Pasquale Minervini

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Service Oriented Architecture (SOA) is a paradigm for organising and using distributed services that may be under the control of different ownership domains and implemented using various technology stacks. In some contexts, an organisation using an IT infrastructure implementing the SOA paradigm can take a great benefit from the integration, in its business(More)
In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances. However, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the IS-A relation needs to be consistent with the transitivity property: if IS-A(x1,(More)
We focus on the problem of predicting missing class memberships and property assertions in Web Ontologies. We start from the assumption that related entities influence each other, and they may be either similar or dissimilar with respect to a given set of properties: the former case is referred to as homophily, and the latter as heterophily. We present an(More)
In this work, we introduce a convolutional neural network model, ConvE, for the task of link prediction. ConvE applies 2D convolution directly on embeddings, thus inducing spatial structure in embedding space. To scale to large knowledge graphs and prevent overfitting due to over-parametrization, previous work seeks to reduce parameters by performing simple(More)
Knowledge Graphs are a widely used formalism for representing knowledge in the Web of Data. We focus on the problem of predicting missing links in large knowledge graphs, so to discover new facts about the world. Recently, representation learning models that embed entities and predicates in continuous vector spaces achieved new state-of-the-art results on(More)
In global software projects work takes place over long distances, meaning that communication will often involve distant cultures with different languages and communication styles that, in turn, exacerbate communication problems. However, being aware of cultural distance is not sufficient to overcome many of the barriers that language differences bring in(More)
Knowledge available through Semantic Web standards can be missing, generally because of the adoption of the Open World Assumption. We present a Statistical Relational Learning system for learning terminological naïve Bayesian classifiers, which estimate the probability that an individual belongs to a target concept given its membership to a set of(More)
Knowledge Graphs (KGs) are a widely used formalism for representing knowledge in the Web of Data. We focus on the problem of link prediction, i.e. predicting missing links in large knowledge graphs, so to discover new facts about the world. Representation learning models that embed entities and relation types in continuous vector spaces recently were used(More)
Abstract. The increasing availability of structured machine-processable knowledge in the context of the Semantic Web, allows for inductive methods to back and complement purely deductive reasoning in tasks where the latter may fall short. This work proposes a new method for similarity-based class-membership prediction in this context. The underlying idea is(More)
We consider the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We first identify which relations tend to link similar individuals by means of a finite-set Gaussian Process regression model, and then efficiently propagate knowledge about individuals across their relations. Our experimental(More)