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This SpringerBrief reviews the knowledge engineering problem of
engineering objectivity in top-k query answering; essentially,
answers must be computed taking into account the user's preferences
and a collection of (subjective) reports provided by other users.
Most assume each report can be seen as a set of scores for a list
of features, its author's preferences among the features, as well
as other information is discussed in this brief. These pieces of
information for every report are then combined, along with the
querying user's preferences and their trust in each report, to rank
the query results. Everyday examples of this setup are the online
reviews that can be found in sites like Amazon, Trip Advisor, and
Yelp, among many others. Throughout this knowledge engineering
effort the authors adopt the Datalog+/- family of ontology
languages as the underlying knowledge representation and reasoning
formalism, and investigate several alternative ways in which
rankings can b e derived, along with algorithms for top-k (atomic)
query answering under these rankings. This SpringerBrief also
investigate assumptions under which our algorithms run in
polynomial time in the data complexity. Since this SpringerBrief
contains a gentle introduction to the main building blocks (OBDA,
Datalog+/-, and reasoning with preferences), it should be of value
to students, researchers, and practitioners who are interested in
the general problem of incorporating user preferences into related
formalisms and tools. Practitioners also interested in using
Ontology-based Data Access to leverage information contained in
reviews of products and services for a better customer experience
will be interested in this brief and researchers working in the
areas of Ontological Languages, Semantic Web, Data Provenance, and
Reasoning with Preferences.
This SpringerBrief proposes a general framework for reasoning about
inconsistency in a wide variety of logics, including inconsistency
resolution methods that have not yet been studied. The proposed
framework allows users to specify preferences on how to resolve
inconsistency when there are multiple ways to do so. This empowers
users to resolve inconsistency in data leveraging both their
detailed knowledge of the data as well as their application needs.
The brief shows that the framework is well-suited to handle
inconsistency in several logics, and provides algorithms to compute
preferred options. Finally, the brief shows that the framework not
only captures several existing works, but also supports reasoning
about inconsistency in several logics for which no such methods
exist today.
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