Ontop: Answering SPARQL Queries over Relational Databases

Ontop: Answering SPARQL Queries over Relational Databases
Guohui Xiao
Faculty of Computer Science, Free University of Bozen-Bolzano, Italy
Free University of Bozen-Bolzano
February 12, 2016
Stanford University, CA, USA

Introduction Overview of Ontop SPARQL Query Answering in Ontop Use Cases Recent Progresses and Future
About Me
• Guohui Xiao, PhD
• Assistant Professor at KRDB Research Centre for Knowledge and Data,
Free University of Bozen-Bolzano, Italy
• Educations
PhD in Computer Science, Vienna University of Technology, Austria
MSc and BSc in Mathematics, Peking University, China
• Research interests:
Artiﬁcial intelligence, Knowledge representation
Description logics, Ontology, Semantic Web
Ontology-based Data Access
Implementation and Optimization of reasoning systems
• Ontop team leader
• Current project: Optique (Scalable End-user Access to Big Data), EU FP7
G. Xiao (FUB) Ontop: Answering SPARQL Queries over Relational Databases 1/56

Outline
1 Introduction
2 Overview of Ontop
3 SPARQL Query Answering in Ontop
4 Use Cases
5 Recent Progresses and Future

Outline
1 Introduction
2 Overview of Ontop
4 Use Cases

We are Living in the Era of Big Data
Data Never
Sleeps 2.0

The Problem: information access

The Problem: information access
How to formulate the right question
to obtain the right answer
in the ocean of Big Data.

How much time is spent searching for data?
Engineers in industry spend a signiﬁcant amount of their time searching
for data that they require for their core tasks.
For example, in the oil&gas industry, 30–70% of engineers’ time is spent
looking for data and assessing its quality (Crompton, 2008).

Example: Statoil Exploration
Experts in geology and geophysics develop
stratigraphic models of unexplored areas on
the basis of data acquired from previous
operations at nearby locations.

Example: Statoil Exploration
Experts in geology and geophysics develop
stratigraphic models of unexplored areas on
the basis of data acquired from previous
operations at nearby locations.
Facts:
• 1,000 TB of relational data
• using diverse schemata
• spread over 2,000 tables, over multiple individual data bases
Data Access for Exploration:
• 900 experts in Statoil Exploration.

How much time/money is spent searching for data?
A user query at Statoil
Show all norwegian wellbores with some additional attributes (wellbore id,
.....................). Limit to all wellbores with ... and show attributes like
............................................... Limit to all wellbores with ... in .................
and show key attributes in a table. After connecting to ... we could for instance
limit further to cores in ... with ...... and where it is larger than a given value,
for instance ..... We could also ﬁnd out whether there are cores in ..... which are
not stored in .... (based on .....) and where there could be .......... value. Some
of the missing data we possibly own, other not.

SELECT [...]
FROM
db_name.table1 table1,
db_name.table2 table2a,
db_name.table2 table2b,
db_name.table3 table3c,
db_name.table3 table3d,
db_name.table4 table4e,
db_name.table4 table4f,
db_name.table16 table16
WHERE [...]
table2a.attr1=‘keyword’ AND
table3a.attr2=table10c.attr1 AND
table3a.attr6=table6a.attr3 AND
table4a.attr10 IN (‘keyword’) AND
table5a.kinds=table4a.attr13 AND
table5b.kinds=table4c.attr74 AND
table5b.name=‘keyword’ AND
(table6a.attr19=table10c.attr17 OR
(table6a.attr2 IS NULL AND
table10c.attr4 IS NULL)) AND
table6a.attr14=table5b.attr14 AND
(table6b.attr14=table10c.attr8 OR
(table6b.attr4 IS NULL AND
table6b.attr19=table5a.attr55 AND
table6b.attr2=‘keyword’ AND
table7a.attr17=table15.attr19 AND
table8.attr19=table7a.attr80 AND
table8.attr19=table13.attr20 AND
table8.attr4=‘keyword’ AND
table3b.attr19=table10c.attr18 AND
table3b.attr22=table12.attr63 AND
table10a.attr16=table4d.attr11 AND
table4c.attr99=‘keyword’ AND
table2b.attr9 IN (‘keyword’) AND
table2b.attr2 LIKE ‘keyword’% AND
table12.attr9 IN (‘keyword’) AND
table3c.attr13=table10c.attr1 AND
table3c.attr10=table6b.attr20 AND
table10b.attr11=table7b.attr8 AND
table13.attr1=table2b.attr10 AND
table13.attr20=’‘keyword’’ AND
table3d.attr49=table12.attr18 AND
table3d.attr18=table10c.attr11 AND
table3d.attr14=‘keyword’ AND
table4d.attr17 IN (‘keyword’) AND
table4e.attr34 IN (‘keyword’) AND
table4f.attr89=table5b.attr7 AND
table4f.attr45 IN (‘keyword’) AND
table4f.attr1=‘keyword’ AND
table10c.attr2=table4e.attr19 AND
(table10c.attr78=table12.attr56 OR
(table10c.attr55 IS NULL AND
table12.attr17 IS NULL))

SELECT [...]
FROM
db_name.table4 table4e,
db_name.table4 table4f,
db_name.table16 table16
WHERE [...]
table5a.kinds=table4a.attr13 AND
table5b.kinds=table4c.attr74 AND
table5b.name=‘keyword’ AND
(table6a.attr19=table10c.attr17 OR
(table6a.attr2 IS NULL AND
(table6b.attr14=table10c.attr8 OR
(table6b.attr4 IS NULL AND
table7a.attr17=table15.attr19 AND
table3b.attr19=table10c.attr18 AND
table10a.attr16=table4d.attr11 AND
table2b.attr9 IN (‘keyword’) AND
table2b.attr2 LIKE ‘keyword’% AND
table12.attr9 IN (‘keyword’) AND
table3c.attr13=table10c.attr1 AND
table3c.attr10=table6b.attr20 AND
table13.attr20=’‘keyword’’ AND
table3d.attr49=table12.attr18 AND
table3d.attr18=table10c.attr11 AND
table3d.attr14=‘keyword’ AND
table4f.attr89=table5b.attr7 AND
table4f.attr45 IN (‘keyword’) AND
table4f.attr1=‘keyword’ AND
table10c.attr2=table4e.attr19 AND
(table10c.attr78=table12.attr56 OR
(table10c.attr55 IS NULL AND
table12.attr17 IS NULL))
At Statoil, it takes up to 4 days to formulate a query in SQL.
Statoil loses up to 50.000.000e per year because of this!!

Challenges Accessing Big Data
This is what happens:

Need for Abstraction
We need to facilitate access to Data
• by abstracting away from how the data is stored, and
• by making use of high level views on the data, so called ontologies.

Ontology Based Data Access Framework
. . .
. . .
. . .
. . .
ONTOLOGY
=
global vocabulary
+
conceptual view
DATA SOURCES
external and
heterogeneous
MAPPINGS
how to populate
the ontology
query
result
Logical transparency in accessing data:
• does not know where and how data is stored;
• can only see a conceptual view of data.

Outline
1 Introduction
2 Overview of Ontop
4 Use Cases

Ontop
• Is a platform to query databases through ontologies, relying on semantic
technologies.
• Compliant with the standards of the W3C.
• Supports all major relational DBs (Oracle, DB2, Postgres, MySQL, etc.).
• Open-source and released under Apache license.
• Development of Ontop:
development started 6 years ago
already well established:
• +200 topics in the mail list
• +2300 downloads in last 10 months
currently being developed in the context of the EU project Optique

Architecture of Ontop
Ontop SPARQL Query Answering Engine (Quest)
OWL-API Sesame Storage And Inference Layer (SAIL) API
R2RML API
OWL-API
(OWL Parser)
Sesame API
(SPARQL Parser)
JDBC
Protege
Optique
Platform
Sesame Workbench &
SPARQL Endpoint
Application
Layer
API
Layer
Ontop
Core
Inputs Relational
Databases
R2RML
Mappings
OWL 2 QL
Ontologies
SPARQL
Queries
Figure: Architecture of the Ontop system

Databases
Ontop supports standard relational database engines via JDBC.
• commercial databases: DB2, Oracle, MS SQL Server
• open-source databases: PostgreSQL, MySQL, H2, HSQL
• federated databases (e.g., Teiid1
or Exareme2
) to support multiple data sources
(e.g., relational databases, XML, CSV, and Web Services).
1
http://teiid.jboss.org
2
http://www.exareme.org

Example: Hospital Database
Table: tbl patient
pid name type stage
1 ’Mary’ false 4
2 ’John’ true 1
types:
• false for Non-Small Cell Lung Carcinoma (NSCLC)
• true for Small Cell Lung Carcinoma (SCLC),.
Stage
• NSCLC: 1–6 for stages I, II, III, IIIa, IIIb, and IV, respectively;
• SCLC: 1 and 2 for stages Limited and Extensive, respectively.

Ontology
• Ontop uses RDFS and OWL 2 QL as ontology languages.
• OWL 2 QL is based on the DL-Lite family of lightweight description logics, which
guarantees FO-rewritability
Example
:NSCLC rdfs:subClassOf :LungCancer .
:SCLC rdfs:subClassOf :LungCancer .
:LungCancer rdfs:subClassOf :Neoplasm .
:hasNeoplasm rdfs:domain :Patient .
:hasNeoplasm rdfs:range :Neoplasm .
:hasName a owl:DatatypeProperty .
:hasStage a owl:ObjectProperty .

Mappings
Ontop supports two mapping languages:
• W3C RDB2RDF mapping language R2RML
• Ontop native mapping language
Example (Mappings in Ontop native mapping language)
:db1/{pid} a :Patient .
← SELECT pid FROM tbl patient
:db1/neoplasm/{pid} a :NSCLC .
WHERE type = false
:db1/neoplasm/{pid} a :SCLC .
← SELECT pid FROM tbl patient WHERE type = true
:db1/{pid} :hasName {name} .
← SELECT pid, name FROM tbl patient
:db1/{pid} :hasNeoplasm :db1/neoplasm/{pid} .
:db1/neoplasm/{pid} :hasStage :stage-IIIa .
← SELECT pid FROM tbl patient WHERE stage = 4 and type = false

Queries
• Ontop supports essentially all features of SPARQL 1.0 as well as the OWL 2 QL
entailment regime of SPARQL 1.1.
• Implementation of other features of SPARQL 1.1 (e.g., aggregates, property path
queries, negation) is working in progress.
The following SPARQL query retrieves all the names of all patients who have a
neoplasm (tumor) at stage IIIa.
SELECT ?name WHERE {
?p a :Patient ;
:hasName ?name ;
:hasNeoplasm ?tumor .
?tumor a :Neoplasm ;
:hasStage :stage -IIIa . }

Ontop Core API
• The core of Ontop is the SPARQL query answering engine Quest.
• We will explain the details in the next section.

API layer of Ontop
System developers can use Ontop as a Java library
• OWL API is a reference implementation for creating, manipulating, and serializing
OWL ontologies. We extended the OWLReasoner Java interface to support
SPARQL query answering.
• Sesame is a de-facto standard framework for processing RDF data. Ontop
implements the Sesame Storage And Inference Layer (SAIL) API supporting
inferencing and querying over relational databases.
• Available as Maven artifacts from central repository.

Application Layer of Ontop
• Command line interface
• Prot´eg´e plugin
• Sesame Workbench and SPARQL Endpoint
• Optique Platform
• Stardog

Ontop Protégé plugin
The Ontop Protégé plugin provides a graphical interface for:
• editing mappings
• executing SPARQL queries
• checking (in)consistency of the ontology
• bootstrapping ontologies and mappings from the database
• importing and exporting R2RML mappings
• materializing RDF triples, etc.

Mapping Editor in Prot´eg´e

SPARQL query answering in Prot´eg´e

Ontop plugin available from Prot´eg´e plugin repository

Sesame workbench and SPARQL endpoint
• Sesame OpenRDF Workbench is a web application for administrating Sesame
repositories.
• We extended the Workbench to create and manage Ontop repositories.
• Such repositories can then be used as standard HTTP SPARQL endpoints.
• Currently Ontop only supports Sesame v2, we are working on supporting v4.

Screenshot of the Ontop Sesame Workbench

Ontop in the Optique Architecture

Ontop in the Optique Architecture
Ontop

Stardog
• Stardog is a commercial triplestore developed by complexible, Inc.
• Since version 4 released in November 2015, Stardog has integrated Ontop code to
support SPARQL queries over virtual RDF graphs.
• The Virtual Graph feature is only available in the enterprise edition

Outline
1 Introduction
2 Overview of Ontop
4 Use Cases

Conceptual Framework of Query Answering by Query Rewriting
ONTOLOGY
MAPPINGS
DATA
SOURCES
. . .
. . .
. . .
. . .
Ontological Query q
Rewritten Query
SQLRelational Answer
Ontological Answer
qresult
Rewriting
Unfolding
Evaluation
Result Translation

Ontop Workflow
Ontop
ON-LINE OFF-LINE
Reasoner
Ontology
Mapping-
Optimiser
Mappings
DB Integrity Constraints
Classified
Ontology
T-mapping
SPARQL
Query
Query Rewriter
SQL query
SPARQL to SQL
Translator
Figure: The Ontop workflow
• The off-line stage (start-up time) processes the ontology, mappings, and database
integrity constraints.
• The on-line stage executes SPARQL queries by rewriting to SQL queries

Offline Stage
The offline stage can be thought of as consisting of three phases:
• ontology classification
• T-mapping construction
• T-mapping optimization
Example
• New axioms in the classified Ontology
:NSCLC rdfs:subClassOf :Neoplasm .
:SCLC rdfs:subClassOf :Neoplasm .
• Inferred Mappings after T-mapping construction
:db1/neoplasm/{pid} a :Neoplasm .
← SELECT pid FROM tbl patient WHERE type = false
← SELECT pid FROM tbl patient WHERE type = true
• Optimized T-mappings
← SELECT pid FROM tbl patient WHERE type = false OR type = true

Online Stage
During query execution (the online stage), Ontop transforms an input SPARQL queries
into an optimized SQL query using the T-mappings and database integrity constraints.
Optimizing the generated SQL queries
structural optimizations
• pushing the joins inside the unions,
• pushing the functions as high as possible in the query tree,
• eliminating sub-queries.
Semantic query optimizations
semantic analysis of SQL queries to reduce the size and complexity
• removing redundant self-joins,
• detecting unsatisﬁable or trivially valid (true) conditions.

Example of SQL translation and optimization
• Consider a SPARQL query
SELECT ?x WHERE { ?x a :Neoplasm ; :hasStage :stage -IIIa . }
• Non-optimized generated SQL query
SELECT Q1.x FROM (( SELECT concat(":db1/neoplasm/", pid) AS x
FROM tbl_patient WHERE type = false OR type = true) Q1
JOIN (SELECT concat(":db1/neoplasm/", pid) AS x
FROM tbl_patient WHERE stage = 4 AND type = false) Q2
ON Q1.x = Q2.x)
• SQL query after the structural optimization
SELECT concat(":db1/neoplasm/", Q.pid) AS x FROM
(SELECT T1.pid
FROM tbl_patient T1 JOIN tbl_patient T2 ON T1.pid = T2.pid
WHERE (T1.type = false OR T1.type = true)
AND T2.stage = 4 AND T2.type = false) Q
• SQL query after the self-join elimination
SELECT concat(":db1/neoplasm/", Q.pid) AS x FROM
(SELECT pid FROM tbl_patient WHERE type = false AND stage = 4) Q
• SQL query after the second structural optimization
SELECT concat(":db1/neoplasm/", pid) AS x FROM tbl_patient
WHERE type = false AND stage = 4

Outline
1 Introduction
2 Overview of Ontop
4 Use Cases

Statoil Use Case
• Optique Use Case Partner
• Main reference: “Ontology Based Access to Exploration Data at Statoil
[Kharlamov, Hovland, et al., 2015, ISWC In-use Track].
• Exploration domain
• Improve the eﬃciency of the information gathering routine for geologists at Statoil
• Eﬃcient, creative data collection from multiple data sources
• ⇒ separate slides for this use case

Siemens Use Case
• Optique Use Case Partner
• Main reference: “How Semantic Technologies Can Enhance Data Access at
Siemens Energy” [Kharlamov, Solomakhina, et al., 2014, ISWC In-use Track]
• ⇒ separate slides for this use case

EPNet Use Case
• EPNet Project (ERC Advanced Grant EPNet “Production and distribution of
food during the Roman Empire: Economics and Political Dynamics”,
ERC-2013-ADG 340828).
• Main reference: “Ontology-Based Data Integration in EPNet: Production and
Distribution of Food During the Roman Empire” [Calvanese, Liuzzo, et al., 2016,
J. of Eng. Appl. of AI]
• Ontology-Based Data Integration: integrating multiple datasource.
• Linking three datasets: the EPNet relational repository , the Epigraphic Database
Heidelberg, and the Pleiades dataset

EMSec Use Case
• EMSec (Echtzeitdienste f¨ur die Maritime Sicherheit, Real-time Services for the Maritime Security)
is a German BMBF (Federal Ministry of Research and Education) funded project
• Geo-spatial support by Ontop-spatial (developed as a fork of Ontop)
• Sextant for visualizing linked geospatial data
• Use case paper “Ontology-based Data Access for Maritime Security” is under
submission

IBM Research Ireland Use Case
• Main reference: “Data Access Linking and Integration with DALI: building a
Safety Net for an Ocean of City Data” [Lopez et al., 2015, ISWC In-use Track]
• Smarter Cities Technology Centre, IBM Research, Ireland

Electronic Health Records Use Case
• Main reference: “Validating an ontology-based algorithm to identify patients with
Type 2 Diabetes Mellitus in Electronic Health Records” [Rahimi et al., 2014, Int.
J. of Medical Informatics]
• Medicine, The University of New South Wales, Australia

Electronic Health Records Use Case (Cont.)

Use Cases
• More use cases are in https://github.com/ontop/ontop/wiki/UseCases
• Unfortunately, we are not able to track all use cases of Ontop.

Outline
1 Introduction
2 Overview of Ontop
4 Use Cases

Recent Progresses
More recent lines of research on Ontop include
• formalization of SPARQL in the context of OBDA [Rodriguez-Muro and Rezk,
2015, J. Web Semantics] [Kontchakov et al., 2014, ISWC]
• OWL 2 QL entailment regime [Kontchakov et al., 2014, ISWC]
• SWRL rule language with a limited form of recursion handled by SQL Common
Table Expressions [Xiao et al., 2014, RR]
• owl:sameAs for cross-linked datasets [Calvanese, Giese, et al., 2015, ISWC]
• Expressive ontologies beyond OWL 2 QL by rewriting and approximation with the
help of the mapping layer [Botoeva et al., 2016, AAAI]
• System description of Ontop [Calvanese, Cogrel, et al., 2016, Semantic Web J.,
to appear]

Recent Progresses
More recent lines of research on Ontop include
• formalization of SPARQL in the context of OBDA [Rodriguez-Muro and Rezk,
2015, J. Web Semantics] [Kontchakov et al., 2014, ISWC]
• OWL 2 QL entailment regime [Kontchakov et al., 2014, ISWC]
• SWRL rule language with a limited form of recursion handled by SQL Common
Table Expressions [Xiao et al., 2014, RR]
• owl:sameAs for cross-linked datasets [Calvanese, Giese, et al., 2015, ISWC]
• Expressive ontologies beyond OWL 2 QL by rewriting and approximation
with the help of the mapping layer [Botoeva et al., 2016, AAAI]
• System description of Ontop [Calvanese, Cogrel, et al., 2016, Semantic Web J.,
to appear]

Beyond OWL 2 QL (AAAI 16 paper)
• Framework for Rewriting and Approximation of OBDA specifications
. . .
. . .
. . .
. . .
T , M, S T , M , S
⇒
. . .
. . .
. . .
. . .
Rewriting The new specification is equivalent to the original one w.r.t. query
answering (query-inseparable).
Approximation The new specification is a sound approximation of the original one
w.r.t. query answering.

Beyond OWL 2 QL (II)

Beyond OWL 2 QL (III)

WIP: OBDA beyond Relational Databases
Mapping parsing
a
Ontology parsing
b
Mapping
compilation
c
sparql
parsing
0
Rewriting
1 Unfolding w.r.t.
mappings
2
Structural/semantic
optimization
3
Normalization/
Decomposition
4
RA-to-native query
translation
5
Evaluation
6
Post-
processing
7
Mapping ﬁle
Ontology ﬁle
Mapping M
Ontology T
T -Mapping MT
sparql
string
sparql Q Rewritten
sparql QT
RA q1
RA q2
RA q3Native
queries
Native
results
sparql
result
OFFLINE
ONLINE
• NoSQL Movement
• In fact most of the components of Ontop are SQL-independent
• We are working on OBDA over non-relational datasource
• We are targeting on MongoDB now

Future
• In order to further improve performance, we will investigate data-dependent
optimizations.
• support larger fragments of SPARQL (e.g., aggregation, negation, and path
queries) and R2RML (e.g., named graphs).
• For end-users, we will improve the GUI and extend utilities to make Ontop even
more user-friendly.
• go beyond relational databases and support other kinds of data sources (e.g.,
graph and document databases).
• Continue building community

Thank you
for your attention!
QUESTIONS?

References I
Kharlamov, Evgeny, Nina Solomakhina, Özgür Lütfü Öz¸cep, Dmitriy Zheleznyakov, Thomas Hubauer, Steffen Lamparter,
Mikhail Roshchin, Ahmet Soylu, and Stuart Watson (2014). “How Semantic Technologies Can Enhance Data Access at
Siemens Energy”. In: The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda,
Italy, October 19-23, 2014. Proceedings, Part I, pp. 601–619.
Kontchakov, Roman, Martin Rezk, Mariano Rodriguez-Muro, Guohui Xiao, and Michael Zakharyaschev (2014). “Answering
SPARQL Queries over Databases under OWL 2 QL Entailment Regime”. In: vol. 8796.
doi:10.1007/978-3-319-11964-9 35, pp. 552–567.
Rahimi, Alireza, Siaw-Teng Liaw, Jane Taggart, Pradeep Ray, and Hairong Yu (2014). “Validating an ontology-based
algorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records”. In: Int. J. of Medical
Informatics 83.10. doi:10.1016/j.ijmedinf.2014.06.002, pp. 768–778.
Xiao, Guohui, Martin Rezk, Mariano Rodriguez-Muro, and Diego Calvanese (2014). “Rules and Ontology Based Data
Access”. In: Proc. 8th Int. Conference on Web Reasoning and Rule Systems (RR 2014). Ed. by Marie-Laure Mugnier and
Roman Kontchakov. Lecture Notes in Computer Science. Springer.
Calvanese, Diego, Martin Giese, Dag Hovland, and Martin Rezk (2015). “Ontology-based Integration of Cross-linked
Datasets”. In: Proc. of the 14th Int. Semantic Web Conference (ISWC). Lecture Notes in Computer Science. Springer.
Kharlamov, Evgeny, Dag Hovland, et al. (2015). “Ontology Based Access to Exploration Data at Statoil”. In: The Semantic
Web - ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015,
Proceedings, Part II, pp. 93–112.
Lopez, Vanessa, Martin Stephenson, Spyros Kotoulas, and Pierpaolo Tommasi (2015). “Data Access Linking and Integration
with DALI: Building a Safety Net for an Ocean of City Data”. In: The Semantic Web - ISWC 2015 - 14th International
Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part II, pp. 186–202.
Rodriguez-Muro, Mariano and Martin Rezk (2015). “Efficient SPARQL-to-SQL with R2RML Mappings”. In: 33.
doi:10.1016/j.websem.2015.03.001, pp. 141–169.

References II
Botoeva, Elena, Diego Calvanese, Valerio Santarelli, Domenico Fabio Savo, Alessandro Solimando, and Guohui Xiao (2016).
“Beyond OWL 2 QL in OBDA: Rewritings and Approximations”. In: Proc. of the 30th AAAI Conf. on Artiﬁcial
Intelligence (AAAI). AAAI Press.
Calvanese, Diego, Benjamin Cogrel, Sarah Komla-Ebri, Roman Kontchakov, Davide Lanti, Martin Rezk,
Mariano Rodriguez-Muro, and Guohui XIao (2016). “Ontop: Answering SPARQL Queries over Relational Databases”. In:
Semantic Web Journal.
Calvanese, Diego, Pietro Liuzzo, Alessandro Mosca, Jose Remesal, Martin Rezk, and Guillem Rull (2016). “Ontology-Based
Data Integration in EPNet: Production and Distribution of Food During the Roman Empire”. In: Engineering Applications
of Artiﬁcial Intelligence.

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