A Semantic Similarity Measure for Expressive Description Logics

A totally semantic measure is presented which is able to calculate a similarity value between concept descriptions and also between concept description and individual or between individuals expressed in an expressive description logic. It is applicable on symbolic descriptions although it uses a numeric approach for the calculus. Considering that Description Logics stand as the theoretic framework for the ontological knowledge representation and reasoning, the proposed measure can be effectively used for agglomerative and divisional clustering task applied to the semantic web domain.Comment: 13 pages, Appeared at CILC 2005, Convegno Italiano di Logica Computazionale also available at http://www.disp.uniroma2.it/CILC2005/downloads/papers/15.dAmato_CILC05.pd

Empowering Knowledge Bases: a Machine Learning Perspective

The construction of Knowledge Bases requires quite often the intervention of knowledge engineering and domain experts, resulting in a time consuming task. Alternative approaches have been developed for building knowledge bases from existing sources of information such as web pages and crowdsourcing; seminal examples are NELL, DBPedia, YAGO and several others. With the goal of building very large sources of knowledge, as recently for the case of Knowledge Graphs, even more complex integration processes have been set up, involving multiple sources of information, human expert intervention, crowdsourcing. Despite signi - cant e orts for making Knowledge Graphs as comprehensive and reliable as possible, they tend to su er of incompleteness and noise, due to the complex building process. Nevertheless, even for highly human curated knowledge bases, cases of incompleteness can be found, for instance with disjointness axioms missing quite often. Machine learning methods have been proposed with the purpose of re ning, enriching, completing and possibly raising potential issues in existing knowledge bases while showing the ability to cope with noise. The talk will concentrate on classes of mostly symbol-based machine learning methods, speci cally focusing on concept learning, rule learning and disjointness axioms learning problems, showing how the developed methods can be exploited for enriching existing knowledge bases. During the talk it will be highlighted as, a key element of the illustrated solutions, is represented by the integration of: background knowledge, deductive reasoning and the evidence coming from the mass of the data. The last part of the talk will be devoted to the presentation of an approach for injecting background knowledge into numeric-based embedding models to be used for predictive tasks on Knowledge Graphs

New genius-entrepreneurs: Itinerary and trajectories of university educational excellence

The purpose of the present work is to rethink, in the university context, the concept of genius, related to the high intellectual abilities associated with intelligence; also, to connect the idea of entrepreneurial competences, such as leadership or social commitment. The hypothesis is that a university genius is defined by his high creative abilities and, in particular, entrepreneurial ones. From the methodological point of view, the recommendations of the National Association for Gifted Children were followed, and evidence collection was based on such practices, using the results obtained by two studies: the first one with professors and postgraduate students (from Argentina and Spain, from hard and soft sciences) who responded to a conceptual questionnaire, previously validated, in order to delineate common minimum denominators of geniuses. The other one comes from analyzing the results of an acceleration program of entrepreneurial competence with undergraduate students. Combining both data resulted in the need to think in an educational proposal (itinerary) with trajectories of excellence. One during the Degree level, with pilot training activities (in entrepreneurial competence), experimenting on a small scale; and the other in the Postgraduate level, encouraging them to be architects of their “routes”, allowing them to self-employ and to become agents of socio-community change

New genius-entrepreneurs: Itinerary and trajectories of university educational excellence

The purpose of the present work is to rethink, in the university context, the concept of genius, related to the high intellectual abilities associated with intelligence; also, to connect the idea of entrepreneurial competences, such as leadership or social commitment. The hypothesis is that a university genius is defined by his high creative abilities and, in particular, entrepreneurial ones. From the methodological point of view, the recommendations of the National Association for Gifted Children were followed, and evidence collection was based on such practices, using the results obtained by two studies: the first one with professors and postgraduate students (from Argentina and Spain, from hard and soft sciences) who responded to a conceptual questionnaire, previously validated, in order to delineate common minimum denominators of geniuses. The other one comes from analyzing the results of an acceleration program of entrepreneurial competence with undergraduate students. Combining both data resulted in the need to think in an educational proposal (itinerary) with trajectories of excellence. One during the Degree level, with pilot training activities (in entrepreneurial competence), experimenting on a small scale; and the other in the Postgraduate level, encouraging them to be architects of their “routes”, allowing them to self-employ and to become agents of socio-community change

Ontology Enrichment by Discovering Multi-Relational Association Rules from Ontological Knowledge Bases

International audienceIn the Semantic Web context, OWL ontologies represent the con-ceptualization of domains of interest while the corresponding as-sertional knowledge is given by the heterogeneous Web resources referring to them. Being strongly decoupled, ontologies and assertion can be out-of-sync. An ontology can be incomplete, noisy and sometimes inconsistent with regard to the actual usage of its conceptual vocabulary in the assertions. Data mining can support the discovery of hidden knowledge patterns in the data, to enrich the ontologies. We present a method for discovering multi-relational association rules, coded in SWRL, from ontological knowledge bases. Unlike state-of-the-art approaches, the method is able to take the intensional knowledge into account. Furthermore, since discovered rules are represented in SWRL, they can be straightforwardly integrated within the ontology, thus (i) enriching its expressive power and (ii) augmenting the assertional knowledge that can be derived. Discovered rules may also suggest new axioms to be added to the ontology. We performed experiments on publicly available ontologies validating the performances of our approach

Enhancement of Dopaminergic Differentiation in Proliferating Midbrain Neuroblasts by Sonic Hedgehog and Ascorbic Acid

We analyzed the molecular mechanisms involved in the acquisition and maturation of dopaminergic (DA) neurons generated in vitro from rat ventral mesencephalon (MES) cells in the presence of mitogens or specific signaling molecules. The addition of basic fibroblast growth factor (bFGF) to MES cells in serum-free medium stimulates the proliferation of neuroblasts but delays DA differentiation. Recombinant Sonic hedgehog (SHH) protein increases up to three fold the number of tyrosine hydroxylase (TH)-positive cells and their differentiation, an effect abolished by anti-SHH antibodies. The expanded cultures are rich in nestin-positive neurons, glial cells are rare, all TH+ neurons are DA, and all DA and GABAergic markers analyzed are expressed. Adding ascorbic acid to bFGF/SHH-treated cultures resulted in a further five- to seven-fold enhancement of viable DA neurons. This experimental system also provides a powerful tool to generate DA neurons from single embryos. Our strategy provides an enriched source of MES DA neurons that are useful for analyzing molecular mechanisms controlling their function and for experimental regenerative approaches in DA dysfunction

Constructing Metrics for Evaluating Multi-Relational Association Rules in the Semantic Web from Metrics for Scoring Association Rules

International audienceWe propose a method to construct asymmetric metrics for evaluating the quality of multi-relational association rules coded in the form of SWRL rules. These metrics are derived from metrics for scoring association rules. We use each constructed metric as a fitness function for evolutionary inductive programming employed to discover hidden knowledge patterns (represented in SWRL) from assertional data of ontological knowledge bases. This new knowledge can be integrated easily within the ontology to enrich it. In addition, we also carry out a search for the best metric to score candidate multi-relational association rules in the evolutionary approach by experiment. We performed experiments on three publicly available ontologies validating the performances of our approach and comparing them with the main state-of-the-art systems

Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields

ter Horst H, Hartung M, Cimiano P. Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields. In: d'Amato C, Theobald M, eds. Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. Lecture Notes in Computer Science. Vol 11078. Springer; 2018: 78-109.In this tutorial we discuss how Conditional Random Fields can be applied to knowledge base population tasks. We are in particular interested in the cold-start setting which assumes as given an ontology that models classes and properties relevant for the domain of interest, and an empty knowledge base that needs to be populated from unstructured text. More specifically, cold-start knowledge base population consists in predicting semantic structures from an input document that instantiate classes and properties as defined in the ontology. Considering knowledge base population as structure prediction, we frame the task as a statistical inference problem which aims at predicting the most likely assignment to a set of ontologically grounded output variables given an input document. In order to model the conditional distribution of these output variables given the input variables derived from the text, we follow the approach adopted in Conditional Random Fields. We decompose the cold-start knowledge base population task into the specific problems of entity recognition, entity linking and slot-filling, and show how they can be modeled using Conditional Random Fields