In this paper we present SPREFQL, an extension of the SPARQL language that allows appending a PREFER clause that expresses "soft" preferences over the query results obtained by the main body of the query. The extension does not add expressivity and any SPREFQL query can be transformed to an equivalent standard SPARQL query. However, clearly separating preferences from the "hard" patterns and filters in the WHERE clause gives queries where the intention of the client is more cleanly expressed, an advantage for both human readability and machine optimization. In the paper we formally define the syntax and the semantics of the extension and we also provide empirical evidence that optimizations specific to SPREFQL improve run-time efficiency by comparison to the usually applied optimizations on the equivalent standard SPARQL query.
When it comes to quantitative preferences, prominent examples include the extensions proposed by and . Closer to our work, influential databases research on preferences includes the work of Kie ling @cite_8 @cite_9 . This was used by to propose a SPARQL extension using a PREFERRING solution modifier. Contrary to our approach, these preferences are expressed using unary preference constructors. These constructors are of two types: boolean preferences where the preferred elements fulfill a specific boolean condition while the non-preferred do not; and scoring preferences , denoted with a HIGHEST or LOWEST keyword, where the preferred elements have a higher (or lower) value from the non preferred ones. Simple preferences expressed with these constructors can be further combined using Pareto and prioritized composition operators. further extended this language with an IF-THEN-ELSE clause which allows expressing conditional preferences that apply only if a condition holds. Conditional preferences allow several other syntactic sugar preference constructors to be defined, such as AROUND and BETWEEN .
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Current search engines can hardly cope adequately with fuzzy predicates defined by complex preferences. The biggest problem of search engines implemented with standard SQL is that SQL does not directly understand the notion of preferences. Preference SQL extends SQL by a preference model based on strict partial orders (presented in more detail in the companion paper [Kie02]), where preference queries behave like soft selection constraints. Several built-in base preference types and the powerful Pareto operator, combined with the adherence to declarative SQL programming style, guarantees great programming productivity. The Preference SQL optimizer does an efficient re-writing into standard SQL, including a high-level implementation of the skyline perator for Pareto-optimal sets. This pre-processor approach enables a seamless application integration, making Preference SQL available on all major SQL platforms. Several commercial B2C portals are powered by Preference SQL. Its benefits comprise cooperative query answering and smart customer advice, leading to higher e-customer satisfaction and shorter development times of personalized search engines. We report practical experiences ranging from m-commerce and comparison shopping to a large-scale performance test for a job portal. The addition of preferences to normal logic programs is a convenient way to represent many aspects of default reasoning. If the derivation of an atom A1 is preferred to that of an atom A2, a preference rule can be defined so that A2 is derived only if A1 is not. Although such situations can be modelled directly using default negation, it is often easier to define preference rules than it is to add negation to the bodies of rules. As first noted by [Proc. Internat. Conf. on Logic Programming, 1995, pp. 731-746], for certain grammars, it may be easier to disambiguate parses using preferences than by enforcing disambiguation in the grammar rules themselves. In this paper we define a general fixed-point semantics for preference logic programs based on an embedding into the well-founded semantics, and discuss its features and relation to previous preference logic semantics. We then study how preference logic grammars are used in data standardization, the commercially important process of extracting useful information from poorly structured textual data. This process includes correcting misspellings and truncations that occur in data, extraction of relevant information via parsing, and correcting inconsistencies in the extracted information. The declarativity of Prolog offers natural advantages for data standardization, and a commercial standardizer has been implemented using Prolog. However, we show that the use of preference logic grammars allow construction of a much more powerful and declarative commercial standardizer, and discuss in detail how the use of the non-monotonic construct of preferences leads to improved commercial software. Abstract In this paper, we address the issue of how Gelfond and Lifschitz's answer set semantics for extended logic programs can be suitably modified to handle prioritized programs. In such programs an ordering on the program rules is used to express preferences. We show how this ordering can be used to define preferred answer sets and thus to increase the set of consequences of a program. We define a strong and a weak notion of preferred answer sets. The first takes preferences more seriously, while the second guarantees the existence of a preferred answer set for programs possessing at least one answer set. Adding priorities to rules is not new, and has been explored in different contexts. However, we show that many approaches to priority handling, most of which are inherited from closely related formalisms like default logic, are not suitable and fail on intuitive examples. Our approach, which obeys abstract, general principles that any approach to prioritized knowledge representation should satisfy, handles them in the expected way. Moreover, we investigate the complexity of our approach. It appears that strong preference on answer sets does not add on the complexity of the principal reasoning tasks, and weak preference leads only to a mild increase in complexity. In this paper we explore the use of selectional preferences for detecting noncompositional verb-object combinations. To characterise the arguments in a given grammatical relationship we experiment with three models of selectional preference. Two use WordNet and one uses the entries from a distributional thesaurus as classes for representation. In previous work on selectional preference acquisition, the classes used for representation are selected according to the coverage of argument tokens rather than being selected according to the coverage of argument types. In our distributional thesaurus models and one of the methods using WordNet we select classes for representing the preferences by virtue of the number of argument types that they cover, and then only tokens under these classes which are representative of the argument head data are used to estimate the probability distribution for the selectional preference model. We demonstrate a highly signicant correlation between measures which use these ‘typebased’ selectional preferences and compositionality judgements from a data set used in previous research. The type-based models perform better than the models which use tokens for selecting the classes. Furthermore, the models which use the automatically acquired thesaurus entries produced the best results. The correlation for the thesaurus models is stronger than any of the individual features used in previous research on the same dataset.