Papers by Maria Vanina Martinez
European Conference on Artificial Intelligence, Aug 18, 2014
Reasoning about an entity's preferences (be it a user of an application, an individual targeted f... more Reasoning about an entity's preferences (be it a user of an application, an individual targeted for marketing, or a group of people whose choices are of interest) has a long history in different areas of study. In this paper, we adopt the point of view that grows out of the intersection of databases and knowledge representation, where preferences are usually represented as strict partial orders over the set of tuples in a database or the consequences of a knowledge base. We introduce probabilistic preference logic networks (PPLNs), which flexibly combine such preferences with probabilistic uncertainty. Their applications are clear in domains such as the Social Semantic Web, where users often express preferences in an incomplete manner and through different means, many times in contradiction with each other. We show that the basic problems associated with reasoning with PPLNs (computing the probability of a world or a given query) are #P-hard, and then explore ways to make these computations tractable by: (i) leveraging results from order theory to obtain a polynomial-time randomized approximation scheme (FPRAS) under fixed-parameter assumptions; and (ii) studying a fragment of the language of PPLNs for which exact computations can be performed in fixed-parameter polynomial time.
Future Generation Computer Systems, Sep 1, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
SpringerBriefs in computer science, 2017
Ontologies are logical theories that describe a formal conceptualization in a domain of interest.... more Ontologies are logical theories that describe a formal conceptualization in a domain of interest. Conceptualizations are intensional semantic structures that codify implicit knowledge that restricts the structure of a part of the domain. Usually, this specification is expressed explicitly in a (declarative) language. This formalization makes the knowledge available for machine processing, facilitating in this way its interchange. For this reason, in the last years, ontologies have been of increasing interest in many and diverse applications, especially in the context of artificial intelligence (AI), the Semantic Web, and data integration.
Online Social Networks and Media, Nov 1, 2022
Proceedings of the AAAI Conference on Artificial Intelligence
Querying inconsistent ontologies is an intriguing new problem that gave rise to a flourishing res... more Querying inconsistent ontologies is an intriguing new problem that gave rise to a flourishing research activity in the description logic (DL) community. The computational complexity of consistent query answering under the main DLs is rather well understood; however, little is known about existential rules. The goal of the current work is to perform an in-depth analysis of the complexity of consistent query answering under the main decidable classes of existential rules enriched with negative constraints. Our investigation focuses on one of the most prominent inconsistency-tolerant semantics, namely, the AR semantics. We establish a generic complexity result, which demonstrates the tight connection between classical and consistent query answering. This result allows us to obtain in a uniform way a relatively complete picture of the complexity of our problem.
Future Generation Computer Systems, 2021
Among the wide variety of malicious behavior commonly observed in modern social platforms, one of... more Among the wide variety of malicious behavior commonly observed in modern social platforms, one of the most notorious is the diffusion of fake news, given its potential to influence the opinions of millions of people who can be voters, consumers, or simply citizens going about their daily lives. In this paper, we implement and carry out an empirical evaluation of a version of the recently-proposed NetDER architecture for hybrid AI decision-support systems with the capability of leveraging the availability of machine learning modules, logical reasoning about unknown objects, and forecasts based on diffusion processes. NetDER is a general architecture for reasoning about different kinds of malicious behavior such as dissemination of fake news, hate speech, and malware, detection of botnet operations, prevention of cyber attacks including those targeting software products or blockchain transactions, among others. Here, we focus on the case of fake news dissemination on social platforms by three different kinds of users: non-malicious, malicious, and botnet members. In particular, we focus on three tasks: (i) determining who is responsible for posting a fake news article, (ii) detecting malicious users, and (iii) detecting which users belong to a botnet designed to disseminate fake news. Given the difficulty of obtaining adequate data with ground truth, we also develop a testbed that combines real-world fake news datasets with synthetically generated networks of users and fully-detailed traces of their behavior throughout a series of time points. We designed our testbed to be customizable for different problem sizes and settings, and make its code publicly available to be used in similar evaluation efforts. Finally, we report on the results of a thorough experimental evaluation of three variants of our model and six environmental settings over the three tasks. Our results clearly show the effects that the quality of knowledge engineering tasks, the quality of the underlying machine learning classifier used to detect fake news, and the specific environmental conditions have on smart policing efforts in social platforms.Fil: Paredes, José Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin
Ontology-Based Data Access Leveraging Subjective Reports, 2017
The author of the book provided the below additional reference and its respective citation for Ch... more The author of the book provided the below additional reference and its respective citation for Chapter 1 after the book is published. This has now been updated in the respective chapter in the revised version of the book. O. Tifrea-Marciuska, Personalised search for the social semantic web.
Information Sciences, 2021
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Artificial Intelligence, 2019
The problem of knowledge evolution has received considerable attention over the years. Mainly, th... more The problem of knowledge evolution has received considerable attention over the years. Mainly, the study of the dynamics of knowledge has been addressed in the area of Belief Revision, a field emerging as the convergence of the efforts in Philosophy, Logic, and more recently Computer Science, where research efforts usually involve "flat" knowledge bases where there is no additional information about the formulas stored in it. Even when this may be a good fit for particular applications, in many real-world scenarios different information items may be attached to formulas. For instance, when the reliability of the source of the piece of information is attached to it as a measure of some quality (v.g., strength) of the piece of information itself, or when some characteristic informs us on the desirability of the item (v.g., the potential benefit that could be obtained from it). If this type of information is available, we can use it to guide how the belief base is to be modified when new information arrives. In this work, we present a novel approach to the contraction of knowledge bases where formulas have values attached that measure some quality linked to those formulas, exploiting it to define their desirability, and uses such desirability to define which formulas need to be removed to solve conflicts. In this context, we introduce a set of properties for contraction operators by extending classic approaches. We also show how the local treatment of minimal conflicts can induce some counter-intuitive contractions, and we present a way to avoid them by considering optimal resolutions of conflicts using the additional information encoded. We show how the proposed formalization captures any contraction that is optimal under a set of features. Finally, we present a refinement based on the identification of related minimal conflicts that performs contraction in optimal ways without looking into the entire knowledge base. The approach is based on the use of the accrual of beliefs where several formulas collaboratively use their respective values to prevail in the resolution of conflicts.
Annals of Mathematics and Artificial Intelligence, 2016
The concept of incoherence naturally arises in ontological settings, specially when integrating k... more The concept of incoherence naturally arises in ontological settings, specially when integrating knowledge. In this work we study a notion of incoherence for Datalog ± ontologies based on the definition of satisfiability of a set of existential rules regarding the set of integrity constraints in a Datalog ± ontology. We show how classical inconsistency-tolerant semantics for query answering behaves when dealing with atoms that are relevant to unsatisfiable sets of existential rules, which may hamper the quality of answers-even under inconsistency-tolerant semantics, which is expected as they were not designed to confront such issues. Finally, we propose a notion of incoherency-tolerant semantics for query answering in Datalog ± , and present a particular one based on the transformation of classic Datalog ± ontologies into defeasible Datalog ± ones, which use argumentation as its reasoning machinery.
En losúltimos tiempos, la colaboración y el intercambio de información se han vuelto aspectos cru... more En losúltimos tiempos, la colaboración y el intercambio de información se han vuelto aspectos cruciales de muchos sistemas. En estos entornos es de vital importancia definir métodos automáticos para resolver conflictos entre el conocimiento compartido por distintos sistemas. Este conocimiento es frecuentemente expresado a través de ontologías que pueden ser compartidas por los sistemas que utilizan el mismo. En la presente investigación se busca la definición de métodos automáticos de integración de ontologías Datalog+/-. En base a lo logrado en este aspecto se buscará la adaptación del framework desarrollado para su aplicación tanto en la creación de federaciones de Bases de Datos (Data Federation) como en el intercambio de datos (Data Exchange). En estos campos de aplicación estos metodos podrán contribuir brindando la posibilidad de obtener de forma automática un esquema universal que respete tanto como sea posible a los originales manteniendo la coherencia del mismo con respecto a las restricciones de integridad impuestas a los datos, y definiendo que datos pueden ser mantenidos en la federación resolviendo incoherencias en el proceso. Adicionalmente, se analizarán posibles extensiones a Datalog+/-basadas en formalismos de Argumentación Rebatible, teniendo en cuenta aspectos como la definición de relaciones de inferencia para estas ontologías aumentadas que tengan en cuenta los aspectos no-monótonos de la Argumentación Rebatible, o el impacto de tales relaciones en las conclusiones finales obtenidas y la complejidad de la obtención de las mismas.
The tastes of a user can be represented in a natural way by using qualitative preferences. In thi... more The tastes of a user can be represented in a natural way by using qualitative preferences. In this paper, we describe how to combine ontological knowledge with CP-nets to represent preferences in a qualitative way and enriched with domain knowledge. Specifically, we focus on conjunctive query (CQ) answering under CP-net-based preferences. We define k-rank answers to CQs based on the user's preferences encoded in an ontological CP-net and we provide an algorithm for k-rank answering CQs.
Argument & Computation
We present a novel argumentation-based method for finding and analyzing communities in social med... more We present a novel argumentation-based method for finding and analyzing communities in social media on the Web, where a community is regarded as a set of supported opinions that might be in conflict. Based on their stance, we identify argumentative coalitions to define them; then, we apply a similarity-based evaluation method over the set of arguments in the coalition to determine the level of cohesion inherent to each community, classifying them appropriately. Introducing conflict points and attacks between coalitions based on argumentative (dis)similarities to model the interaction between communities leads to considering a meta-argumentation framework where the set of coalitions plays the role of the set of arguments and where the attack relation between the coalitions is assigned a particular strength which is inherited from the arguments belonging to the coalition. Various semantics are introduced to consider attacks’ strength to particularize the effect of the new perspective....
IEEE Access, 2018
With the rapid growth of social tagging systems, many research efforts are being put into persona... more With the rapid growth of social tagging systems, many research efforts are being put into personalized search and recommendation using social tags (i.e., folksonomies). As users can freely choose their own vocabulary, social tags can be very ambiguous (for instance, due to the use of homonyms or synonyms). Machine learning techniques (such as clustering and deep neural networks) are usually applied to overcome this tag ambiguity problem. However, the machine-learning-based solutions always need very powerful computing facilities to train recommendation models from a large amount of data, so they are inappropriate to be used in lightweight recommender systems. In this paper, we propose an ontological similarity to tackle the tag ambiguity problem without the need of model training by using contextual information. The novelty of this ontological similarity is that it first leverages external domain ontologies to disambiguate tag information, and then semantically quantifies the relevance between user and item profiles according to the semantic similarity of the matching concepts of tags in the respective profiles. Our experiments show that the proposed ontological similarity is semantically more accurate than the state-of-the-art similarity metrics, and can thus be applied to improve the performance of content-based tag-aware personalized recommendation on the social web. Consequently, as a model-training-free solution, ontological similarity is a good disambiguation choice for lightweight recommender systems and a complement to machine-learningbased recommendation solutions. INDEX TERMS Folksonomies, ontological similarity, personalized recommendation, social tags.
Semantic web, May 22, 2023
The role of explanations in intelligent systems has in the last few years entered the spotlight a... more The role of explanations in intelligent systems has in the last few years entered the spotlight as AI-based solutions appear in an ever-growing set of applications. Though data-driven (or machine learning) techniques are often used as examples of how opaque (also called black box) approaches can lead to problems such as bias and general lack of explainability and interpretability, in reality these features are difficult to tame in general, even for approaches that are based on tools typically considered to be more amenable, like knowledge-based formalisms. In this paper, we continue a line of research and development towards building tools that facilitate the implementation of explainable and interpretable hybrid intelligent socio-technical systems, focusing on features that users can leverage to build explanations to their queries. In particular, we present the implementation of a recently-proposed application framework (and make available its source code) for developing such systems, and explore user-centered mechanisms for building explanations based both on the kinds of explanations required (such as counterfactual, contextual, etc.) and the inputs used for building them (coming from various sources, such as the knowledge base and lower-level data-driven modules). In order to validate our approach, we develop two use cases, one as a running example for detecting hate speech in social platforms and the other as an extension that also contemplates cyberbullying scenarios.
XXIII Workshop de Investigadores en Ciencias de la Computación (WICC 2021, Chilecito, La Rioja), 2021
arXiv (Cornell University), Aug 2, 2021
With the availability of large datasets and ever-increasing computing power, there has been a gro... more With the availability of large datasets and ever-increasing computing power, there has been a growing use of data-driven artificial intelligence systems, which have shown their potential for successful application in diverse areas. However, many of these systems are not able to provide information about the rationale behind their decisions to their users. Lack of understanding of such decisions can be a major drawback, especially in critical domains such as those related to cybersecurity. In light of this problem, in this paper we make three contributions: (i) proposal and discussion of desiderata for the explanation of outputs generated by AI-based cybersecurity systems; (ii) a comparative analysis of approaches in the literature on Explainable Artificial Intelligence (XAI) under the lens of both our desiderata and further dimensions that are typically used for examining XAI approaches; and (iii) a general architecture that can serve as a roadmap for guiding research efforts towards the development of explainable AI-based cybersecurity systems-at its core, this roadmap proposes combinations of several research lines in a novel way towards tackling the unique challenges that arise in this context.
SpringerBriefs in computer science, 2017
User preferences have been incorporated in both traditional databases and ontology-based query an... more User preferences have been incorporated in both traditional databases and ontology-based query answering mechanisms for some time now. The recent change in the way data is created and consumed in the Social Semantic Web has caused this aspect of query answering to receive more attention, since users play a central role in both knowledge engineering and knowledge consumption.
Over the years, inconsistency management has caught the attention of researchers of different are... more Over the years, inconsistency management has caught the attention of researchers of different areas. Inconsistency is a problem that arises in many different scenarios, for instance, ontology development or knowledge integration. In such settings, it is important to have adequate automatic tools for handling conflicts that may appear in a knowledge base. We introduce an approach to consolidation of belief bases based on a refinement of kernel contraction that accounts for the relation among kernels using clusters instead. We define cluster contraction-based consolidation operators contraction by falsum on a belief base using cluster incision functions, a refinement of kernel incision functions.
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Papers by Maria Vanina Martinez