Help Generation in a System for Learning Natural Language to First Order Logic Conversion

2011 ◽  
Author(s):  
Isidoros Perikos ◽  
Foteini Grivokostopoulou ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Natural Language ◽  
Order Logic ◽  
First Order Logic ◽  
First Order

Translating Advanced Integrity Checking Technology to SQL

2002 ◽  
pp. 203-249 ◽  
Author(s):  
Hendrik Decker
Keyword(s):  
Natural Language ◽  
Order Logic ◽  
Predicate Calculus ◽  
Integrity Constraints ◽  
First Order Logic ◽  
First Order ◽  
Integrity Checking ◽  
Natural Language Interface ◽  
User Friendly ◽  
Declarative Specifications

The main goal of this chapter is to arrive at a coherent technology for deriving efficient SQL triggers from declarative specifications of arbitrary integrity constraints. The user may specify integrity constraints declaratively as closed queries in predicate calculus syntax (i.e., sentences in the language of first-order logic, abbr. FOL), as datalog denials, as query conditions in SQL WHERE clauses, or in some other, possibly more user-friendly manner (e.g., via a dialog-driven graphical or natural language interface which internally translates to equivalent WHERE clause conditions). As we are going to see, the triggers derived from such specifications behave such that whenever some update event would violate any of the integrity constraints, one or several of the triggers derived from that constraint are activated in order to enforce the constraint. That is, the violation is either prevented by rolling back the update or repaired instantly by subsequent further updates.

Automatic estimation of exercises'difficulty levels in a tutoring system for teaching the conversion of natural language into first-order logic

2016 ◽  
Vol 33 (6) ◽  
pp. 569-580 ◽  
Author(s):  
Isidoros Perikos ◽  
Foteini Grivokostopoulou ◽  
Konstantinos Kovas ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Natural Language ◽  
Order Logic ◽  
First Order Logic ◽  
Tutoring System ◽  
First Order ◽  
Automatic Estimation

Difficulty Estimator for Converting Natural Language into First Order Logic

2011 ◽  
pp. 135-144 ◽  
Author(s):  
Isidoros Perikos ◽  
Foteini Grivokostopoulou ◽  
Ioannis Hatzilygeroudis ◽  
Konstantinos Kovas
Keyword(s):  
Natural Language ◽  
Order Logic ◽  
First Order Logic ◽  
First Order

First-Order Logic Reasoning Support for the Semantic Web

2009 ◽  
Vol 19 (12) ◽  
pp. 3091-3099 ◽  
Author(s):  
Gui-Hong XU ◽  
Jian ZHANG
Keyword(s):  
Semantic Web ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Logic Reasoning

Boolean-valued structures

2018 ◽  
Author(s):  
Tim Button ◽  
Sean Walsh
Keyword(s):  
Model Theory ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Rules ◽  
Mathematical Concepts ◽  
Semantic Framework ◽  
First Order ◽  
Standard Models ◽  
Boolean Valued Model ◽  
Semantic Values

Chapters 6-12 are driven by questions about the ability to pin down mathematical entities and to articulate mathematical concepts. This chapter is driven by similar questions about the ability to pin down the semantic frameworks of language. It transpires that there are not just non-standard models, but non-standard ways of doing model theory itself. In more detail: whilst we normally outline a two-valued semantics which makes sentences True or False in a model, the inference rules for first-order logic are compatible with a four-valued semantics; or a semantics with countably many values; or what-have-you. The appropriate level of generality here is that of a Boolean-valued model, which we introduce. And the plurality of possible semantic values gives rise to perhaps the ‘deepest’ level of indeterminacy questions: How can humans pin down the semantic framework for their languages? We consider three different ways for inferentialists to respond to this question.

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