it's our presentation at WEB 2013 the first internationl conference on building and exploring web based environments, held at seville, Spain (January 27-February 01)

1. GlobeNet 2013
WEB 2013, The First International Conference on
Building and Exploring Web Based Environments
January 27 - February 1, 2013 - Seville, Spain
Translating Natural Language
Competency Questions into SPARQL
Queries: A Case Study
Authors:
Leila ZEMMOUCHI-GHOMARI, l_zemmouchi@esi.dz
Abdessamed Réda GHOMARI, a_ghomari@esi.dz
LMCS Laboratory
National Superior School of Computer Science, Algiers, Algeria
www.esi.dz

2. OUTLINE
1. MOTIVATION
2. RELATED WORK
3. PROPOSED TRANSLATION APPROACH
4. CASE STUDY
6. CONCLUSIONS AND FUTURE WORK
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3. 1. MOTIVATION
The context of the current research work is a PHD thesis focused on an ontology
engineering process
Translation
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4. 1. MOTIVATION
The context of the current research work is a PHD thesis focused on an ontology
engineering process
expressed in a formal
language in order to
allow automatic
evaluation
Competency questions is a
well-known technique that
allow to determine the
requirements or needs the
ontology should fulfill
Translation
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5. 2. RELATED WORK
To the best of our knowledge, automatic translation of competency questions into
SPARQL queries, with the aim of validating an ontology, has not been tackled by
researchers.
Although, in a more general perspective, there exist several approaches dedicated
to web Question Answering (QA) area
CNL
OWLPATH
PANTO
DEANNA
Ben Abacha &
Zweigenbaum
Approach, 2012
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6. 2. RELATED WORK
CNL OWLPATH PANTO DEANNA Ben Abacha &
Zweigenbaum
Ontology-based OWL Ontology- Portable Natural Deep Answers for Approach, 2012
Controlled guided query Language Naturally Asked
Natural Language Editor Interface to Questions Translating Medical
Editor Ontologies Questions into
SPARQL Queries
Limitations:
 Scalability: Their test ontologies are relatively small
 Preliminary work are necessary to apply theses approaches like Mapping
set between concepts’ questions and queried knowledge bases difficult to carry
out and to maintain.
 some of them focus on some types of questions and some know. domains
 No consensus of web QA community on a single approach
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7. 3. PROPOSED TRANSLATION APPROACH (1/3)
A variation of [Ben Abacha & Zweigenbaum, 2012] Approach
 Specific to the medical field
WHY ?
 Limited to a particular set of questions:
WH questions, except complex ones (why and when).
Their approach Our approach
1. Identifying QuestionType 1. Identifying QuestionType
HOW ?
2. Determining the Expected Answer(s) 2. Determining the expected
Type(s) for WH questions answer
3. Constructing the question’s
affirmative and simplified form
4. Medical Entity Recognition 3. Entity Extraction
(treatment, disease…)
5. Relation Extraction 4. Identifying answer entity type
and entity location in the ontology
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SPARQL Query 2013 - Seville, Spain
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8. 3. PROPOSED TRANSLATION APPROACH (2/3)
Phase I: Identifying competency questions’ categories according to expected
answers’ types:
a) Definition Questions: that begins with “What is/are” or “What does mean”
b) Boolean or Yes/No Questions
c) Factual Questions: the answer is a fact or a precise information
d) List questions: the answer is a list of entities
e) Complex Questions: that begins with “How” and “Why”
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9. 3. PROPOSED TRANSLATION query result clause (2/3)
the APPROACH
specifies the result form
Phase I: Identifying competency questions’ categories according to expected
answers’ types:
a) Definition Questions: that begins with “What is/are” or “What does mean”
b) Boolean or Yes/No Questions
c) Factual Questions: the answer is a fact or a precise information
d) List questions: the answer is a list of entities
e) Complex Questions: that begins with “How” and “Why”
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10. 3. PROPOSED TRANSLATION APPROACH (3/3)
Phase II: Determining the expected (perfect or ideal) answer
Phase III: Extracting Entity or Entities from questions and their
corresponding expected answers identified in II
Phase IV: Identifying answer entity type (class, data property,
object property, annotation, axiom, instance) and entity location in
the ontology
Phase V: Constructing SPARQL query based on question type
identified in phase I, question/answer entity extracted from phase
III and its corresponding entity type/entity location in the ontology
from phase IV
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11. 3. PROPOSED TRANSLATION APPROACH (3/3)
Mapping between
question/answer entity
Phase II: Determining the expected (perfect or ideal) answer
and ontology entity
Phase III: Extracting Entity or Entities from questions and their
corresponding expected answers identified in II
Phase IV: Identifying answer entity type (class, data property,
object property, annotation, axiom, instance) and entity location in
the ontology
Phase V: Constructing SPARQL query based on question type
identified in phase I, question/answer entity extracted from phase
III and its corresponding entity type/entity location in the ontology
from phase IV
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12. 3. PROPOSED TRANSLATION APPROACH (3/3)
Phase II: Determining the expected (perfect or ideal) answer
Phase III: Extracting Entity or Entities from questions and their
corresponding expected answers * WHERE in II
SELECT identified
{?Teacher rdf:type HERO:Teacher . }
Phase IV: Identifying answer entity type (class, data property,
object property, annotation, axiom, instance) and entity location in
the ontology
Phase V: Constructing SPARQL query based on question type
identified in phase I, question/answer entity extracted from phase
III and its corresponding entity type/entity location in the ontology
from phase IV
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13. 4. CASE STUDY: HERO
Translation of Competency Questions of
HERO ontology (Higher Education Reference Ontology) into SPARQL Queries
HERO describes several aspects of university domain such as organizational
structure, administration, staff, roles, incomes, etc.
HERO aims to be a valuable tool for researchers and institutional employees
interested in analyzing the system of higher education as a whole.
 HERO Ontology is available at:
http://sourceforge.net/projects/heronto/?source=directory
 Competency questions (81) and their corresponding queries are available
at: http://herontology.esi.dz/content/downloads
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14. 4. CASE STUDY
Phase I: Identifying competency questions’ categories according to
expected answers’ types
CQs’ Categories CQs’ Examples from 81 CQs
Definition questions CQ59.What is a Credit?
Yes/No questions CQ3. Must a university teacher be a researcher?
Factual questions CQ44. What average size and duration have governing board?
List questions CQ1. What are the possible academic ranks of a teacher?
Complex questions CQ41.Why universities are organized into departments?
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15. 4. CASE STUDY
Phase II: Determining the expected answer
CQs’ Examples Corresponding Answers
CQ59.What is a Credit? Each course bears a specified number of credits.
In general, the number of credits a course carries is determined
by the number of class hours the course meets each week.
CQ3. Must a university Nearly all faculty members are expected to engage in research.
teacher be a researcher?
CQ44. What average size and The average size of public boards is approximately 10 people and
duration have governing the average size among independent (private) institutions is 30.
board? The length of board members’ terms varies from three years to as
long as 12 years.
CQ1. What are the possible Assistant Professor, Associate Professor, Full Professor, Professor
academic ranks of a teacher? Emeritus.
CQ41.Why universities are The basic unit of academic organization in most institutions is
organized into departments? the department (e.g., chemistry, political science). Every
department belongs to an academic field.
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16. 4. CASE STUDY Answers sources are:
academic reports,
Phase II: Determining the expected answer governmental websites,
experts’ interviews, ...
CQs’ Examples Corresponding Answers
CQ59.What is a Credit? Each course bears a specified number of credits.
In general, the number of credits a course carries is determined
by the number of class hours the course meets each week.
CQ3. Must a university Nearly all faculty members are expected to engage in research.
teacher be a researcher?
CQ44. What average size and The average size of public boards is approximately 10 people and
duration have governing the average size among independent (private) institutions is 30.
board? The length of board members’ terms varies from three years to as
long as 12 years.
CQ1. What are the possible Assistant Professor, Associate Professor, Full Professor, Professor
academic ranks of a teacher? Emeritus.
CQ41.Why universities are The basic unit of academic organization in most institutions is
organized into departments? the department (e.g., chemistry, political science). Every
department belongs to an academic field.
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17. 4. CASE STUDY
Phase III: Extracting Entity or Entities from competency questions and
their corresponding expected answers identified in II.
This extraction is based on a mapping between relevant terms in
questions/answers pairs and their equivalent terms in the ontology
Extracted terms from CQs’ Extracted terms from Answers
CQ59.What is a Credit? Each course bears a specified number of credits.
In general, the number of credits a course carries is
determined by the number of class hours the course meets
each week.
CQ3. Must a university teacher Nearly all faculty members are expected to engage in
be a researcher? research.
CQ44. What average size and The average size of public boards is approximately 10 people
duration has governing and the average size among independent (private)
board? institutions is 30. The length of board members’ terms varies
from three years to as long as 12 years.
CQ41.Why universities are The basic unit of academic organization in most institutions
organized into departments? is the department (e.g., chemistry, political science). Every
department belongs to an academic field.
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18. 4. CASE STUDY:
Phase IV: Identifying answer entity type (class, data property, object
property, annotation, axiom, instance) and entity location in the ontology
Entities’ Types Entities’ Locations in the ontology
Class: Course CourseCreditsNumber Domain Course
Data Property: CourseCreditsNumber
Classes: Teacher, Researcher Teacher SubClassOf Researcher
Class: Governing Board GoverningBoardSize Domain GoverningBoard
Data Properties: Size, Duration GoverningBoardDuration Domain GoverningBoard
Class: Teacher TeacherRank Domain Teacher
Data Property: Rank, Assistant AssistantProfessor SubPropertyOf TeacherRank
Professor, Associate Professor, Full AssociateProfessor SubPropertyOf TeacherRank
Professor, Professor Emeritus FullProfessor SubPropertyOf TeacherRank
ProfessorEmeritus SubPropertyOf TeacherRank
Classes: Higher Education Department SubClassOf Faculty
Organization, Department Faculty SubClassOf Role
Role SubClassOf HigherEducationOrganization
Department Definition
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19. 4. CASE STUDY:
Phase V: Construction of SPARQL queries
Competency Questions SPARQL Queries
CQ59.What is a Credit? SELECT ?comment WHERE
{ HERO:CourseCreditsNumber rdfs:comment ?comment }
CQ3. Must a university teacher be a ASK
researcher? {HERO:Teacher rdfs:subClassOf HERO:Researcher .}
SELECT ?university ?size WHERE
CQ44. What average size and { ?university rdf:type HERO:HigherEducationOrganization;
duration have governing board? ?y rdfs:subClassOf ?university ;
?y HERO:GoverningBoardSize ?size }
SELECT ?university ?duration
WHERE { ?university rdf:type HERO:HigherEducationOrganization ;
?y rdfs:subClassOf ?university ;
?y HERO:GoverningBoardDuration?duration }
CQ1. What are the possible SELECT ?a ?b ?c ?d WHERE
academic ranks of a teacher? {?a rdfs:subPropertyOf HERO:TeacherRank.
?b rdfs:subPropertyOf ?a .
?c rdfs:subPropertyOf ?b .
?d rdfs:subPropertyOf ?c .}
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20. 4. CASE STUDY: These queries can be checked out by
using available online SPARQL end-
Phase V: Construction of SPARQL queries or off-line tools such as: TWINKLE
points
Competency Questions SPARQL Queries
CQ59.What is a Credit? SELECT ?comment WHERE
{ HERO:CourseCreditsNumber rdfs:comment ?comment }
CQ3. Must a university teacher be a ASK
researcher? {HERO:Teacher rdfs:subClassOf HERO:Researcher .}
SELECT ?university ?size WHERE
CQ44. What average size and { ?university rdf:type HERO:HigherEducationOrganization;
duration have governing board? ?y rdfs:subClassOf ?university ;
?y HERO:GoverningBoardSize ?size }
SELECT ?university ?duration
WHERE { ?university rdf:type HERO:HigherEducationOrganization ;
?y rdfs:subClassOf ?university ;
?y HERO:GoverningBoardDuration?duration }
CQ1. What are the possible SELECT ?a ?b ?c ?d WHERE
academic ranks of a teacher? {?a rdfs:subPropertyOf HERO:TeacherRank.
?b rdfs:subPropertyOf ?a .
?c rdfs:subPropertyOf ?b .
?d rdfs:subPropertyOf ?c .}
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21. 5. CONCLUSION AND FUTURE WORK
• Summary
Intended users: ontology developers, i.e.;
They are familiar with: ontology language, ontology
structure and query language
Intended uses: ontology validation, i.e.;
Since competency questions are the starting point for
extracting relevant terms that become later ontology entities
translated CQs on SPARQL Queries target directly
ontology entities
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22. 5. CONCLUSION AND FUTURE WORK
Helps in Entity location
• Summary (phase 4 ) and query
construction (phase 5)
Intended users: ontology developers, i.e.;
They are familiar with: ontology language, ontology
structure and query language
Helps in Entity extraction (phase 3 )
Intended uses: ontology validation, i.e.;
Since competency questions are the starting point for
extracting relevant terms that become later ontology entities
translated CQs on SPARQL Queries target directly
ontology entities
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23. 5. CONCLUSION AND FUTURE WORK
• Limitations
 Two of
proposed approach phases are manual and
dependent of user knowledge background:
Entity extraction from questions/answers pairs and mapping
between questions/answers relevant terms and ontology entities
 Weak treatment of complex questions
• Future Work
 The best way to tackle the issue of manual phases is to
integrate natural language processing tools like GATE in
terms extraction phase and automatic matching systems
such as COMA 3.0 which efficiency has been already proved.
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24. SOME REFERENCES
1. CQs……M. Gruninger and M. S. Fox, “Methodology for the design and evaluation
of ontologies”, IJCAI95, Workshop on Basic Ontological Issues in Knowledge
Sharing. Montreal, 1995, pp. 6.1–6.10.
2. Web QA Approach….. A. Ben Abacha and P. Zweigenbaum, “Medical Question
Answering: Translating Medical Questions into SPARQL Queries”, Proceedings of
the 2nd ACM SIGHIT International Health Informatics Symposium, Miami,
Florida, USA, 2012, pp. 41-50.
3. SPARQL….Querying the Semantic Web: SPARQL by Emanuelle Della Valle and
Stefano Ceri, pp 299-363 in HANDBOOK OF SEMANTIC WEB
TECHNOLOGIES, 2011, SPRINGER.
THANK YOU FOR YOUR ATTENTION
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