Learning from Ontology Streams with Semantic Concept Drift

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing

Molecular mechanisms underlying cannabis abuse and schizophrenia: Focus on 5-HT2A receptors and Akt/mTOR signaling pathway

267 p.Schizophrenia is a chronic and disabling mental illness that affects around 20 million people worldwide. The etiology of the disorder is multifactorial, and different genetic and environmental factors take part in its onset and course. However, the mechanisms underlying this interaction remain poorly understood. Cannabis abuse, especially during adolescence, has been associated with an increased risk of developing schizophrenia as well as with an earlier onset. The main aim of this Thesis consisted in evaluating the molecular mechanisms underlying this relationship, with a focus in two targets previously related with schizophrenia: serotonin 2A receptors (5-HT2AR) and Akt/mTOR signaling pathway. For this purpose, we evaluated (1) the G¿ protein subunits activation exerted by three cannabinoids, including THC in mouse brain cortex, (2) chronic THC effects on psychosis-like states, cortical 5-HT2AR functionality and Akt/mTOR signaling pathway status, (3) the implication of Akt/mTOR signaling pathway in these effects, (4) the Akt/mTOR signaling pathway status in postmortem prefrontal cortex (PFC) of subjects with schizophrenia, and (5) the 5-HT2AR protein expression and Akt functional status in platelets from subjects with a cannabis use disorder, with and without schizophrenia. Most significant results from this Thesis show that chronic THC leads to hyperactive 5-HT2AR functionality in the brain cortex associated with a hyperactive Akt/mTOR signaling and psychosis-like behavior. Disruption of this signaling pathway is also evident in postmortem PFC and platelets of subjects with schizophrenia, and cannabis abuse seems to exert different effects depending on the presence of schizophrenia pathology. Together, this Doctoral Thesis suggests that 5-HT2AR and Akt/mTOR pathway are elements of an interacting mechanism involving chronic cannabis pharmacological effects and schizophrenia pathogenesis

Knowledge-based Transfer Learning Explanation

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.Comment: Accepted by International Conference on Principles of Knowledge Representation and Reasoning, 201

Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions

Similarity functions measure how comparable pairs of elements are, and play a key role in a wide variety of applications, e.g., notions of Individual Fairness abiding by the seminal paradigm of Dwork et al., as well as Clustering problems. However, access to an accurate similarity function should not always be considered guaranteed, and this point was even raised by Dwork et al. For instance, it is reasonable to assume that when the elements to be compared are produced by different distributions, or in other words belong to different ``demographic'' groups, knowledge of their true similarity might be very difficult to obtain. In this work, we present an efficient sampling framework that learns these across-groups similarity functions, using only a limited amount of experts' feedback. We show analytical results with rigorous theoretical bounds, and empirically validate our algorithms via a large suite of experiments.Comment: Accepted at NeurIPS 202

Using SPICES for a better service consumption

In this poster we present SPICES (Semantic Platform for the Interaction and Consumption of Enriched Services), a Web based tool that automates the process of consuming a Web service by making use of the semantic annotations that describe them. SPICES supports both traditional WSDL services and RESTful ones and offers end-users the possibility of interacting with them in an easy yet personalised manner, without the need of advanced technical skills -which were traditionally required-, being the complexity that lies underneath hidden to them. SPICES is being developed within the European project SOA4All