Pull-tabbing is an evaluation approach for functional logic computations, b ased on a graph transformation recently proposed, which avoids making irrevocable non-deterministic choices that would jeopardize the completeness of computations. In contrast to other approaches with this property, it does not require an upfront cloning of a possibly large portion of the choice’s context. We formally define the pull-tab transformation, characterize the class of programs f or which the transformation is intended, extend the computations in these programs to include the transformation, and prove the correctness of the extended computations.
The idea of identifying choices to avoid combining in some expression the left and right alternatives of the same choice appears in @cite_8 . The idea is developed in the framework of a natural semantics for the translation of (flat) Curry programs into Haskell. A proof of the correctness of this idea will appear in @cite_24 which also addresses the similarities between the natural semantics and graph rewriting. This discussion, although informal, is enlightening.
@cite_24 @cite_8
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Research has seen considerable achievements concerning translation of natural language patterns into formal queries for Question Answering (QA) based on Knowledge Graphs (KG). One of the main challenges in this research area is about how to identify which property within a Knowledge Graph matches the predicate found in a Natural Language (NL) relation. Current approaches for formal query generation attempt to resolve this problem mainly by first retrieving the named entity from the KG together with a list of its predicates, then filtering out one from all the predicates of the entity. We attempt an approach to directly match an NL predicate to KG properties that can be employed within QA pipelines. In this paper, we specify a systematic approach as well as providing a tool that can be employed to solve this task. Our approach models KB relations with their underlying parts of speech, we then enhance this with extra attributes obtained from Wordnet and Dependency parsing characteristics. From a question, we model a similar representation of query relations. We then define distance measurements between the query relation and the properties representations from the KG to identify which property is referred to by the relation within the query. We report substantive recall values and considerable precision from our evaluation. Formal semantics of programming languages needs to model the potentially infinite state transition behavior of programs as well as the computation of their final results simultaneously. This requirement is essential in correctness proofs for compilers. We show that a greatest fixed point interpretation of natural semantics is able to model both aspects equally well. Technically, we infer this interpretation of natural semantics based on an easily omprehensible introduction to the dual definition and proof principles of induction and coinduction. Furthermore, we develop a proof calculus based on it and demonstrate its application for two typical problems.