Conceptual Graphs Based Modeling of Semi-structured Data

Varga, Viorica; Săcărea, Christian; Molnár, Andrea Éva

doi:10.1007/978-3-319-91379-7_13

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…CGs have many applications in research and industry, e.g. in security [11], semi-structure data modeling [19], software development [20], clustering [16], music [10] or decisionmaking [18] to name a few. One drawback is the major difficulty in designing a CG database without prior expertise.…”

Section: Introductionmentioning

confidence: 99%

CG2A: Conceptual Graphs Generation Algorithm

Faci

Lesot

Laudy

2021

Atlantis Studies in Uncertainty Modelling

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Conceptual Graphs (CGs) are a formalism to represent knowledge. The production of CG benchmarks is currently a crucial need in the community to validate algorithms. This paper proposes CG2A, an algorithm to build synthetic CGs exploiting most of their expressivity. CG2A takes as input constraints that constitute ontological knowledge including a vocabulary and a set of CGs with some label variables, called γ-CGs, as components of the generated CGs. Extensions also enable the automatic generation of the set of γ-CGs and vocabulary to ease the database generation and increase variability.

show abstract

Section: Introductionmentioning

confidence: 99%

CG2A: Conceptual Graphs Generation Algorithm

Faci

Lesot

Laudy

2021

Atlantis Studies in Uncertainty Modelling

View full text Add to dashboard Cite

show abstract

“…CGs have many applications in research and industry, eg. in security [11], semi-structure data modeling [18], software development [19], clustering [16], music [10] or decision-making [17] to name a few. One drawback is the major difficulty in designing a CG database without prior expertise.…”

Section: Introductionmentioning

confidence: 99%

CG2A: Conceptual Graphs Generation Algorithm

Faci¹,

Lesot²,

Laudy³

2021

Preprint

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Conceptual Graphs (CGs) are a formalism to represent knowledge. However producing a CG database is complex. To the best of our knowledge, existing methods do not fully use the expressivity of CGs. It is particularly troublesome as it is necessary to have CG databases to test and validate algorithms running on CGs. This paper proposes CG2A, an algorithm to build synthetic CGs exploiting most of their expressivity. CG2A takes as input constraints that constitute ontological knowledge including a vocabulary and a set of CGs with some label variables, called γ-CGs, as components of the generated CGs. Extensions also enable the automatic generation of the set of γ-CGs and vocabulary to ease the database generation and increase variability.

show abstract