  Artificial Intelligence federates numerous scientific fields in the aim of
developing machines able to assist human operators performing complex
treatments -- most of which demand high cognitive skills (e.g. learning or
decision processes). Central to this quest is to give machines the ability to
estimate the likeness or similarity between things in the way human beings
estimate the similarity between stimuli.
  In this context, this book focuses on semantic measures: approaches designed
for comparing semantic entities such as units of language, e.g. words,
sentences, or concepts and instances defined into knowledge bases. The aim of
these measures is to assess the similarity or relatedness of such semantic
entities by taking into account their semantics, i.e. their meaning --
intuitively, the words tea and coffee, which both refer to stimulating
beverage, will be estimated to be more semantically similar than the words
toffee (confection) and coffee, despite that the last pair has a higher
syntactic similarity. The two state-of-the-art approaches for estimating and
quantifying semantic similarities/relatedness of semantic entities are
presented in detail: the first one relies on corpora analysis and is based on
Natural Language Processing techniques and semantic models while the second is
based on more or less formal, computer-readable and workable forms of knowledge
such as semantic networks, thesaurus or ontologies. (...) Beyond a simple
inventory and categorization of existing measures, the aim of this monograph is
to convey novices as well as researchers of these domains towards a better
understanding of semantic similarity estimation and more generally semantic
measures.
