Similarity Relational Calculus and Its Reduction to a Similarity Algebra

The result quality of queries incorporating impreciseness can be improved by the specification of user-defined weights. Existing approaches evaluate weighted queries by applying arithmetic evaluations on top of the query's intrinsic logic. This complicates the usage of logic-based optimization. Therefore, we suggest a weighting approach that is completely embedded in a logic.In order to facilitate the user interaction with the system, we exploit the intuitively comprehensible concept of preferences. In addition, we use a machine-based learning algorithm to learn weighting values in correspondence to the user's intended semantics of a posed query. Experiments show the utility of our approach.

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“…The usage of fuzzy logic, however, seems feasible [4,10,24,31]. Unfortunately, fuzzy logic does not always meet the user's needs [13,15,25].…”

Section: Cqql Conditions and Their Evaluationmentioning

confidence: 94%

“…To cope with this, classic approaches usually exploit the means provided by fuzzy logic [4,10,24,31]. In contrast to prevailing works, our presented CQQL 1 approach is completely based on quantum logic [2,23].…”

Section: Introductionmentioning

confidence: 96%

A preference-based approach for interactive weight learning: learning weights within a logic-based query language

Zellhöfer

Schmitt

2009

Distrib Parallel Databases

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“…The underlying problem is the lack of a generally accepted theoretical framework unifying retrieval and database search conditions. One attempt to establish such a framework is the usage of fuzzy logic, see for example [9], [29]. As a generalization of boolean logic, it supports vague truth values together with a formalism to construct and evaluate complex conditions.…”

Section: Searching For Imagesmentioning

confidence: 99%

“…Many extensions of the relational model by imprecision applying fuzzy-logic were carried out on the relational algebra, see e.g. [1], [9], [29], [32], [31]. We discuss related problems in more detail in Sect.…”

Section: Introductionmentioning

confidence: 99%

Image Database Search using Fuzzy and Quantum Logic

Schmitt

Nürnberger

2007

2007 IEEE International Fuzzy Systems Conference

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Most of the currently existing image retrieval systems make use of either low-level features or semantic (textual) annotations. A combined usage during annotation and retrieval is rarely attempted. In this paper, we present a query language that makes use of both, low and high level annotations, that are provided in the MPEG-7 standard. The query language is based on results from quantum logic and improves existing fuzzy approaches. Furthermore, it is integrated in the SAFIRE system, a framework for semi-automatic image annotation and retrieval.

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“…Most utilize fuzzy logic Zadeh (1965), and the annotations are typically modelled by a membership function to the unit interval, [0, 1] Ma (2006); Penzo (2005); Rosado et al (2006); Schmitt & Schulz (2004), although there are generalizations where the membership function instead maps to an algebraic structure of some kind (typically poset or lattice based) Belohlávek & V. Vychodil (2006); Peeva & Kyosev (2004); Shenoi & Melton (1989). Green et al Green et al (2007) proposed a general data model (referred to as the K-relation model) for annotated relations.…”

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confidence: 99%