Ana Ozaki works as an associate professor at the University of Oslo (full time) and at the University of Bergen (part-time), Norway. Her research in Artificial Intelligence focuses on knowledge representation and machine learning theory. Ozaki studies the formalisation of the learning phenomenon so that questions involving learnability, complexity, and reducibility can be systematically investigated and understood. She is particularly interested in algorithms for learning logical theories formulated in description logic and related formalisms for knowledge representation.
She serves the AI community as a program committee member for NORA and as a steering committee member of KR and DL. Also, she is a member of the editorial boards of the Journal of Machine Learning Research and the Journal of Web Semantics. Ozaki has recently worked as Program Committee Chair for the 7th International Joint Conference on Rules and Reasoning and the 36th International Description Logic Workshop. Ozaki is the principal investigator of the project Learning Description Logic Ontologies funded by RCN. Also, she has worked as the principal investigator of the project Apprendimento PAC di Ontologie in Logica Descrittiva (PACO) funded by Unibz. She has also collaborated with Montserrat Hermo within the project Modelos y metodos basados en grafos para la computacion en gran scala.
Ozaki has recently been an invited speaker for the 32nd International Conference on Inductive Logic Programming (ILP), the Symposium of the Norwegian AI Society (NAIS), the TBiLLC 2023 Workshop on Learning and Logic, and for Integreat. She recently co-authored the paper Learning Horn envelopes via queries from language models published at the International Journal of Approximate Reasoning and the paper Mining ℰℒ⊥ Bases with Adaptable Role Depth published at the Journal of Artificial Intelligence Research.
Relevant Links:
Updated publication list by DBLP
Google scholar
Publications
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Mosolygó, Balázs; Opdahl, Andreas Lothe; Xiao, Guohui; Chen, Jiaoyan & Ozaki, Ana
(2025).
Benchmarking Knowledge Graph Question Answering via Complexity-Aware Queries.
In Grabis, Jānis & Wautelet, Yves (Ed.),
Advanced Information Systems Engineering Workshops: CAiSE 2025 Workshops, Vienna, Austria, June 16–20, 2025, Proceedings | Springer Nature Link.
Springer Cham.
ISSN 9783031949319.
p. 30–37.
doi:
10.1007/978-3-031-94931-9_3.
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This paper presents our progress towards creating an end-to-end query generation approach. The resulting pairs of NL and executable queries can be used for setting up KGQA datasets and benchmarking LLMs. The usage of a rigorous theoretical background will ensure that queries are generated with predeterminable complexities.
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Magnini, Matteo; Squarcialupi, Riccardo; Sterri, Martin Tunge & Castillo, Ana Ozaki Rivera
(2025).
Actively Learning EL Terminologies from Large Language Models.
Frontiers in Artificial Intelligence and Applications.
ISSN 0922-6389.
413,
p. 1792–1799.
doi:
10.3233/faia251009.
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In active learning, a learner attempts to learn from a teacher by posing questions. The questions made by the learner are called membership queries and are answered with ‘yes’ or ‘no’. This kind of query is often studied as part of a communication protocol that also includes equivalence queries. Intuitively, equivalence queries ask whether the idea of the learner about the knowledge of the teacher is correct or not. If not, then the teacher should provide a counterexample showing the difference. Here, we consider the teacher as a large language model (LLM) and study the case in which knowledge is expressed as an EL terminology. Membership queries ask whether concept inclusions are true or not. E.g., “Can algae be considered a subcategory of plant?”. Equivalence queries are simulated by a sample with concept inclusions labelled as positive or negative. We present a non-trivial extension of the ExactLearner tool to extract EL terminologies from LLMs. Given the relevant symbols as input (e.g., algae, plant, etc.), the tool tries to find how these symbols should be logically connected by posing questions to LLMs. To evaluate the approach, we present performance results of the ExactLearner in the task of reconstructing existing EL terminologies.
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George, Anne-Marie & Castillo, Ana Ozaki Rivera
(2025).
On Middle Grounds for Preference Statements.
IJCAI International Joint Conference on Artificial Intelligence.
ISSN 1045-0823.
doi:
10.24963/ijcai.2025/503.
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In group decisions or deliberations, stakeholders are often confronted with conflicting opinions. We investigate a logic-based way of expressing such opinions and a formal general notion of a middle ground between stakeholders. Inspired by the literature on preferences with hierarchical and lexicographic models, we instantiate our general framework to the case where stakeholders express their opinions using preference statements of the form ‘I prefer ‘a’ to ‘b’’, where ‘a’ and ‘b’ are alternatives expressed over some attributes, e.g., in a trolley problem, one can express I prefer to save 1 adult and 1 child to 2 adults (and 0 children). We prove theoretical results on the existence and uniqueness of middle grounds. In particular, we show that, for preference statements, middle grounds may not exist and may not be unique. We provide algorithms for deciding the existence and finding middle grounds.
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Castillo, Ana Ozaki Rivera; Confalonieri, Roberto; Guimaraes, Ricardo & Imenes, Anders
(2025).
Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Study Case on BERT-based Language Models.
In Shah, Julie & Kolter, Zico (Ed.),
Proceedings of the 39th AAAI Conference on Artificial Intelligence.
AAAI Press.
ISSN 9781577358978.
p. 19767–19775.
doi:
10.1609/aaai.v39i18.34177.
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Decision trees are a popular machine learning method, valued for their inherent explainability. In Explainable AI, decision trees serve as surrogate models for complex black box AI models or as approximations of parts of such models. A key challenge of this approach is assessing how accurately the extracted decision tree represents the original model and determining the extent to which it can be trusted as an approximation of its behaviour. In this work, we investigate the use of the Probably Approximately Correct (PAC) framework to provide a theoretical guarantee of fidelity for decision trees extracted from AI models. Leveraging the theoretical foundations of the PAC framework, we adapt a decision tree algorithm to ensure a PAC guarantee under specific conditions. We focus on binary classification and conduct experiments where we extract decision trees from BERT-based language models with PAC guarantees. Our results indicate occupational gender bias in these models, which confirm previous results in the literature. Additionally, the decision tree format enhances the visualization of which occupations are most impacted by social bias.
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Artale, Alessandro; Mazzullo, Andrea & Castillo, Ana Helena Ozaki Rivera
(2024).
First-Order Temporal Logic on Finite Traces: Semantic Properties, Decidable Fragments, and Applications.
ACM Transactions on Computational Logic.
ISSN 1529-3785.
25(2),
p. 1–43.
doi:
10.1145/3651161.
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Formalisms based on temporal logics interpreted over finite strict linear orders, known in the literature as finite traces, have been used for temporal specification in automated planning, process modelling, (runtime) verification and synthesis of programs, as well as in knowledge representation and reasoning. In this article, we focus on first-order temporal logic on finite traces. We first investigate preservation of equivalences and satisfiability of formulas between finite and infinite traces, by providing a set of semantic and syntactic conditions to guarantee when the distinction between reasoning in the two cases can be blurred. Moreover, we show that the satisfiability problem on finite traces for several decidable fragments of first-order temporal logic is ExpSpace-complete, as in the infinite trace case, while it decreases to NExpTime when finite traces bounded in the number of instants are considered. This leads also to new complexity results for temporal description logics over finite traces. Finally, we investigate applications to planning and verification, in particular by establishing connections with the notions of insensitivity to infiniteness and safety from the literature.
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Castillo, Ana Helena Ozaki Rivera; Rehman, Anum & Slavkovik, Marija
(2024).
Finding middle grounds for incoherent horn expressions: the moral machine case.
Autonomous Agents and Multi-Agent Systems.
ISSN 1387-2532.
38(2).
doi:
10.1007/s10458-024-09681-6.
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Smart devices that operate in a shared environment with people need to be aligned with their values and requirements. We study the problem of multiple stakeholders informing the same device on what the right thing to do is. Specifically, we focus on how to reach a middle ground among the stakeholders inevitably incoherent judgments on what the rules of conduct for the device should be. We formally define a notion of middle ground and discuss the main properties of this notion. Then, we identify three sufficient conditions on the class of Horn expressions for which middle grounds are guaranteed to exist. We provide a polynomial time algorithm that computes middle grounds, under these conditions. We also show that if any of the three conditions is removed then middle grounds for the resulting (larger) class may not exist. Finally, we implement our algorithm and perform experiments using data from the Moral Machine Experiment. We present conflicting rules for different countries and how the algorithm finds the middle ground in this case.
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Wesenberg, Grunde Haraldsson & Castillo, Ana Ozaki Rivera
(2024).
TØIRoads: A Road Data Model Generation Tool.
Transactions on Graph Data and Knowledge (TGDK).
2(2),
p. 6:1–6:12.
doi:
10.4230/TGDK.2.2.6.
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We describe road data models which can represent high level features of a road network such as population, points of interest, and road length/cost and capacity, while abstracting from time and geographic location. Such abstraction allows for a simplified traffic usage and congestion analysis that focus on the high level features. We provide theoretical results regarding mass conservation and sufficient conditions for avoiding congestion within the model. We describe a road data model generation tool, which we call "TØI Roads". We also describe several parameters that can be specified by a TØI Roads user to create graph data that can serve as input for training graph neural networks (or another learning approach that receives graph data as input) for predicting congestion within the model. The road data model generation tool allows, for instance, the study of the effects of population growth and how changes in road capacity can mitigate traffic congestion.
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Bourgaux, Camille; Guimarães, Ricardo; Koudijs, Raoul; Lacerda, Victor & Castillo, Ana Ozaki Rivera
(2024).
Knowledge Base Embeddings: Semantics and Theoretical Properties.
In Jung, Jean Christoph; Dumančić, Sebastijan & Mansouri, Masoumeh (Ed.),
Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning.
Reasoning, Learning & Decision-Making Track.
AAAI Press.
ISSN 9781956792058.
doi:
10.24963/kr.2024/77.
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Blum, Sophie; Koudijs, Raoul; Ozaki, Ana & Touileb, Samia
(2023).
Learning Horn envelopes via queries from language models.
International Journal of Approximate Reasoning.
ISSN 0888-613X.
171.
doi:
10.1016/j.ijar.2023.109026.
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We present an approach for systematically probing a trained neural network to extract a symbolic abstraction of it, represented as a Boolean formula. We formulate this task within Angluin's exact learning framework, where a learner attempts to extract information from an oracle (in our work, the neural network) by posing membership and equivalence queries. We adapt Angluin's algorithm for Horn formula to the case where the examples are labelled w.r.t. an arbitrary Boolean formula in CNF (rather than a Horn formula). In this setting, the goal is to learn the smallest representation of all the Horn clauses implied by a Boolean formula—called its Horn envelope—which in our case correspond to the rules obeyed by the network. Our algorithm terminates in exponential time in the worst case and in polynomial time if the target Boolean formula can be closely approximated by its envelope. We also show that extracting Horn envelopes in polynomial time is as hard as learning CNFs in polynomial time. To showcase the applicability of the approach, we perform experiments on BERT based language models and extract Horn envelopes that expose occupation-based gender biases.
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Artale, Alessandro; Jung, Jean Christoph; Mazzullo, Andrea; Ozaki, Ana & Wolter, Frank
(2023).
Living without Beth and Craig: Definitions and Interpolants in Description and Modal Logics with Nominals and Role Inclusions.
ACM Transactions on Computational Logic.
ISSN 1529-3785.
24(4).
doi:
10.1145/3597301.
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The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating im- plicit definability is valid. Thus, the CIP and PBDP reduce potentially hard existence problems to entailment in the underlying logic. Description (and modal) logics with nominals and/or role inclusions do not enjoy the CIP nor the PBDP, but interpolants and explicit definitions have many applications, in particular in con- cept learning, ontology engineering, and ontology-based data management. In this article, we show that, even without Beth and Craig, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as ALCO, ALCH , and ALCH OI and corresponding hybrid modal logics. However, living without Beth and Craig makes these problems harder than entailment: the ex- istence problems become 2ExpTime-complete in the presence of an ontology or the universal modality, and coNExpTime-complete otherwise. We also analyze explicit definition existence if all symbols (except the one that is defined) are admitted in the definition. In this case, the complexity depends on whether one considers individual or concept names. Finally, we consider the problem of computing interpolants and explicit defini- tions if they exist and turn the complexity upper bound proof into an algorithm computing them, at least for description logics with role inclusions.
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Guimaraes, Ricardo; Ozaki, Ana; Persia, Cosimo Damiano & Sertkaya, Bariş
(2023).
Mining EL<sup>⊥</sup> Bases with Adaptable Role Depth.
The journal of artificial intelligence research.
ISSN 1076-9757.
76,
p. 883–924.
doi:
10.1613/JAIR.1.13777.
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In Formal Concept Analysis, a base for a finite structure is a set of implications that characterizes all valid implications of the structure. This notion can be adapted to the context of Description Logic, where the base consists of a set of concept inclusions instead of implications. In this setting, concept expressions can be arbitrarily large. Thus, it is not clear whether a finite base exists and, if so, how large concept expressions may need to be. We first revisit results in the literature for mining ℰℒ⊥ bases from finite interpretations. Those mainly focus on finding a finite base or on fixing the role depth but potentially losing some of the valid concept inclusions with higher role depth. We then present a new strategy for mining ℰℒ⊥ bases which is adaptable in the sense that it can bound the role depth of concepts depending on the local structure of the interpretation. Our strategy guarantees to capture all ℰℒ⊥ concept inclusions holding in the interpretation, not only the ones up to a fixed role depth. We also consider the case of confident ℰℒ⊥ bases, which requires that some proportion of the domain of the interpretation satisfies the base, instead of the whole domain. This case is useful to cope with noisy data.
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Imenes, Anders; Guimaraes, Ricardo & Ozaki, Ana
(2023).
Marrying Query Rewriting and Knowledge Graph Embeddings.
In Fensel, Anna; Ozaki, Ana; Dumitru, Roman & Soylu, Ahmet (Ed.),
Rules and Reasoning - 7th International Joint Conference, RuleML+RR 2023, Oslo, Norway, September 18-20, 2023, Proceedings.
Springer Publishing Company.
ISSN 9783031450716.
p. 126–140.
doi:
10.1007/978-3-031-45072-3_9.
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Dalmonte, Tiziano; Mazzullo, Andrea; Ozaki, Ana & Troquard, Nicolas
(2023).
Non-Normal Modal Description Logics.
In Gaggi, Sara Alice (Eds.),
Logics in Artificial Intelligence - 18th European Conference, JELIA 2023, Dresden, Germany, September 20-22, 2023, Proceedings.
Springer Publishing Company.
ISSN 9783031436185.
p. 306–321.
doi:
10.1007/978-3-031-43619-2_22.
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Guimaraes, Ricardo; Ozaki, Ana & Ribeiro, Jandson S.
(2023).
Finite Based Contraction and Expansion via Models.
In Chen, Yiling & Neville, Jennifer (Ed.),
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence.
AAAI Press.
ISSN 9781577358800.
p. 6389–6397.
doi:
10.1609/AAAI.V37I5.25786.
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We propose a new paradigm for Belief Change in which the new information is represented as sets of models, while the agent's body of knowledge is represented as a finite set of formulae, that is, a finite base. The focus on finiteness is crucial when we consider limited agents and reasoning algorithms. Moreover, having the input as arbitrary set of models is more general than the usual treatment of formulas as input. In this setting, we define new Belief Change operations akin to traditional expansion and contraction, and we identify the rationality postulates that emerge due to the finite representability requirement. We also analyse different logics concerning compatibility with our framework.
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Persia, Cosimo Damiano & Ozaki, Ana
(2022).
Extracting Rules from Neural Networks with Partial Interpretations.
Proceedings of the Northern Lights Deep Learning Workshop.
3.
doi:
10.7557/18.6301.
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We investigate the problem of extracting rules, expressed in Horn logic, from neural network models. Our work is based on the exact learning model, in which a learner interacts with a teacher (the neural network model) via queries in order to learn an abstract target concept, which in our case is a set of Horn rules. We consider partial interpretations to formulate the queries. These can be understood as a representation of the world where part of the knowledge regarding the truthness of propositions is unknown. We employ Angluin’s algorithm for learning Horn rules via queries and evaluate our strategy empirically.
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Artale, Alessandro; Jung, Jean Christoph; Mazzullo, Andrea; Ozaki, Ana & Wolter, Frank
(2021).
Living Without Beth and Craig: Definitions and Interpolants in Description Logics with Nominals and Role Inclusions.
In Leyton-Brown, Kevin & Mausam, Mausam (Ed.),
Proceedings of the 35th National Conference on Artificial Intelligence‚ AAAI 2021 (volume 35 no. 7).
AAAI Press.
ISSN 9781577358664.
p. 6193–6201.
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Artale, Alessandro; Mazzullo, Andrea; Ozaki, Ana & Wolter, Frank
(2021).
On Free Description Logics with Definite Descriptions.
In Bienvenu, Meghyn; Gerhard, Lakemeyer & Erdem, Esra (Ed.),
Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR2021).
International Joint Conferences on Artificial Intelligence Organization (IJCAI).
ISSN 9781956792997.
p. 63–73.
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Guimaraes, Ricardo; Ozaki, Ana; Persia, Cosimo Damiano & Sertkaya, Baris
(2021).
Mining EL Bases with Adaptable Role Depth.
In Leyton-Brown, Kevin & Mausam, Mausam (Ed.),
Proceedings of the 35th National Conference on Artificial Intelligence‚ AAAI 2021 (volume 35 no. 7).
AAAI Press.
ISSN 9781577358664.
p. 6367–6374.
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Artale, Alessandro; Mazzullo, Andrea; Ozaki, Ana & Wolter, Frank
(2020).
On free description logics with definite descriptions.
CEUR Workshop Proceedings.
2663.
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Ozaki, Ana; Persia, Cosimo Damiano & Mazzullo, Andrea
(2020).
Learning query inseparable ELH ontologies.
CEUR Workshop Proceedings.
2663.
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Tahrat, Sabiha; Braun, German; Artale, Alessandro; Gario, Marco & Ozaki, Ana
(2020).
Automated reasoning in temporal DL-lite.
CEUR Workshop Proceedings.
2663.
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Ozaki, Ana
(2020).
Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?
Künstliche Intelligenz.
ISSN 0933-1875.
34(3),
p. 317–327.
doi:
10.1007/s13218-020-00656-9.
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The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.
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Artale, Alessandro; Jung, Jean Christoph; Mazzullo, Andrea; Ozaki, Ana & Wolter, Frank
(2020).
Living without beth and craig: Explicit definitions and interpolants in description logics with nominals.
CEUR Workshop Proceedings.
2663.
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Bourgaux, Camille; Ozaki, Ana; Peñaloza, Rafael & Predoiu, Livia
(2020).
Provenance for the description logic ELH<sup>r</sup>.
CEUR Workshop Proceedings.
2663.
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Ullrich, Hustadt; Ozaki, Ana & Dixon, Clare
(2020).
Theorem Proving for Pointwise Metric Temporal Logic Over the Naturals via Translations.
Journal of automated reasoning.
ISSN 0168-7433.
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Persia, Cosimo & Ozaki, Ana
(2020).
On the Learnability of Possibilistic Theories.
In Bessiere, Christian (Eds.),
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020.
International Joint Conferences on Artificial Intelligence.
ISSN 9780999241165.
p. 1870–1876.
doi:
10.24963/ijcai.2020/259.
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Persia, Cosimo Damiano & Ozaki, Ana
(2020).
Learning query inseparable ELH ontologies.
Proceedings of the AAAI Conference on Artificial Intelligence.
ISSN 2159-5399.
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Ozaki, Ana; Persia, Cosimo & Mazzullo, Andrea
(2020).
Learning Query Inseparable ELH Ontologies.
In Rossi, Francesca (Eds.),
The Thirty-Fourth Conference on Artificial Intelligence, AAAI.
AAAI Press.
ISSN 9781577358237.
p. 2959–2966.
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Bourgaux, Camille; Ozaki, Ana; Peñaloza, Rafael & Predoiu, Livia
(2020).
Provenance for the Description Logic ELHr.
In Bessiere, Christian (Eds.),
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020.
International Joint Conferences on Artificial Intelligence.
ISSN 9780999241165.
p. 1862–1869.
doi:
10.24963/ijcai.2020/258.
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Baader, Franz; Borgwardt, Stefan; Koopmann, Patrick; Ozaki, Ana & Thost, Veronika
(2020).
Metric Temporal Description Logics with Interval-Rigid Names.
ACM Transactions on Computational Logic.
ISSN 1529-3785.
21(4).
doi:
10.1145/3399443.
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Ozaki, Ana & Bourgaux, Camille
(2019).
Querying Attributed DL-Lite Ontologies Using Provenance Semirings,
Proceedings of the AAAI Conference on Artificial Intelligence.
AAAI Press.
ISSN 9781577358091.
p. 2719–2726.
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Ozaki, Ana; Krotzsch, Markus & Rudolph, Sebastian
(2019).
Temporally Attributed Description Logics,
Description Logic, Theory Combination, and All That.
Springer.
ISSN 9783030221010.
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Ozaki, Ana & Troquard, Nicola
(2019).
Learning Ontologies with Epistemic Reasoning: The EL Case.
In Manna, Marco (Eds.),
Logics in Artificial Intelligence - 16th European Conference, JELIA 2019, Rende, Italy, May 7-11, 2019, Proceedings.
Springer.
ISSN 9783030195694.
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Artale, Alessandro; Mazzullo, Andrea & Ozaki, Ana
(2019).
Do You Need Infinite Time?
In Kraus, Sarit (Eds.),
Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, {IJCAI} 2019, Macao, China, August 10-16, 2019.
International Joint Conferences on Artificial Intelligence.
ISSN 9780999241141.
p. 1516–1522.
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Calvanese, Diego; Lanti, Davide; Ozaki, Ana; Peñaloza, Rafael & Xiao, Guohui
(2019).
Enriching Ontology-based Data Access with Provenance.
In Kraus, Sarit (Eds.),
Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, {IJCAI} 2019, Macao, China, August 10-16, 2019.
International Joint Conferences on Artificial Intelligence.
ISSN 9780999241141.
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Ozaki, Ana & Hermo, Montserrat
(2018).
Exact learning of multivalued dependency formulas.
Theoretical Computer Science.
ISSN 0304-3975.
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Konev, Boris; Lutz, Carsten; Ozaki, Ana & Wolter, Frank
(2018).
Exact Learning of Lightweight Description Logic Ontologies.
Journal of machine learning research.
ISSN 1532-4435.
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Krotzsch, Markus; Maximilian, Marx; Ozaki, Ana & Thost, Veronika
(2018).
Attributed Description Logics: Reasoning on Knowledge Graphs.
In Lang:, Jérôme (Eds.),
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden.
ijcai.org.
ISSN 9780999241127.
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Duarte, Ricardo; Konev, Boris & Ozaki, Ana
(2018).
ExactLearner: A Tool for Exact Learning of EL Ontologies.
In Thielscher, Michael (Eds.),
Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona, 30 October - 2 November 2018.
AAAI Press.
ISSN 9781577358039.
Full text in Research Archive
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Ozaki, Ana & Peñaloza, Rafael
(2018).
Consequence-Based Axiom Pinpointing,
Scalable Uncertainty Management - 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings.
Springer.
ISSN 9783030004606.
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Carral, David; Krotzsch, Markus; Maximilian, Marx; Ozaki, Ana & Rudolph, Sebastian
(2018).
Preserving Constraints with the Stable Chase.
In Kimelfeld, Benny (Eds.),
21st International Conference on Database Theory, ICDT 2018, March 26-29, 2018, Vienna, Austria.
Schloss Dagstuhl - Leibniz-Zentrum für Informatik.
ISSN 9783959770637.
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Baader, Franz; Borgwardt, Stefan; Koopmann, Patrick; Ozaki, Ana & Thost, Veronika
(2017).
Metric Temporal Description Logics with Interval-Rigid Names,
Frontiers of Combining Systems - 11th International Symposium, FroCoS 2017, Brasília, Brazil, September 27-29, 2017, Proceedings.
Springer.
ISSN 9783319661667.
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Krotzsch, Markus; Maximilian, Marx; Ozaki, Ana & Thost, Veronika
(2017).
Attributed Description Logics: Ontologies for Knowledge Graphs.
In D'Amato, C; Fernandez, M; Tamma, V; Lecue, V; Cudré-Mauroux, Philippe; Sequeda, J; Lange, C & Heflin, J (Ed.),
The Semantic Web – ISWC 2017. 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings.
Springer.
ISSN 9783319682037.
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Hustadt, Ullrich; Ozaki, Ana & Dixon, Clare
(2017).
Theorem Proving for Metric Temporal Logic over the Naturals.
In Moura, Leonardo de (Eds.),
Automated Deduction - CADE 26 - 26th International Conference on Automated Deduction, Gothenburg, Sweden, August 6-11, 2017, Proceedings.
Springer.
ISSN 9783319630458.
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Basulto, Víctor; Jung, jean & Ozaki, Ana
(2016).
On Metric Temporal Description Logics.
In Kaminka, Gal (Eds.),
ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016).
IOS Press.
ISSN 9781614996712.
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Ozaki, Ana; Konev, Boris & Wolter, Frank
(2016).
A Model for Learning Description Logic Ontologies Based on Exact Learning.
In Schuurmans, Dale (Eds.),
Proceedings of the 30th National Conference on Artificial Intelligence‚ AAAI 2016.
AAAI Press.
ISSN 9781577357605.
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Hernich, André; Lutz, Carsten; Ozaki, Ana & Wolter, Frank
(2015).
Schema.org as a Description Logi.
In Wooldridge, Michael (Eds.),
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015.
AAAI Press.
ISSN 9781577357384.
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Hermo, Montserrat & Ozaki, Ana
(2015).
Exact Learning of Multivalued Dependencies.
In Kamalika, Chaudhuri (Eds.),
Algorithmic Learning Theory - 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings.
Springer.
ISSN 9783319244853.
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Konev, Boris; Lutz, Carsten; Ozaki, Ana & Wolter, Frank
(2014).
Exact Learning of Lightweight Description Logic Ontologies.
In Baral, Chitta (Eds.),
Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, KR 2014, Vienna, Austria, July 20-24, 2014.
AAAI Press.
ISSN 9781577356578.
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Fensel, Anna; Ozaki, Ana; Dumitru, Roman & Soylu, Ahmet
(2023).
Rules and Reasoning - 7th International Joint Conference, RuleML+RR 2023, Oslo, Norway, September 18-20, 2023, Proceedings.
Springer Publishing Company.
ISBN 9783031450716.
269 p.
doi:
10.1007/978-3-031-45072-3.
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Bourgaux, Camille; Ozaki, Ana & Peñaloza, Rafael
(2022).
International Research School in Artificial Intelligence in Bergen,
{AIB} 2022, June 7-11, 2022, University of Bergen, Norway.
Dagstuhl Publishing.
ISBN 9783959772280.
200 p.
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Muñoz-Velasco, Emilio; Ozaki, Ana & Theobald, Martin
(2020).
27th International Symposium on Temporal Representation and Reasoning, TIME 2020, September 23-25, 2020, Bozen-Bolzano, Italy.
Schloss Dagstuhl - Leibniz-Zentrum für Informatik.
ISBN 9783959771672.
292 p.
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Castillo, Ana Ozaki Rivera; Confalonieri, Roberto; Guimarães, Ricardo & Imenes, Anders
(2025).
Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Study Case on BERT-based Language Models.
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Wesenberg, Grunde Haraldsson & Castillo, Ana Helena Ozaki Rivera
(2024).
On Road Traffic Prediction with Graph Neural Networks.
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Castillo, Ana Helena Ozaki Rivera; Guimarães, Ricardo & Lacerda, Victor
(2024).
FaithEL: Strongly Faithful Knowledge Base Embeddings for ELH.
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Bourgaux, Camille; Guimarães, Ricardo; Koudijs, Raoul; Lacerda, Victor & Castillo, Ana Ozaki Rivera
(2024).
Knowledge Base Embeddings: Semantics and Theoretical Properties.
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Castillo, Ana Helena Ozaki Rivera
(2023).
Extracting Rules from Machine Learning Models in Angluin’s Style.
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Show summary
We first study Angluin’s exact learning framework and Valiant's probably approximately correct (PAC) learning framework, from computational learning theory. We discuss how results from the exact framework can be transferred to the PAC framework. Then, we see an overview of recent approaches to extract simpler abstractions of complex neural networks in Angluin's style. The aim of constructing such abstractions is to obtain high level information from machine learning models, which can be useful to interpret their behavior, detect harmful biases, among others. Finally, we discuss algorithms for learning logical theories in Angluin’s framework, in particular, those for learning rules in Horn logic.
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Przybysz, Emilia; Bhattarai, Bimal; Persia, Cosimo Damiano; Ozaki, Ana; Granmo, Ole-Christoffer & Sharma, Jivitesh
(2023).
Verifying Properties of Tsetlin Machines.
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Guimarães, Ricardo & Castillo, Ana Helena Ozaki Rivera
(2022).
Reasoning in Knowledge Graphs.
In Bourgaux, Camille; Ozaki, Ana & Peñaloza, Rafael (Ed.),
International Research School in Artificial Intelligence in Bergen,
{AIB} 2022, June 7-11, 2022, University of Bergen, Norway.
Dagstuhl Publishing.
ISSN 9783959772280.
doi:
10.4230/OASIcs.AIB.2022.2.
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Published
July 10, 2023 9:59 AM
- Last modified
Sep. 9, 2026 9:55 AM
