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, 2016Stanford 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

• EducationsI PhD in Computer Science, Vienna University of Technology, AustriaI MSc and BSc in Mathematics, Peking University, China

• Research interests:I Artificial intelligence, Knowledge representationI Description logics, Ontology, Semantic WebI Ontology-based Data AccessI 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 NeverSleeps 2.0



The Problem: information access

How to formulate the right questionto obtain the right answerin the ocean of Big Data.



The Problem: information access

How to formulate the right questionto obtain the right answerin the ocean of Big Data.



How much time is spent searching for data?

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



Example: Statoil Exploration

Experts in geology and geophysics developstratigraphic models of unexplored areas onthe basis of data acquired from previousoperations 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.



Example: Statoil Exploration

Experts in geology and geophysics developstratigraphic models of unexplored areas onthe basis of data acquired from previousoperations 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 instancelimit 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 arenot stored in .... (based on .....) and where there could be .......... value. Someof 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))

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!!






SELECT [...]

FROM































WHERE [...]












































































SELECT [...]

FROM































WHERE [...]









































































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 andheterogeneous

MAPPINGS

how to populatethe 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.G. Xiao (FUB) Ontop: Answering SPARQL Queries over Relational Databases 11/56



. . .

. . .

. . .

. . .

ONTOLOGY=

global vocabulary+

conceptual view

DATA SOURCES


MAPPINGS


query

result






. . .

. . .

. . .

. . .

ONTOLOGY=

global vocabulary+

conceptual view

DATA SOURCES


MAPPINGS


query

result






. . .

. . .

. . .

. . .

ONTOLOGY=

global vocabulary+

conceptual view

DATA SOURCES


MAPPINGS


query

result





Outline

1 Introduction

2 Overview of Ontop


4 Use Cases




Ontop

• Is a platform to query databases through ontologies, relying on semantictechnologies.

• 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:I development started 6 years agoI already well established:

• +200 topics in the mail list• +2300 downloads in last 10 months

I 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 APIOWL-API (OWL Parser)

Sesame API(SPARQL Parser)JDBC

Protege Optique Platform

Sesame Workbench & SPARQL Endpoint

Application Layer

API Layer

OntopCore

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).

1http://teiid.jboss.org2http://www.exareme.org


http://teiid.jboss.org

http://www.exareme.org


Example: Hospital Database

Table: tbl patient

pid name type stage

1 ’Mary’ false 42 ’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 OWL2QL as ontology languages.

• OWL2QL is based on the DL-Lite family of lightweight description logics, whichguarantees 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 OWL2QLentailment regime of SPARQL 1.1.

• Implementation of other features of SPARQL 1.1 (e.g., aggregates, property pathqueries, negation) is working in progress.

The following SPARQL query retrieves all the names of all patients who have aneoplasm (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 serializingOWL ontologies. We extended the OWLReasoner Java interface to supportSPARQL query answering.

• Sesame is a de-facto standard framework for processing RDF data. Ontopimplements the Sesame Storage And Inference Layer (SAIL) API supportinginferencing and querying over relational databases.

• Available as Maven artifacts from central repository.



Application Layer of Ontop

• Command line interface

• Protege plugin

• Sesame Workbench and SPARQL Endpoint

• Optique Platform

• Stardog



Ontop Protege plugin

The Ontop Protege 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 Protege



SPARQL query answering in Protege



Ontop plugin available from Protege plugin repository



Sesame workbench and SPARQL endpoint

• Sesame OpenRDF Workbench is a web application for administrating Sesamerepositories.

• 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.



Sesame workbench and SPARQL endpoint

• Sesame OpenRDF Workbench is a web application for administrating Sesamerepositories.

• 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

Figure



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 tosupport 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

DATASOURCES

. . .

. . .

. . .

. . .

Ontological Query q

Rewritten Query

SQLRelational Answer

Ontological Answer

qresult

Rewriting

Unfolding

Evaluation

Result Translation




ONTOLOGY

MAPPINGS

DATASOURCES

. . .

. . .

. . .

. . .

Ontological Query q

Rewritten Query


Ontological Answer

qresult

Rewriting

Unfolding

Evaluation

Result Translation




ONTOLOGY

MAPPINGS

DATASOURCES

. . .

. . .

. . .

. . .

Ontological Query q

Rewritten Query


Ontological Answer

qresult

Rewriting

Unfolding

Evaluation

Result Translation




ONTOLOGY

MAPPINGS

DATASOURCES

. . .

. . .

. . .

. . .

Ontological Query q

Rewritten Query


Ontological Answer

qresult

Rewriting

Unfolding

Evaluation

Result Translation




ONTOLOGY

MAPPINGS

DATASOURCES

. . .

. . .

. . .

. . .

Ontological Query q

Rewritten Query


Ontological Answer

qresult

Rewriting

Unfolding

Evaluation

Result Translation




ONTOLOGY

MAPPINGS

DATASOURCES

. . .

. . .

. . .

. . .

Ontological Query q

Rewritten Query


Ontological Answer

qresult

Rewriting

Unfolding

Evaluation

Result Translation




ONTOLOGY

MAPPINGS

DATASOURCES

. . .

. . .

. . .

. . .

Ontological Query q

Rewritten Query


Ontological Answer

qresult

Rewriting

Unfolding

Evaluation

Result Translation



Ontop Workflow

Ontop

ON-LINE OFF-LINE

Reasoner

Ontology

Mapping-Optimiser

Mappings

DB Integrity Constraints

ClassifiedOntology

T-mapping

SPARQLQuery

Query Rewriter

SQL query

SPARQL to SQLTranslator

Figure: The Ontop workflow

• The off-line stage (start-up time) processes the ontology, mappings, and databaseintegrity 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

:db1/neoplasm/{pid} a :Neoplasm .← SELECT pid FROM tbl patient WHERE type = true

• Optimized T-mappings:db1/neoplasm/{pid} a :Neoplasm .← SELECT pid FROM tbl patient WHERE type = false OR type = true



Online Stage

During query execution (the online stage), Ontop transforms an input SPARQL queriesinto 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 xFROM tbl_patient WHERE type = false OR type = true) Q1

JOIN (SELECT concat(":db1/neoplasm/", pid) AS xFROM 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.pidFROM tbl_patient T1 JOIN tbl_patient T2 ON T1.pid = T2.pidWHERE (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_patientWHERE type = false AND stage = 4









ON Q1.x = Q2.x)
















ON Q1.x = Q2.x)
















ON Q1.x = Q2.x)
















ON Q1.x = Q2.x)










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 atSiemens 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 offood during the Roman Empire: Economics and Political Dynamics”,ERC-2013-ADG 340828).

• Main reference: “Ontology-Based Data Integration in EPNet: Production andDistribution 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 DatabaseHeidelberg, and the Pleiades dataset



EMSec Use Case

• EMSec (Echtzeitdienste fur 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 undersubmission



IBM Research Ireland Use Case

• Main reference: “Data Access Linking and Integration with DALI: building aSafety 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 withType 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.


https://github.com/ontop/ontop/wiki/UseCases


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 CommonTable Expressions [Xiao et al., 2014, RR]

• owl:sameAs for cross-linked datasets [Calvanese, Giese, et al., 2015, ISWC]

• Expressive ontologies beyond OWL2QL by rewriting and approximation with thehelp 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 CommonTable Expressions [Xiao et al., 2014, RR]

• owl:sameAs for cross-linked datasets [Calvanese, Giese, et al., 2015, ISWC]

• Expressive ontologies beyond OWL2QL by rewriting and approximationwith 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. queryanswering (query-inseparable).

Approximation The new specification is a sound approximation of the original onew.r.t. query answering.



Beyond OWL 2 QL (II)



Beyond OWL 2 QL (III)

Figure: OntoproxG. Xiao (FUB) Ontop: Answering SPARQL Queries over Relational Databases 50/56


WIP: OBDA beyond Relational Databases

Mapping parsinga

Ontology parsingb

Mappingcompilation

c

sparqlparsing

0Rewriting

1 Unfolding w.r.t.mappings

2Structural/semantic

optimization

3

Normalization/Decomposition

4

RA-to-native querytranslation

5

Evaluation6

Post-processing

7

Mapping file

Ontology file

Mapping M

Ontology T

T -Mapping MT

sparqlstring

sparql Q Rewrittensparql Q′

T

RA q1

RA q2

RA q3Nativequeries

Nativeresults

sparqlresult

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-dependentoptimizations.

• support larger fragments of SPARQL (e.g., aggregation, negation, and pathqueries) and R2RML (e.g., named graphs).

• For end-users, we will improve the GUI and extend utilities to make Ontop evenmore user-friendly.

• go beyond relational databases and support other kinds of data sources (e.g.,graph and document databases).

• Continue building community


Acknowledgment

Thank youfor your attention!

QUESTIONS?


References I

Kharlamov, Evgeny, Nina Solomakhina, Ozgur Lutfu Ozcep, Dmitriy Zheleznyakov, Thomas Hubauer, Steffen Lamparter,Mikhail Roshchin, Ahmet Soylu, and Stuart Watson (2014). “How Semantic Technologies Can Enhance Data Access atSiemens 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). “AnsweringSPARQL 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-basedalgorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records”. In: Int. J. of MedicalInformatics 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 DataAccess”. In: Proc. 8th Int. Conference on Web Reasoning and Rule Systems (RR 2014). Ed. by Marie-Laure Mugnier andRoman Kontchakov. Lecture Notes in Computer Science. Springer.

Calvanese, Diego, Martin Giese, Dag Hovland, and Martin Rezk (2015). “Ontology-based Integration of Cross-linkedDatasets”. 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 SemanticWeb - 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 Integrationwith DALI: Building a Safety Net for an Ocean of City Data”. In: The Semantic Web - ISWC 2015 - 14th InternationalSemantic 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 ArtificialIntelligence (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-BasedData Integration in EPNet: Production and Distribution of Food During the Roman Empire”. In: Engineering Applicationsof Artificial Intelligence.


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