Concept discovery on relational databases: New techniques for search space pruning and rule quality improvement

Abstract Bootstrap methods, initially developed for solving statistical and quantum ﬁeld theories, have recently been shown to capture the discrete spectrum of quantum mechanical problems, such as the single particle Schrödinger equation with an anharmonic potential. The core of bootstrap methods builds on exact recursion relations of arbitrary moments of some quantum operator and the use of an adequate set of positivity criteria. We extend this methodology to models with continuous Bloch band spectra, by considering a single quantum particle in a periodic cosine potential. We ﬁnd that the band structure can be obtained accurately provided the bootstrap uses moments involving both position and momentum variables. We also introduce several new techniques that can apply generally to other bootstrap studies. First, we devise a trick to reduce by one unit the dimensionality of the search space for the variables parametrizing the bootstrap. Second, we employ statistical techniques to reconstruct the distribution probability allowing to compute observables that are analytic functions of the canonical variables. This method is used to extract the Bloch momentum, a quantity that is not readily available from the bootstrap recursion itself.

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Circuit-Based Search Space Pruning in QBF

Theory and Applications of Satisfiability Testing – SAT 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-94144-8_12 ◽

2018 ◽

pp. 187-198 ◽

Cited By ~ 2

Author(s):

Mikoláš Janota

Keyword(s):

Search Space ◽

Search Space Pruning

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Effective Search-Space Pruning for Solvers of String Equations, Regular Expressions and Length Constraints

Computer Aided Verification - Lecture Notes in Computer Science ◽

10.1007/978-3-319-21690-4_14 ◽

2015 ◽

pp. 235-254 ◽

Cited By ~ 19

Author(s):

Yunhui Zheng ◽

Vijay Ganesh ◽

Sanu Subramanian ◽

Omer Tripp ◽

Julian Dolby ◽

...

Keyword(s):

Search Space ◽

Regular Expressions ◽

Search Space Pruning

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A novel method for providing relational databases with rich semantics and natural language processing

Journal of Enterprise Information Management ◽

10.1108/jeim-01-2015-0005 ◽

2017 ◽

Vol 30 (3) ◽

pp. 503-525

Author(s):

Kamal Hamaz ◽

Fouzia Benchikha

Keyword(s):

Natural Language ◽

Data Storage ◽

Language Processing ◽

Relational Database ◽

Relational Databases ◽

Data Access ◽

New Techniques ◽

Content Type ◽

Semantic Tree ◽

Novel Method

Purpose With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDBs is not widespread due to their deficiencies in understanding natural language (NL) queries. In this sense, the purpose of this paper is to propose a novel method for an intelligent understanding of NL queries using semantically enriched database sources. Design/methodology/approach First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing NL queries uses the stored semantics to generate a semantic tree. Findings The evaluation part of the work shows the advantages of using a semantically enriched database source to understand NL queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a NL query. Originality/value Existing NLIDBs are not yet a standard option for interfacing a relational database due to their lack for understanding NL queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying the NL elements by their semantic nature in order to generate a semantic tree. This last is a key solution towards an intelligent understanding of NL queries to relational databases.

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