Discovering New Sentiments from the Social Web

A persistent challenge in Complex Systems (CS) research is the phenomenological reconstruction of systems from raw data. In order to face the problem, the use of sound features to reason on the system from data processing is a key step. In the specific case of complex societal systems, sentiment analysis allows to mirror (part of) the affective dimension. However it is not reasonable to think that individual sentiment categorization can encompass the new affective phenomena in digital social networks. The present papers addresses the problem of isolating sentiment concepts which emerge in social networks. In an analogy to Artificial Intelligent Singularity, we propose the study and analysis of these new complex sentiment structures and how they are similar to or diverge from classic conceptual structures associated to sentiment lexicons. The conjecture is that it is highly probable that hypercomplex sentiment structures -not explained with human categorizations- emerge from high dynamic social information networks. Roughly speaking, new sentiment can emerge from the new global nervous systems as it occurs in humans

Algebraic combinatorics in bounded induction

In this paper, new methods for analyzing models of weak subsystems of Peano Arithmetic are proposed. The focus will be on the study of algebro-combinatoric properties of certain definable cuts. Their relationship with segments that satisfy more induction, with those limited by the standard powers/roots of an element, and also with definable sets in Bounded Induction is studied. As a consequence, some considerations on the Π1-interpretability of IΔ0 in weak theories, as well as some alternative axiomatizations, are reviewed. Some of the results of the paper are obtained by immersing Bounded Induction models in its Stone-Cech Compactification, once it is endowed with a topology.Ministerio de Ciencia, Innovación y Universidades PID2019-109152GB-I0

Logic Negation with Spiking Neural P Systems

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing.Comment: 25 pages, 1 figur

Explainable Artificial Intelligence in Data Science: From Foundational Issues Towards Socio-technical Considerations

A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already afects many sensitive realms (as in Politics, Social Sciences, or Law). The decision making power handover to opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solu tions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientifc and humanistic areas. The frst aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is benefciated from part of the scientifc and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the feld of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justifcation and the explainer-explainee agreement.Agencia Estatal de Investigación PID2019-109152GB-I00/AEI/10.13039/50110001103

Extension of Ontologies Assisted by Automated Reasoning Systems

A method to extend ontologies with the assistance of automated reasoning systems and preserving a kind of completeness with respect to their associate conceptualizations is presented. The use of such systems makes feasible the ontological insertion of new concepts, but it is necessary to re-interpret the older ones with respect to new ontological commitments.We illustrate the method extending a well-known ontology about spatial relationships, the called Region Connection Calculus.Ministerio de Educación y Ciencia TIN2004-0388

Mereotopological Analysis of Formal Concepts in Security Ontologies

In this paper an analysis of security ontologies, using an mereotopological interpretation of the relationship amongst their classes, based on the entailment in the ontology, is presented. The analysis is carried out by means of a graphical tool (called Paella) that implements such an interpretation and it can suggest the potential debugging of anomalies. The analysis also suggests how to interpret the representational anomalies.Ministerio de Ciencia e Innovación TIN2009-0949

A Formal Foundation for Knowledge Integration of Defficent Information in the Semantic Web

Maintenance of logical robustness in Information Integration represents a major challenge in the envisioned Semantic Web. In this framework, it is previsible unprecise information (with respect to an ontology) is retrieved from some resources. The sound integration of such information is crucial to achieve logical soundness. We present a data-driven approach to classify that knowledge by means of the cognitive entropy of the possible robust ontology extensions and data.Ministerio de Educación y Ciencia TIN2004-0388

Reconciling Knowledge in Social Tagging Web Services

Sometimes we want to search for new information about topics but we can not find relevant results using our own knowledge (for example, our personal bookmarks). A potential solution could be the use of knowledge from other users to find what we are searching for. This solution implies that we can achieve some agreement on implicit semantics used by the other users. We call it Reconciliation of Knowledge. The aim of this paper is to show an agent-based method which lets us reconcile two different knowledge basis (associated with tagging systems) into a common language, obtaining a new one that allows the reconcilitiation of (part of) this knowledge. The agents use Formal Concept Analysis concepts and tools and it has been implemented on the JADE multiagent platform.Ministerio de Ciencia e Innovación TIN2009-0949

On the Use of Automated Reasoning Systems in Ontology Integration.

Ontology Integration is a challenge in the field of Knowledge Engineering, whose solution is indispensable for the envisioned Semantic Web. Some approximations suffer from logical confidence, and others are hard to mechanize. In this paper a method – assisted by Automated Reasoning Systems – to solve a subproblem, the merging of ontologies, is presented. A case study of application is drawn from the field of Qualitative Spatial Reasoning.Junta de Andalucía Minerva Services in Mobility Platform Project WeTeVe (2C/040

Semantics for incident identification and resolution reports

In order to achieve a safe and systematic treatment of security protocols, organizations release a number of technical briefings describing how to detect and manage security incidents. A critical issue is that this document set may suffer from semantic deficiencies, mainly due to ambiguity or different granularity levels of description and analysis. An approach to face this problem is the use of semantic methodologies in order to provide better Knowledge Externalization from incident protocols management. In this article, we propose a method based on semantic techniques for both, analyzing and specifying (meta)security requirements on protocols used for solving security incidents. This would allow specialist getting better documentation on their intangible knowledge about them.Ministerio de Economía y Competitividad TIN2013-41086-