Preference queries over taxonomic domains

Lecture Notes in Computer Science, 2006

Ranking is an important concept to avoid empty or overfull and unordered result sets. However, such scoring can only express total orders, which restricts its usefulness when several factors influence result relevance. A more flexible way to express relevance is the notion of preferences. Users state which kind of answers they 'prefer' by adding soft constraints to their queries.

2008

The paper presents a new approach to database preferences queries, where preferences are represented in a possibilistic logic manner, using symbolic weights. The symbolic weights may be processed without assessing their precise value, which leaves the freedom for the user to not specify any priority among the preferences. The user may also enforce a (partial) ordering between them, if necessary. The approach can be related to the processing of fuzzy queries whose components are conditionally weighted in terms of importance. Here, importance levels are symbolically processed, and refinements of both Pareto ordering and minimum ordering are used. The representational power of the proposed setting is stressed, while the approach is compared with database Best operator-like methods and with the CP-net approach developed in artificial intelligence. The paper also provides a structured and rather broad overview of the different lines of research in the literature dealing with the handling of preferences in database queries.

Lecture Notes in Computer Science

Preferences can be used for information filtering and extraction to deliver the most relevant data to the user. Therefore the efficient integration of querying with preferences into standard database technology is an important issue. The paper resumes a logical framework for formulating preferences and their embedding into relational algebra through a single preference operator parameterized by a set of user preferences of sixteen various kinds and returning only the most preferred subsets of its argument relation. Most importantly, preferences between sets of elements can be expressed. To make a relational query language with the preference operator useful for practical applications, formal foundation for algebraic optimization, applying heuristics like push preference, has to be provided. Therefore abstract properties of the preference operator and a variety of algebraic laws describing its interaction with other relational algebra operators are presented.

Annals of Mathematics and Artificial Intelligence, 2011

This note corrects a claim made in the above-mentioned paper about the exact representation of a conditional preference network by means of a possibilistic logic base with partially ordered symbolic weights. We provide a counterexample that shows that the possibilistic logic representation is indeed not always exact. This is the basis of a short discussion on the difficulty of obtaining an exact representation. This note corrects a claim made in [6] about the representation of Conditional Preference networks (CP-nets for short) [1] by means of a possibilistic logic base [2], as well as a similar claim in [7, 8]. A CP-net encodes a set of preference statements concerning the values of Boolean decision variables, conditioned on the values of other Boolean decision variables that influence the former. More formally, let V = {X 1 , • • • , Xn} be a set of Boolean variables. We denote by Ast(S) the set of interpretations of variables of S (⊆ V). Definition 1 A CP-net N over V = {X 1 , • • • , Xn} is a directed graph with nodes X 1 , • • • , Xn, and there is a directed edge from X i to X j if the preference about the value X j depends on the value of X i. Each node X i ∈ V is associated with a conditional preference table CP T i that associates a strict preference (x i > ¬x i or ¬x i > x i) with each possible instantiation u j i ∈ Ast(P a(X i)) of the parents P a(X i) of X i (if any). Each entry in a conditional preference table CP T i is of the form φ = u j i : j x i > j ¬x i , where u j i ∈ Ast(P a(X i)), j is blank if the preference is x i > ¬x i and is ¬ otherwise. Each interpretation ω, i.e., an instantiation of all variables in V , is understood as a solution to the decision problem described by the preference statements in the CP-net. A CP-net induces a partial preference ordering over solutions defined as follows. Each

IEEE Transactions on Knowledge and Data Engineering, 2014

In this paper, we argue that preference-aware query processing needs to be pushed closer to the DBMS. We introduce a preference-aware relational data model that extends database tuples with preferences and an extended algebra that captures the essence of processing queries with preferences. Based on a set of algebraic properties and a cost model that we propose, we provide several query optimization strategies for extended query plans. Further, we describe a query execution algorithm that blends preference evaluation with query execution, while making effective use of the native query engine. We have implemented our framework and methods in a prototype system, PrefDB. PrefDB allows transparent and efficient evaluation of preferential queries on top of a relational DBMS. Our extensive experimental evaluation on two real-world datasets demonstrates the feasibility and advantages of our framework.

2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007

User-defined preferences allow personalized ranking of query results. A user provides a declarative specification of his/her preferences, and the system is expected to use that specification to give more prominence to preferred answers. We study constraint formalisms for expressing user preferences as base facts in a partial order. We consider a language that allows comparison and a limited form of arithmetic, and show that the transitive closure computation required to complete the partial order terminates. We consider various ways of composing partial orders from smaller pieces, and provide results on the size of the resulting transitive closures. Finally, we show how preference queries within our language can be supported by suitable index structures for efficient evaluation over large data sets. Our results provide guidance about when complex preferences can be efficiently evaluated, and when they cannot.

Annals of Mathematics and Artificial Intelligence, 2015

This note corrects a claim made in the above-mentioned paper about the exact representation of a conditional preference network by means of a possibilistic logic base with partially ordered symbolic weights. We provide a counterexample that shows that the possibilistic logic representation is indeed not always exact. This is the basis of a short discussion on the difficulty of obtaining an exact representation. This note corrects a claim made in [6] about the representation of Conditional Preference networks (CP-nets for short) [1] by means of a possibilistic logic base [2], as well as a similar claim in [7, 8]. A CP-net encodes a set of preference statements concerning the values of Boolean decision variables, conditioned on the values of other Boolean decision variables that influence the former. More formally, let V = {X 1 , • • • , X n } be a set of Boolean variables. We denote by Ast (S) the set of interpretations of variables of S (⊆ V). Definition 1 A CP-net N over V = {X 1 , • • • , X n } is a directed graph with nodes X 1 , • • • , X n , and there is a directed edge from X i to X j if the preference about the value X j depends on the value of X i. Each node X i ∈ V is associated with a conditional preference table CP T i that associates a strict preference (x i > ¬x i or ¬x i > x i

Journal of Web Semantics, 2007

We introduce conditional preference bases as a means for ranking objects in ontologies. Conditional preference bases consist of a description logic knowledge base and a finite set of conditional preferences, which are statements of the form “generally, in the context ϕϕ, property αα is preferred over property ¬α¬α with strength s”. They are inspired by variable-strength defaults in conditional knowledge bases. We define the notion of consistency for conditional preference bases, and we show how consistent conditional preference bases can be used for ranking objects in ontologies, where every object represents essentially a set of individuals that are sharing the same ranking-relevant properties. More concretely, we define two object rankings, denoted κsumκsum and κlexκlex, which evaluate the strengths of conditional preferences in an additive and a lexicographic way, respectively. Furthermore, we provide algorithms for the main computational tasks for ranking objects under conditional preference bases, we analyze the complexity of these tasks, and we delineate a tractable special case. To give evidence of the usefulness of this approach in practice, we describe two applications in the areas of product and literature search, where it allows especially for a flexible user-defined ranking of the query results reflecting personal preferences.

Because of the rise in data volume of knowledge bases that are being published as a result of Open Data initiatives, new approaches are required to assist users in locating the items that most closely matches their preference criteria. In many approaches, the user is called to supply quantitative weights that may not be known in advance to manage the ranking of results.Contrary to the quantitative technique, preference criteria are sometimes more intuitive and can be conveyed more readily under the qualitative approach. We are interested in this paper to the problem of evaluating SPARQL qualitative preference queries over user preferences in SPARQL. Many approaches address this problem based on different frameworks as CP-net, skyline, fuzzy set and top-k. This article outlines a novel approach for dealing with SPARQL preference queries, where preferences are represented through symbolic weights using the possibilistic logic framework. It is possible to manage symbolic weights withou...

2013

The study of preferences has a long tradition in many disciplines, but it has only relatively recently entered the realm of data management through their application in answering queries to relational databases. The current revolution in data availability through the Web and, perhaps most importantly in the last few years, social media sites and applications, puts ontology languages at the forefront of data and information management technologies. In this paper, we propose the first (to our knowledge) integration of ontology languages with preferences as in relational databases by developing PrefDatalog+/-, an extension of the Datalog+/family of languages with preference management formalisms closely related to those previously studied for relational databases. We focus on two kinds of answers to queries that are relevant to this setting, skyline and k-rank (a generalization of top-k queries), and develop algorithms for computing these answers to both DAQs (disjunctions of atomic queries) and CQs (conjunctive queries). We show that DAQ answering in PrefDatalog+/-can be done in polynomial time in the data complexity, as in relational databases, as long as query answering can also be done in polynomial time (in the data complexity) in the underlying classical ontology.