Interpreting network formalisms

AbstractIn a recent paper, Reiter and Criscuolo [3] remark “that (semantic) networks are notational variants of logical formulae is by now a truism in Artificial Intelligence circles”. Shamelessly exploiting the foregoing quote as a pretext, I attempt to sketch adequate semantic accounts for at least two (kinds of) semantic network formalisms; one, based on the notion of inheritance, one, not. A crucial condition of adequacy to be satisfied is fidelity to some of the intuitions of the creators of the formalisms

Meinongian Semantics and Artificial Intelligence

This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semantic-network semantics. In particular, propositional semantic networks (exemplified by SNePS) are discussed; it is argued that only a fully intensional, Meinongian semantics is appropriate for them; and several Meinongian systems are presented

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

Philosophical logics - a survey and a bibliography

Intensional logics attract the attention of researchers from differing academic backgrounds and various scientific interests. My aim is to sketch the philosophical background of alethic, doxastic, and deontic logics, their formal and metaphysical presumptions and their various problems and paradoxes, without attempting formal rigor. A bibliography, concise on philosophical writings, is meant to allow the reader\u27s access to the maze of literature in the field