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
