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.
