappings are not automatically transferred to Quest.
  • With all the mappings in place, now we “refresh” the reasonerPREFIX : <http://example.org/>SELECT * WHERE { ?p a :Patient . ?p :id ?id . ?p :name ?name . ?p :stage ?stage . ?p :type ?type .}
  • URI templates are URI’s (short with prefix or long) with place holders with column names.
  • URI templates are URI’s (short with prefix or long) with place holders with column names.Note that constant values can be used anywhere, e.g., Michael_Jackson.
  • URI templates are URI’s (short with prefix or long) with place holders with column names.
  • URI templates are URI’s (short with prefix or long) with place holders with column names.Note that constant values can be used anywhere, e.g., Michael_Jackson.
  • URI templates are URI’s (short with prefix or long) with place holders with column names.Note that constant values can be used anywhere, e.g., Michael_Jackson.
  • URI templates are URI’s (short with prefix or long) with place holders with column names.
  • Neoplasm is the technical term for tumor, an abnormal growth of tissue. NSCLC – Non small cell lung cancer
  • TYPO: please remove the last slash after :db1/neoplasm/{PATIENTID}/, that is after :db1/neoplasm/{PATIENTID}
  • TYPO: please remove the last slash after :db1/neoplasm/{PATIENTID}/, that is after :db1/neoplasm/{PATIENTID}
  • TYPO: please remove the last slash after :db1/neoplasm/{PATIENTID}/, that is after :db1/neoplasm/{PATIENTID}
  • PREFIX : <http://example.org/>SELECT * WHERE { ?x a :Person ; :hasNeoplasm ?n . ?n ?p ?v }If we query the new model now we can obtain the information of each patient and their condition. More importantly, this information is not encoded in numeric values, dependent on the DB, but in the URIs of classes or individuals that have clear semantics.
  • To create the database use the commands located in the file patient-table2.sql. Do it as before, copy the commands from your text editor into the H2 console and execute them. The tables should appear in the DB
  • Add the new data properties :age and :ssn, then your basic mappings look like this
  • A solution to this which is available through ontop is
  • In the case of the lung cancer domain, we could express some of our knowledge as depicted in the picture. The result would be
  • After doing this and “synchronizing with the reasoner”, instead of having just data as now (switch to slide)

Transcript

  • 1. + ontop: A Tutorial Mariano Rodriguez Muro, Ph.D. Free University of Bozen-Bolzano Bolzano, Italy http://www.rodriguez-muro.com Protégé-OWL Short Course September 2-4, 2013 Vienna, Austria
  • 2. + Disclaimer License This work is licensed under the Creative Commons Attribution-Share Alike 3.0 License http://creativecommons.org/licenses/by-sa/3.0/ The material for this presentation is available at: https://www.dropbox.com/sh/q3aowgiq5dnco7n/as0QniGPKy
  • 3. + Who am I?  Researcher at: Free University of Bozen Bolzano, Bolzano, Italy From October at: IBM Watson Research Center  Research topics: OBDA, Efficient reasoning in OWL, Query rewriting, Data integration  Leader of the ontop project
  • 4. + Why are we here?  Data and ontologies  To get the basics of ontology based data access  To learn how to do it with ontop  To grasp some of the possible uses of the technology and hint to the resources available
  • 5. + Tutorial Overview  Part 1: Introduction  Quick Introduction to Ontology Based Data Access  Part 2: The basics  Creating an SQL database, creating simple (direct) mappings with ontopPro and querying.  Part 3: Modeling in OBDA  Creating mappings that reflect the domain  Part 4: Data Integration  Using ontop to query data from multiple sources  Part 5: Ontologies and ontop  Extending and using with domain knowledge (OWL)
  • 6. + Material  The tutorial is organized as a hands-on session. Try to perform the described tasks.  Most command/mappings/queries are in files in the “materials” folder, still, try to write them on your own  Material included (ontop-tutorial-viena13.zip)  README.txt is an index for the ZIP file  H2 Database (h2.zip)  .obda/.owl files (resulting mappings and ontologies for all examples)  .sql files (. SQL commands that create the tutorial DBs)
  • 7. + Part 1 Introduction
  • 8. + SQL DBs  Standard way to store LARGE volumes of data  Mature, Robust and FAST  Domain is structured as tables, data becomes rows in these tables.  Powerful query language (SQL) to retrieve this data.  Major companies developed SQL DBs for the last 30 years (IBM, Microsoft, Oracle) and even open source projects are now quite robust (MySQL, PostgreSQL).
  • 9. + OBDA and motivation  Ontology Based Data Access (OBDA) is an research are that focuses on accessing data through ontologies. –ontop-’s focus is on SQL DBs (RDBMs)  Benefits:  Flexible data model (OWL/RDF)  Flexible query language (OWL or SPARQL)  Inference  Speed, volume and features (by reusing SQL DBs)  Possible applications  Semantic Query Answering  Data integration  Semantic Search
  • 10. + Two approaches for OBDA Extract Transform Load (ETL) Reasoner Source Application TBox Inputs Data Code Data is transformed into OWL ABox assertions that are combined with OWL axioms and then given to a reasoner or query engine. Limitations: performance and memory.
  • 11. + Two approaches for OBDA On-the-fly Reasoner Source Application Ontology Mappings Input Mappings are axioms that relate the data in a RDBMs to the vocabulary of the ontology (the classes and the properties), they “connect” the two vocabularies in a sense. The input are ontology and mappings, the reasoner answers the queries by transforming them into queries over the source. The reasoner is connected to the source, data is not duplicated, is always up-to-date.
  • 12. +  ontop is a platform to query RDBMs through OWL/RDFS ontologies on-the-fly, using SPARQL. It's extremely fast and is packed with features  It‟s composed by 2 main components:  Quest. A reasoner/query engine that is able to answer SPARQL 1.0 queries, supports OWL 2 QL inference and a powerful mapping language. Can be run in Java applications or a stand-alone SPARQL server.  ontopPro. A plugin for Protégé 4 that provides a mapping editor, and that allows to use Quest directly from Protégé.  Today we will focus learning to use OBDA with ontopPro
  • 13. + Part 2 ontopPro: The basics SQL, Mappings, Queries
  • 14. + Overview  Flash recap of SQL DBs with H2  Using ontopPro  Connecting a DB with Protégé  Creating mappings  Querying  About mappings in ontop  About query answering in ontop
  • 15. + An SQL database, H2  A pure java SQL database  Easy to install  Just unzip the downloaded package, already in your USB stick h2- simple.zip  Easy to run, just run the scripts:  Open a terminal (in mac Terminal.app, in windows run cmd.exe)  Move to the H2 folder (e.g., cd h2)  Start H2 using the h2 scripts  sh h2.sh (in mac/linux)  h2w.bat (in windows)
  • 16. starting H2 with the terminal A yellow icon indicates the DB is running (right click for a context menu)
  • 17. jdbc:h2:tcp://localhost/test jdbc:h2:tcp: = protocol information locahost = server location test = database name
  • 18. + Creating the database  We‟ll create a table to store lung cancer information as follows: patientid name type stage 1 Mary false 2 2 John true 7 type is: • true for Non-Small Cell Lung Cancer (NSCLC) • false for Small Cell Lung Cancer (SCLC) stage is: • 1-6 for stage I,II,III,IIIa,IIIb,IV NSCLC • 7-8 for Limited,Extensive SCLC
  • 19. + Creating the table  To create the table (file patient-table1.sql): CREATE TABLE tbl_patient ( patientid INT NOT NULL PRIMARY KEY, name VARCHAR(40), type BOOLEAN, stage TINYINT )
  • 20. + Inserting the data  To insert the data: INSERT INTO tbl_patient (patientid,name,type,stage) VALUES (1,'Mary',false,2), (2,'John',true,7);
  • 21. + Retrieving data: SQL  To retrieve all data: SELECT * FROM TBL_PATIENT ;
  • 22. + Other relevant queries  To get all id‟s of patients with NSCLC SELECT patientid FROM TBL_PATIENT WHERE TYPE = false  To get all information about patients with NSCLC and stage 3 or above SELECT patientid FROM TBL_PATIENT WHERE TYPE = false AND stage >= 2
  • 23. + First ontop mapping  Objective  That each row generates the following OWL data  An OWL individual of the form: :db1/1  OWL assertions of the form: ClassAssertion( :Person :db1/1 ) DataPropertyAssertion ( :id :db1/1 “1”) DataPropertyAssertion ( :name :db1/1 “Mary”) DataPropertyAssertion ( :type :db1/1 “false”) DataPropertyAssertion ( :stage :db1/1 “2”)  That is, we define a vocabulary of Classes and Properties that we want to “populate” using the data from the database. A direct mapping
  • 24. + A direct mapping (cont.)  Seen graphically:  Things to note:  The OWL object is identified by an IRI  Values have OWL data types patientid name type stage 1 Mary false 2
  • 25. + Step 0: Starting Protégé+ontop  Unzip the protégé-ontop bundle from your material This is a Protégé 4.3 package that includes the ontop plugin  Run Protégé using the run.bat or run.sh scripts. That is, execute: cd Protege_4.3_ontopPro/ sh run.sh
  • 26. + Step 1: Defining the base URI  Define the ontology base URI: http://example.org/  Save the ontology  Close and re-open Protégé (Sorry this is due to a bug)  Enable the OBDA Model tab in Window -> Tabs
  • 27. + Step 2: Add the datasource  Using the OBDA model tab, we now need to define the connection parameters to our lung cancer database  Steps:  0. Switch to the OBDA model tab  1. Add a new data source (give it a name, e.g., LungCancerDB)  2. Define the connection parameters as follows:  Connection URL: jdbc:h2:tcp://localhost/test  Username: sa  Password: (leave empty)  Driver class: org.h2.Driver (choose it from the drop down menu)  3. Test the connection using the “Test Connection” button
  • 28. +  A “Connection is OK” means Protégé and ontop were able to connect to our H2 server and see the “tests” DB we just created. We are now ready to add the mappings for the DB.
  • 29. + Step 3: Create a mapping  Add the class: http://example.org/Patient  Switch to the “Mapping Manager” tab in the OBDA Model tab.  1. Select the LungCancerDB source  2. Add a mapping with ID “patient-map” target: :db1/{PATIENTID} a :Patient . source: SELECT * FROM TBL_PATIENT NOTE: use upper case
  • 30. + Adding a Mapping  Select the LungCancerDB from the drop down menu.  Click the “Create Button”
  • 31. +  The “Assertion template” a.k.a. “triple Template” tells ontop how to create URI‟s and Class and Property assertions using the data from the DB (from the SQL query)
  • 32. + The meaning of mappings  Mappings + DB data “entail (consequence)” OWL data, i.e., OWL ABox assertions.  These “entailed” data is accessible during query time (the on-the-fly approach) or can be imported into the OWL ontology (the ETL approach)
  • 33. + The meaning of mappings  ontop‟s main way to access data is on-the-fly, however, you can also do ETL using the “import data from mappings” function in the “ontop” menu.  Do it now and explore the result in the “individuals tab”, when done remember to delete these individuals. Use with care, you may run out of memory.
  • 34. + On-the-fly access to the DB  This is the main way to access data in ontop and its done by querying ontop with SPARQL.  The “query engine”/”reasoner” that comes with ontop is called “Quest”  Enable Quest in the “Reasoner” menu
  • 35. + On-the-fly access to the DB  Next, enable the “OBDA query” tab (ontop SPARQL) in the tabs menu
  • 36. + Querying with Quest In the OBDA Tab: 1. Write the SPARQL query 2. Click execute 3. Inspect the results
  • 37. TEMPLATE: :db1/{PATIENTID} a :Patient . The result is no longer numeric ID‟s in the database. The results are URI‟s constructed in the way that you wrote in the mapping by replacing the “column references” with the actual values obtained from the database (the values in each row)
  • 38. + The rest of the patient mappings  Add the following Data Properties:  :id  :name  :type  :stage target: :db1/{PATIENTID} :id {PATIENTID} . source: SELECT * FROM TBL_PATIENT target: :db1/{PATIENTID} :name {NAME} . source: SELECT * FROM TBL_PATIENT target: :db1/{PATIENTID} :type {TYPE} . source: SELECT * FROM TBL_PATIENT To complete the model we can add the following mappings one by one and “synchronize” the reasoner: target: :db1/{PATIENTID} :stage {STAGE} . source: SELECT * FROM TBL_PATIENT OR…
  • 39. + The rest of the patient mappings  Add the following Data Properties:  :id  :name  :type  :stage target: :db1/{PATIENTID} a :Patient ; :id {PATIENTID} ; :name {NAME} ; :type {TYPE} ; :stage {STAGE} . source: SELECT * FROM TBL_PATIENT Or, you can modify the original mapping as follows so that it generates multiple assertions at the same time: Don‟t forget to synchronize with the reasoner…
  • 40. + About Mappings  A mapping represents OWL assertions, one set of OWL assertions for each result row returned by the SQL query in the mapping. The assertions that the mapping represents are those obtained by replacing the place holders with the values from the DB.  Mappings are composed by:  Mapping IDs are arbitrary names for each mapping (choose something that allows you to identify the mapping)  The “Source” of the mapping is an SQL query that retrieves some of the data from the database.  The “Target” of the mapping is a form of “template” that indicates how to generate OWL Assertions (class or property) in a syntax very close to “Turtle” syntax for RDF.
  • 41. + Assertion Template Examples  Assertion templates are formed as a triple “subject predicate object”  The subject is always a URI, the object maybe another URI or an OWL value.  Class Assertions use rdf:type or a as predicate, and a URI as object (the class name) e.g., :db1/{id} rdf:type :Person <http://live.dbpedia.org/page/{name}> a :Writer  Object/Data Property Assertion have any URI as predicate (the property URI) and a URIs or OWL Value as object :db1/{id} :name {NAME} :db1/{id} :age {C1}^^xsd:string :db1/{id} :knows :db1/{id2} :db1/{id} :knows :Michael_Jackson
  • 42. + Practical Notes About Mappings  With ontopPro, mapping and data source definitions are stored in .obda files  .obda files are located in the same folder as the .owl ontology  They should be named as the .owl file  .obda files are text files, they may be edited and created manually, this can be more convenient in several cases, e.g., automatically generating large amounts of mappings, quick refactoring using regular expressions, etc.
  • 43. + About Query Answering in Ontop  ontop‟s query engine uses “query rewriting” techniques  Given a SPARQL query, ontop translates it into an SQL query using the mappings (and the ontology). You can get the SQL query generate by ontop using the context menu in the OBDA query tab.
  • 44. + About Query Answering in Ontop  Key features:  Volume: By relying on SQL DBs, the datasets that ontop can handle are in the GBs and TBs  Fast: ontop generates efficient SQL queries, that when combined with a fast SQL engine to provide answers in ms. Not all SQL queries are fast, most of the research and development efforts in ontop go towards generating FAST SQL queries  Possible drawbacks:  Maturity: SPARQL support in ontop is under development and many features are still missing  Know-how: SQL expertise will be required to obtain the best performance with large datasets
  • 45. + Part 3 Domain Modeling in OBDA
  • 46. +  The OWL vocabulary so far is a one-to-one reflection of the database, not very interesting or useful  We would like:  Application independent vocabulary  Vocabulary beyond the one explicit in the DB  Individuals and relations between them that reflect our understanding of the domain  For example… Application independent mappings
  • 47. + Redesigning our model Highlights: • The vocabulary is more domain oriented • No more values to encode types or stages. There is a new individual :db1/neoplasm/1 that stands for the cancer (tumor) of Mary and it is an instance of the class :NSCLC. There are URI‟s (individuals) that represent the stage of the cancer This model is closer to the formal model of the domain, independent from the DB. Later, this will allow us to easily integrate new data or domain information (e.g., an ontology).
  • 48. + Constructing the new model 1 Remove the old mappings and vocabulary, then:  Create the new vocabulary Object Properties: :hasStage, :hasNeoplasm Classes: :SCLC, :NSCLC  Add mappings for the new classes and properties as follows:
  • 49. + Basic mappings Basic mapping to generate the patient individual as well as the new “neoplasm” for that individual.
  • 50. + Classifying the neoplasm Now we classify the neoplasm individual using our knowledge of the database. We know that “false” in the table patient indicates a “Non Small Cell Lung Cancer”, so we classify the neoplasm as a :NSCLC. Similar for :SCLC
  • 51. + Associating a stage We associate the neoplasms of each patient to a stage. Note that the stage is no longer an arbitrary value, but a constant URI with clear meaning, independent from the DB.
  • 52. + Querying the new model In the new model now we can obtain the information of each patient and their condition through URIs of classes or individuals that have clear semantics, not DB dependent. We are using a “global vocabulary”. This will allow us to easily integrate new DBs and a domain ontology…
  • 53. + Part 4 Data integration Integration by alignment
  • 54. + Data integration in OBDA  Even if two databases contain data about the same domain, integrating the data is often problematic since the data may be represented in different ways  However, if proper modeling is used, integrating multiple data sources using OBDA may become simple:  Insert the data in the database (either as new tables, or through database federation)  Create the new mappings for the source such that they match the “global vocabulary”  Query using the global vocabulary as usual  Consider for example…
  • 55. + A different Lung Cancer DB  Consider a new lung cancer DB as follows (create it NOW in H2 using the commands in patient-table2.sql) ID name ssn age 1 Mariano SSN1 33 2 Morgan SSN2 45 ID stage 1 i ID stage 2 limited T_NAME T_NSCLC T_SCLC In this DB information is distributed in multiple tables. Moreover, the way in which meaning is encoded is different. In particular, • The type of cancer is separated by table • The stage of cancer is text (i,ii,iii,iiia,iiib,iv, limited, extensive) Moreover, the IDs of the two DBs overlap (ID 1 is a different patient here, not Mary) and ssn and age do not exist in the DB1
  • 56. + Basic mappings 2  The URI‟s for the new individuals differentiate the data sources (db2 vs. db1)  Being an instance of NSCLC and SCLC depends now on the table, not a column value You can find this mappings in lung- cancer3-4tables.obda
  • 57. + Stage mappings 2 The new mappings reflect what we know of this new data source.
  • 58. + The integration result Now, using a single SPARQL query, we can query both data sources independently of their structure; they have been aligned to a global view.
  • 59. + However  Multiple sources maybe have different properties  We cannot know before hand if we don‟t know the sources and the only thing you see is the ontology  This can be a BIG issue for the user of our integrating ontology, since many queries would be empty, e.g.: SELECT ?x ?y ?z WHERE { ?x a :Person ; :name “Mary” ; :ssn ?y ; :age ?z . } This query is empty because Mary is from DB1 and individuals from DB1 have no SSN or AGE. Similar problems arise with SQL DBs. But, in SPARQL we have DESCRIBE…
  • 60. + Flexible queries with SPARQL “Retrieve all information about individuals named „Mary‟ and all information about all conditions they have” PREFIX : <http://example.org/> DESCRIBE ?x WHERE { {?x :name ”Mary" .} UNION { ?y :name ”Mary"; :hasNeoplasm ?x } }
  • 61. + Flexible queries with SPARQL “Retrieve all information about individuals named „Mary‟ and all information about all conditions they have” PREFIX : <http://example.org/> DESCRIBE ?x WHERE { {?x :name ”Mariano" .} UNION { ?y :name ”Mariano"; :hasNeoplasm ?x } }
  • 62. + OBDA for Data Integration  Key features of on-the-fly OBDI (ontology-based data integration) with ontop:  Flexible: Mapping and ontology languages are powerful enough to accommodate most needs (consider that SQL allows even to transform the data, make calculations, etc.)  Dynamic: Changes in the data are automatically reflected during query answering. Through DB federation new databases can be incorporated easily  Possible drawbacks:  Performance: With large volumes of data (hundreds of thousands of rows) performance may suffer (depends on the DB engine, indexes, and other SQL related issues)
  • 63. + Data integration resources You may integrate any JDBC resource, here go some interesting options:  Teiid – can integrate different DB SQL dbs and other types of documents (XML, Excel, etc.) http://www.jboss.org/teiid/  Oracle database links – integrates Oracle DBs http://docs.oracle.com/cd/B28359_01/server.111/b28310/ds_conce pts002.htm  MySQL Federated tables – integrates MySQL dbs http://dev.mysql.com/doc/refman/5.0/en/federated-storage- engine.html  Excel as SQL – Integrates Excel spread sheets http://sourceforge.net/projects/xlsql/
  • 64. + Part 5 Ontologies and ontop
  • 65. + Domain knowledge  Up to know, we only have “explicit data”, however, combining data with domain knowledge (ontology) we can enrich our queries with “implicit data”. For example, that NSCLC is a kind of malignant tumor (neoplasm), that having a neoplasm is a kind of condition, etc.  This knowledge can be expressed using OWL axioms, which ontop will use during query answering.
  • 66. + Lung Cancer knowledge Terminology Knowledge (TBox) Plus: ObjectPropertyRange( :hasStage :LungCancerStage )
  • 67. + The result  After synchronization, all the implied information is available during query answering PREFIX : <http://example.org/> DESCRIBE ?x WHERE { {?x :name "Mariano"} UNION { ?y :name "Mariano" ; :hasNeoplasm ?x } }
  • 68. + Our data before (only explicit) Our existing data looked like this picture. With the new axioms, it now looks like this (next slide):
  • 69. + Our data now (implicit and explicit) Recall, in the on-the-fly approach all this information is available at query time but not really stored anywhere
  • 70. + Domain Knowledge  Large amounts of data “belong” in databases, i.e., it changes fast, application specific, large volumes, etc.). OBDA allows you to do keep it in the DB, but…  Some data in the domain does belong on the ontology side, i.e., static information, independent from the application. For example: ABox data
  • 71. + Domain Knowledge  This information usually given in the form of OWL individual assertions (ABox).  A unique feature of ontop is its ability to mix these two worlds, Allows to link virtual individuals to real individuals to achieve things like: ABox data We want to do this for all individuals in db1 and db2!
  • 72. + Hybrid ABoxes: How?  Add the individuals ABox assertions to your ontology (6 individuals and 6 ABox assertions)
  • 73. + Hybrid ABoxes: How?  Add the individuals ABox assertions to your ontology (6 individuals and 6 ABox assertions)  Add mappings that link your “virtual” individuals to the real ones
  • 74. + Hybrid ABoxes
  • 75. + Hybrid ABoxes PREFIX : <http://example.org/> DESCRIBE ?x WHERE { ?x :age ?age ; :recordIn [ a :ResearchCenter; :locatedIn [ :partOf :usa ] ] FILTER (?age > 40) }
  • 76. + Notes about reasoning in ontop  ontop can only understand OWL 2 QL axioms, that‟s:  subClassOf, subPropertyOf, equivalence  InverseOf  Domain and Range  Plus some limited forms of qualified existential restrictions  Any axiom that is not understood by ontop is ignored while reasoning  Reasoning is also done by means of query rewriting (no data moves from the database). Again, most of our research goes into generating efficient SQL.
  • 77. + Conclusions Pointers and Final thoughts
  • 78. + Other features of ontop  Mapping Assistant (OBDA model tab) - A view to help you generate custom mappings quickly  Mapping bootstrapping (OBDA menu) – automatically generate “direct” mappings (actually the first mappings we created can be generated automatically with this function)  Mapping materialization (OBDA menu) – generate OWL assertions from mappings with one click (import). Try it now, all the data will be available in the “Individuals tab” and you can now use it with any reasoner  SPARQL end-point – Use the mappings and ontology independently from Protégé, as a SPARQL server
  • 79. + Other features of ontop  OWLAPI and Sesame– Once you created the ontology and mappings, program your application with ontop and Java using these.  Command line tools – All previous features can be used directly from the command line with ontop scripts  R2RML mappings – Ontop now supports also R2RML mappings (http://www.w3.org/TR/r2rml/) the expressive power of these more or less the same, however our syntax is more user friendly ☺, use R2RML for mapping exchange  JDBC sources – Ontop can support any JDBC data source. This means not only RDBMs, but anything that can be seen as a RDBMs and queried with SQL, currently there are many wrappers that allow to do this for Excel files, XML documents, etc.
  • 80. + Disclaimer Although the code of ontop is evolving fast, there are several (Sept/13) important issues to consider when using ontop:  Datatypes many data types not supported yet, issues with dateTime  SPARQL Current target SPARQL 1.0 plus most features of 1.1 (no paths). From SPARQL 1.0 we still miss several built in functions  SQL issues Some issues with SQL and some DBs, e.g., problems getting DB metadata, issues with caps and quotes to qualify column names  SQL Optimization Performance good , but could be better. Many planned optimizations not yet implemented.  GUI/ontopPro Many bugs in the GUI (we focus on the DB aspect)
  • 81. + Additional material  Ontop‟s website  http://ontop.inf.unibz.it  Ontop‟s documentation  https://babbage.inf.unibz.it/trac/obdapublic/wiki  Ontop‟s source code  https://github.com/ontop/ontop/ Since August‟13 ontop is a open source (AGPL). Consider contributing!  Ontop‟s google group https://groups.google.com/d/forum/ontop4obda
  • 82. + Thank you