A Dataflow Graph Transformation Language and Query Rewriting System for RDF Ontologies

Author(s):  
Marianne Shaw ◽  
Landon T. Detwiler ◽  
James F. Brinkley ◽  
Dan Suciu
2021 ◽  
Vol 47 ◽  
Author(s):  
Justas Trinkūnas ◽  
Olegas Vasilecas

The paper analyses graph oriented ontology transformation into conceptual data model. A number of methodswere proposed to develop conceptual datamodels, but only fewdealswith knowledge reuse. In this paperwe present an approach for knowledge represented by ontology automatic transformation into conceptual data model. The graph transformation language is presented and adapted for formal transformation of ontology into conceptualmodel. Details and examples of proposed ontology transformation into conceptual data model are presented.


2016 ◽  
Vol 28 (2) ◽  
pp. 287-337 ◽  
Author(s):  
MAKOTO HAMANA ◽  
KAZUTAKA MATSUDA ◽  
KAZUYUKI ASADA

The aim of this paper is to provide mathematical foundations of a graph transformation language, called UnCAL, using categorical semantics of type theory and fixed points. About 20 years ago, Bunemanet al. developed a graph database query language UnQL on the top of a functional meta-language UnCAL for describing and manipulating graphs. Recently, the functional programming community has shown renewed interest in UnCAL, because it provides an efficient graph transformation language which is useful for various applications, such as bidirectional computation.In order to make UnCAL more flexible and fruitful for further extensions and applications, in this paper, we give a more conceptual understanding of UnCAL using categorical semantics. Our general interest of this paper is to clarify what is the algebra of UnCAL. Thus, we give an equational axiomatisation and categorical semantics of UnCAL, both of which are new. We show that the axiomatisation is complete for the original bisimulation semantics of UnCAL. Moreover, we provide a clean characterisation of the computation mechanism of UnCAL called ‘structural recursion on graphs’ using our categorical semantics. We show a concrete model of UnCAL given by the λG-calculus, which shows an interesting connection to lazy functional programming.


2006 ◽  
Vol 152 ◽  
pp. 207-222 ◽  
Author(s):  
Attila Vizhanyo ◽  
Sandeep Neema ◽  
Feng Shi ◽  
Daniel Balasubramanian ◽  
Gabor Karsai

2000 ◽  
Vol 12 (5) ◽  
pp. 694-714 ◽  
Author(s):  
Kian-Lee Tan ◽  
Cheng Hian Goh ◽  
Beng Chin Ooi
Keyword(s):  

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 149
Author(s):  
Petros Zervoudakis ◽  
Haridimos Kondylakis ◽  
Nicolas Spyratos ◽  
Dimitris Plexousakis

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.


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