University of Montpellier, France.

University of Montpellier, France.
Thesis defense 17 Oct 2016 Montpellier, France Formalizing and Studying Dialectical Explanations in Inconsistent Knowledge Bases Abdallah Arioua University of Montpellier, France. Thesis supervisors: Patrice Buche Madalina Croitoru Jérôme Fortin

Motivation Durum Wheat durability in the project ANR DUR-DUR
Motivation Durum Wheat durability in the project ANR DUR-DUR ANR DUR-DUR project: reduce energy consumption, chemical inputs while providing protein-rich Durum Wheat. Knowledge integration from different disciplines to facilitate interdisciplinary communication. General knowledge about Agriculture Knowledge about technological itineraries Knowledge about diseases and their causes A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Motivation Durum Wheat durability in the project ANR DUR-DUR
Motivation Durum Wheat durability in the project ANR DUR-DUR ANR DUR-DUR project: reduce energy consumption, chemical inputs while providing protein-rich Durum Wheat. Knowledge integration from different disciplines to facilitate interdisciplinary communication. Problem of inconsistency due to conflicting sources and errors in knowledge acquisition. Provide a user-friendly system that allows: Reasoning under inconsistency. Providing explanations. A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

AI Research Context Query answering under inconsistency
AI Research Context Query answering under inconsistency Expert Academic Documents Research InconsistentKnowledge base Query Consistent answers Inconsistency Handling Method A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

AI Research Context Existential Rules logical framework
AI Research Context Existential Rules logical framework A knowledge base is a set of facts, rules and constraints. Knowledge base A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

AI Research Context Existential Rules logical framework A knowledge base is a set of facts, rules and constraints. Knowledge base Consistent query answering (Lembo et al. 2010) A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

AI Research Context Existential Rules logical framework A knowledge base is a set of facts, rules and constraints. Knowledge base Consistent query answering (Lembo et al. 2010) Repairs of F: A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

AI Research Context Existential Rules logical framework A knowledge base is a set of facts, rules and constraints. Knowledge base Consistent query answering (Lembo et al. 2010) Repairs of F: A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Consistent answers:

AI Research Context Logic-based Argumentation
AI Research Context Logic-based Argumentation Argumentation framework (Dung 1995): a c d e b Arguments and attack (M. Croitoru and S. Vesic 2013): A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

AI Research Context Logic-based Argumentation
AI Research Context Logic-based Argumentation Extensions of an argumentation framework: A set of arguments that respects: Conflict-freeness, Defense and Maximality Two extensions in our example: A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Universal answers to a query:

How do we interactively explain query answering under inconsistency
Research Question What the thesis is about The two approaches equivalently1 cope with inconsistency. Provide explanations under inconsistency? How do we interactively explain query answering under inconsistency to the end-user? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Argumentation fits better with this requirement. 1Madalina Croitoru, Srdjan Vesic: What Can Argumentation Do for Inconsistent Ontology Query Answering? SUM 2013

Research Problems What the thesis is about Research problem 1:
Research Problems What the thesis is about Research problem 1: Is it possible to define a dialectical model of explanation? Input: domain-specific explanation request? Output: an explanation dialogue that aims to fulfill the request. Why do we perform tiller fertilization? Original contributions: Object-level Dialectical Explanation. Durum Wheat knowledge base. DALEK prototype. Case study: knowledge acquisition Walton dialogue model (2011, 2016) A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. (Ontologies: Agrovoc, Agroportal, Agrontology...) (debate platforms: Arvina, d-bas...)

Research Problems What the thesis is about Research problem 2:
Research Problems What the thesis is about Research problem 2: How do we explain the mechanism of consistent query answering? Input: a query that has (has not) a consistent answer. Output: an explanation that aims to make the user understand how consistent query answering works for the given query. Why the query Q()=field(x) ∧ useChemical(y,x) has a consistent answer? Original contributions: One-shot Argument-based Explanations. Meta-level Dialectical Explanations. Experimental evaluation: comparison A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Axiom pinpointing. Concept pinpointing. MIS. Dialogue games Dialectical proofs Game semantics

esearch problem 1 R R Is it possible to define a dialectical model of explanation? Durum Wheat Knowledge Base DALEK Prototype Object-level Dialectical Explanation Case Study: knowledge acquisition A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Durum Wheat Knowledge Base
1 Durum Wheat Knowledge Base The content (A. Arioua, M. Croitoru and P. Buche, MTSR’16) General knowledge about Agriculture Knowledge about technological itineraries Knowledge about diseases and their causes A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

1 Durum Wheat Knowledge Base The authoring (A. Arioua, M. Croitoru and P. Buche, MTSR’16) Reports CoGui 1.6 A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

1 Durum Wheat Knowledge Base The content of the knowledge base Nb concepts: 279 Nb relations: 116 Nb rules: 37 Nb constraints: 30 Nb facts: 900 A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

1 Durum Wheat Knowledge Base The content of the knowledge base A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

esearch problem 1 R Durum Wheat Knowledge Base DALEK Prototype Object-level Dialectical Explanation Case Study: knowledge acquisition Is it possible to define a dialectical model of explanation? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) Built by non-experts  problem of quality A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Why do we perform tiller fertilization?
1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) Why do we perform tiller fertilization? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) Why do we perform tiller fertilization? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Because there is no Nitrogen in the soil.

I don’t understand why there is no
1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) I don’t understand why there is no Nitrogen in the soil? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) I don’t understand why there is no Nitrogen in the soil? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Because sunflower was the precedent on this soil.

1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) I don’t understand why sunflower has been chosen as precedent? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Because it is a good precedent if we aim at fighting against diseases and weed

I still don’t understand
1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) I still don’t understand A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. In order to reduce chemical inputs we use a precedent that uses small amounts of Nitrogen and exploits well the soil. In this case it’s sunflower.

1 DALEK prototype Dialogue example (A. Arioua, M. Croitoru and P. Buche, COMMA’16) A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. I understand why do we use sunflower. I understand why do we perform tiller fertilization.

1 Object-level Dialectical Explanations
Formalization (A. Arioua and M. Croitoru, SUM’16)

Object-level Dialectical Explanations
1 Object-level Dialectical Explanations The use-case A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

R esearch problem 1 Durum Wheat Knowledge Base DALEK Prototype Object-level Dialectical Explanation Case Study: knowledge acquisition Is it possible to define a dialectical model of explanation? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Case Study: knowledge acquisition
1 Case Study: knowledge acquisition Quality = less inconsistencies and more knowledge Improve the quality A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Argument/Explanation
1 Case Study: knowledge acquisition The interaction with the experts (A. Arioua, M. Croitoru and P. Buche, IN-OVIVE’16) 9 queries Wizard of OZ Expert Decision (Y,N,Neutral) + A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Argument/Explanation

1 Case Study: knowledge acquisition The interaction with the experts
“Use a straw cereal precedent” “Use straw cereal precedent” Wizard of OZ Expert A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

“Use a straw cereal precedent” “Use straw cereal precedent” Wizard of OZ Expert “Use a straw cereal precedent” “No, we need a precedent that allows us to fight against weed, provide nitrogen and reduce diseases. Which is quite the opposite of a cereal.” A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Responds with an argument
1 Case Study: knowledge acquisition The interaction with the experts Responds with an argument Wizard of OZ Expert A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Responds with an argument Wizard of OZ Expert Argument/Explanation A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

1 Case Study: knowledge acquisition The forms N Yes No Neutral
Relevance Queries N A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Case Study: knowledge acquisition
1 Case Study: knowledge acquisition Results (A. Arioua, M. Croitoru and P. Buche, IN-OVIVE’16) Table 3: The gain of new knowledge for Expert 1 and Expert 2 (49 inconsistencies before the case study). A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Table 4: The percentage of revised decisions and revised relevance values.

esearch problem 2 R How do we explain the mechanism of consistent query answering? One-shot Argument-based Explanations Meta-level Dialectical Explanations Experimental Evaluation: comparison A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

One-shot Argument-based Explanations
2 One-shot Argument-based Explanations Preliminaries Universal query answering and consistent query answering are equivalent. Explanation of consistent query answering reduces down to explanation of universal query answering. Characterize universal query answering under logic-based argumentation in terms of arguments in favor/against a query. A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

2 One-shot Argument-based Explanations The intuition – Universal acceptance Query Q()= field(x) ∧ useChemical(y,x) has a universal answer yes. A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Analysis: the query is supported from all points of view (extensions). In each extension there is an argument in favor of the query (i.e. a supporter). Explanation: “Either we use Fertilon only or Archipel only we will always use a chemical product on the field Aix.”

2 One-shot Argument-based Explanations Formalization and new results (A. Arioua and M. Croitoru, ECAI’16) A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

2 One-shot Argument-based Explanations The intuition – Non-universal acceptance Query Q()= field(x) ∧ useChemical(y,x) ∧ fertilizer(y) has no universal answer. A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Analysis: the query is not supported from one point of view. There exists at least one extension that is not in favor of the query. Explanation: “It is possible that the chemical product that we use would be a herbicide.”

2 One-shot Argument-based Explanations Formalization and new results (A. Arioua and M. Croitoru, ECAI’16) A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

2 One-shot Argument-based Explanations Proponent set and block (A. Arioua and M. Croitoru, ECAI’16) A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

esearch problem 2 R One-shot Argument-based Explanations Meta-level Dialectical Explanations Experimental Evaluation: comparison How do we explain the mechanism of consistent query answering? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

2 Meta-level Dialectical Explantions Intuition – Universal acceptance
2 Intuition – Universal acceptance A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Query Q()= field(x) ∧ useChemical(y,x) ∧ fertilizer(y) has a universal answer yes.

2 Meta-level Dialectical Explantions Intuition – Universal acceptance
2 Intuition – Universal acceptance Knowledge base Meta-level dialectical explanation PRO: Do we use any chemical product of type fertilizer in any field x? OPP: No. PRO: But we are going to use a tank mix that contains Fertilon. OPP: Well, it is possible that we use a tank mix of Archipel instead. PRO: But the residue of Nitrogen in Aix are small therefore we are going to use a chemical product of type fertilizer in Aix. OPP: I concede. A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database. Query Q()= field(x) ∧ useChemical(y,x) ∧ fertilizer(y) has a universal answer yes. Same intuition for non-universal acceptance

PRO OPP 2 Meta-level Dialectical Explantions
Intuition (A. Arioua and M. Croitoru, ECAI’16) PRO A game (dialogue) between two fictitious players, the PROponent of the query and the OPPonent of the query. OPP PRO plays only with supporters. OPP tries to attack all the supporters (construct a block). Goal of the dialogue: prove that Q is (non-)universally accepted. If OPP wins then a block exists  Q is not accepted. Otherwise, Q is accepted.

2 Meta-level Dialectical Explantions Dialogues
A dialogue d about a query Q is a finite sequence of moves:

2 Meta-level Dialectical Explantions Turn taking
A dialogue d about a query Q is a finite sequence of moves:

2 Meta-level Dialectical Explantions Moves and locutions
A dialogue d about a query Q is a finite sequence of moves: PRO OPP

Meta-level Dialectical Explantions
2 Termination

2 Meta-level Dialectical Explantions
Winning A dialogue d about a query Q is a finite sequence of moves:

Example – Q is universally accepted

2 Dialogue Associated tree

2 Dialogue Associated tree g

2 Dialogue Associated tree c g

2 Dialogue Associated tree g c

2 Dialogue Associated tree i

d is called a dialectical proof for the universal acceptance of Q
Meta-level Dialectical Explantions 2 Dialogue Associated tree PRO wins d is called a dialectical proof for the universal acceptance of Q

Results and properties (A. Arioua and M. Croitoru, ECAI’16)

A complete and sound proof theory for consistent query answering.
Meta-level Dialectical Explantions 2 Results and properties (A. Arioua and M. Croitoru, ECAI’16) A complete and sound proof theory for consistent query answering.

Results and properties (A. Arioua and M. Croitoru, ECAI’16)

esearch problem 2 R One-shot Argument-based Explanations Meta-level Dialectical Explanations Experimental Evaluation: comparison How do we explain the mechanism of consistent query answering? A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

2 Evaluation Experimental setting Objective: Subjects: Method:
Investigate the impact of dialectical explanations. Subjects: 34 first year computer science students (IUT). Method: Between-subjects (17 each group). OE group One-shot Argument-based Explanations. DE group  Meta-level Dialectical Explanations. General hypothesis: Dialectical explanations have a positive impact on user’s understanding of query answering under CQA [Accuracy, Time, Appreciation]. Part of a study with Abdelraouf Hecham (PhD student), Gem Stapleton (Univ. Brighton)

2 Evaluation Experimental setting Objective: Subjects: Method:
Investigate the impact of dialectical explanations. Subjects: 34 first year computer science students (IUT). Method: Between-subjects (17 each group). OE group One-shot Argument-based Explanations. DE group  Meta-level Dialectical Explanations. General hypothesis: Meta-level Dialectical explanations have a positive impact on user’s understanding of query answering under CQA [Answer’s Accuracy, Time, Appreciation]. Part of a study with Abdelraouf Hecham (PhD student), Gem Stapleton (Univ. Brighton)

Evaluation 2 Evaluation platform 7 situations /subject

2 Evaluation Evaluation platform 7 situations /subject

2 Evaluation Impact on answer’s accuracy Hypothesis 1:
Group DE (Meta-level Dialectical Explanations) has a better accuracy in answering test queries than Group OE (One-shot Argument-based Explanations)

For each group we count number of correct and incorrect answers.
Evaluation 2 Impact on answer’s accuracy Procedure For each group we count number of correct and incorrect answers.

2 Evaluation Impact on answer’s accuracy Procedure
For each group we count number of correct and incorrect answers. Table 1: Contingency table of accuracy, n=119 (Chi-square test p-value=0.016 < 0.05).

Group DE answer time would be shorter than the one of Group OE.
Evaluation 2 Impact on answer time Hypothesis 2: Group DE answer time would be shorter than the one of Group OE.

Group DE answer time would be shorter than the one of Group OE.
Evaluation 2 Impact on answer time Hypothesis 2: Group DE answer time would be shorter than the one of Group OE. Not significant (p-value = > 0.05) Group DE mean = 10.9 sec Group OE mean = 16.1 sec

2 Evaluation Users’ evaluation of explanations Hypothesis 3:
Meta-level Dialectical Explanations are more intelligible than One-shot Argument-based Explanations.

2 Evaluation Users’ evaluation of explanations
30 Figure 1: Median Group DE = clear, median Group OE = so-so Mann-Whitney U test (p-value = < 0.05)

C onclusion Summary Perspectives
A new paradigm that seeks to exploit knowledge, typically domain knowledge, when querying data. More precisely, instead of a database, we consider a knowledge base which is composed of data and of an ontology, and we want to query the data while taking into account inferences enabled by the ontology. More generally, adding an ontological layer on top of data has at least three kinds of well-acknowledged advantages: Ontologies can be used to enrich the vocabulary of data sources, thereby allowing users to formulate their queries in a richer, more familiar vocabulary which abstracts from the specific way data is stored. By allowing inference of new facts, ontologies allow for incomplete data. Data incompleteness may come from a lack of information but it may also be deliberate because one does not want to explicitely store all details about objects in the database.

Conclusion 3 Summary Motivation: ANR DUR-DUR project meets AI research context. Goal: A user-friendly system that allows inconsistency handling an explanation. Research question: How do we interactively explain query answering under inconsistency to the end-user? Research problem 1: Is it possible to define a dialectical model of explanation? Contributions: Durum Wheat knowledge base and DALEK prototype. Object-level Dialectical Explanation. Case study: knowledge acquisition

Conclusion 3 Summary Motivation: ANR DUR-DUR project meets AI research context. Goal: A user-friendly system that allows inconsistency handling an explanation. Research question: How do we interactively explain query answering under inconsistency to the end-user? Research problem 2: How do we explain the mechanism of consistent query answering? Contributions: One-shot Argument-based Explanations. Meta-level Dialectical Explanations. Experimental evaluation: comparison.

3 Conclusion Long term perspectives Practical aspects:
Experimentation with experts of Object-level and Meta-level Dialectical Explanations. Extend DALEK with Meta-level Dialectical Explanations. Natural language processing. Theoretical aspects: Dialectical proof theories for other semantics. Investigate the relation with BAF and Causality. General semantics for EDS.

Conclusion 3 Short term perspectives Your questions? Thank you

Publications 3 Abdallah Arioua, Madalina Croitoru and Patrice Buche:A Datalog+/- Domain-Specific Durum Wheat Knowledge Base. MTSR 2016 Abdallah Arioua, Madalina Croitoru, Patrice Buche: DALEK: A Tool for Dialectical Explanations in Inconsistent Knowledge Bases. COMMA 2016 Abdallah Arioua, Madalina Croitoru: Dialectical Characterization of Consistent Query Explanation with Existential Rules. FLAIRS 2016 Abdallah Arioua, Madalina Croitoru: A Dialectical Proof Theory for Universal Acceptance in Coherent Logic-Based Argumentation Frameworks. ECAI 2016 Abdallah Arioua, Madalina Croitoru, Laura Papaleo, Nathalie Pernelle, Swan Rocher: On the Explanation of SameAs Statements Using Argumentation. SUM 2016 Abdallah Arioua, Patrice Buche, Madalina Croitoru: Explanation Dialogues in the Service of Durum Wheat Sustainability Improvement. IN-OVIVE 2016

Publications 3 Abdallah Arioua, Nouredine Tamani, Madalina Croitoru, Jérôme Fortin, Patrice Buche: Investigating the Mapping between Default Logic and Inconsistency-Tolerant Semantics. ICAISC 2015 Abdallah Arioua, Nouredine Tamani, Madalina Croitoru: Query Answering Explanation in Inconsistent Datalog +/- Knowledge Bases. DEXA 2015 Abdallah Arioua, Madalina Croitoru: Formalizing Explanatory Dialogues. SUM 2015 Abdallah Arioua, Nouredine Tamani, Madalina Croitoru, Patrice Buche: Query Failure Explanation in Inconsistent Knowledge Bases Using Argumentation. COMMA 2014 Abdallah Arioua, Nouredine Tamani, Madalina Croitoru: On Conceptual Graphs and Explanation of Query Answering under Inconsistency. ICCS 2014

3 References Under submission:
Abdallah Arioua, Madalina Croitoru and Srdjan Vesic: Logic-Based Argumentation with Existential Rules. International Journal of Approximate Reasoning. Abdallah Arioua, Madalina Croitoru, Patrice Buche: Explanatory Dialogues with Argumentative Capacities over Inconsistent Knowledge Bases. Expert Systems with Applications

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