Cardinality constraints in semantic data models

Liddle, Stephen W.; Embley, David W.; Woodfield, Scott N.

doi:10.1016/0169-023x(93)90024-j

Cited by 69 publications

(23 citation statements)

References 8 publications

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“…Each one illustrates different views on showing the cardinality constraints that exist on the relationships. Liddle [21] expands the previous analysis by examining and describing additional models such as the semantic binary data model, the structural model, the semantic association model, the NijssenÕs information analysis methodology, and the binary relationship model. Song [26] further compares and contrasts various ER diagramming notations.…”

Section: Related Workmentioning

confidence: 99%

“…The model is not just a set of individually constrained relationships pieced together between sets of entities, but a holistic view of the representation domain. Each set of cardinality constraints on a single relationship must be consistent with all the remaining constraints in the model and over all possible states [13,21]. More restricted methods of cardinality constraints have been proposed, that allow upper and lower bounds, or an algorithm to define the constraint [13][14][15][16]31,32].…”

Section: Validitymentioning

confidence: 99%

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An analysis of structural validity in entity-relationship modeling

Dullea

Song

Lamprou

2003

Data & Knowledge Engineering

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We explore the criteria that contribute to the structural validity of modeling structures within the entityrelationship (ER) diagram. Our approach examines cardinality constraints in conjunction with the degree of the relationship to address constraint consistency, state compliance, and role uniqueness issues to derive a complete and comprehensive set of decision rules. Unlike typical other analyses that use only maximum cardinality constraints, we have used both maximum and minimum cardinality constraints in defining the properties and their structural validity criteria yielding a complete analysis of the structural validity of recursive, binary, and ternary relationship types. Our study evaluates these relationships as part of the overall diagram and our rules address these relationships as they coexist in a path structure within the model. The contribution of this paper is to provide a comprehensive set of decision rules to determine the structural validity of any ERD containing recursive, binary, and ternary relationships. These decision rules can be readily applied to real world data models regardless of their complexity. The rules can easily be incorporated into the database modeling and designing process, or extended into case tool implementations.

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Section: Related Workmentioning

confidence: 99%

Section: Validitymentioning

confidence: 99%

An analysis of structural validity in entity-relationship modeling

Dullea

Song

Lamprou

2003

Data & Knowledge Engineering

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“…They can be considered as constraints only after a user assessment and application over a given dataset. Despite this, our work builds upon existing approaches for cardinality constraints in RDF and other data models such as XML [3] and Entity-Relationship [28,10]. Consistency in RDF graphs.…”

Section: Related Workmentioning

confidence: 99%

Mining Cardinalities from Knowledge Bases

Muñoz

Nickles

2017

Lecture Notes in Computer Science

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Abstract. Cardinality is an important structural aspect of data that has not received enough attention in the context of RDF knowledge bases (KBs). Information about cardinalities can be useful for data users and knowledge engineers when writing queries, reusing or engineering KBs. Such cardinalities can be declared using OWL and RDF constraint languages as constraints on the usage of properties over instance data. However, their declaration is optional and consistency with the instance data is not ensured. In this paper, we address the problem of mining cardinality bounds for properties to discover structural characteristics of KBs, and use these bounds to assess completeness. Because KBs are incomplete and error-prone, we apply statistical methods for filtering property usage and for finding accurate and robust patterns. Accuracy of the cardinality patterns is ensured by properly handling equality axioms (owl:sameAs); and robustness by filtering outliers. We report an implementation of our algorithm with two variants using SPARQL 1.1 and Apache Spark, and their evaluation on real-world and synthetic data.

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“…C-XML supports mixed content and open content. Finally, C-XML provides for all XML cardinality constraints; indeed it provides for a very large spectrum of cardinality constraints [8] encompassing and going beyond those provided by XML. Fig.…”

Section: C-xmlmentioning

confidence: 99%

Augmenting Traditional Conceptual Models to Accommodate XML Structural Constructs

Al-Kamha

Embley

Liddle

2007

Conceptual Modeling - ER 2007

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Abstract.Although it is possible to present XML Schema graphically, such representations do not raise the level of abstraction for XML schemata in the same way traditional conceptual models raise the level of abstraction for data schemata. Traditional conceptual models, on the other hand, do not accommodate several XML Schema structures. Thus, there is a need to enrich traditional conceptual models with new XML Schema features. After establishing criteria for XML conceptual modeling, we propose an enrichment to represent the XML features missing in traditional models. We argue that our solution can be adapted generally for traditional conceptual models and show how it can be adapted for two popular conceptual models.

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Cardinality constraints in semantic data models

Cited by 69 publications

References 8 publications

An analysis of structural validity in entity-relationship modeling

An analysis of structural validity in entity-relationship modeling

Mining Cardinalities from Knowledge Bases

Augmenting Traditional Conceptual Models to Accommodate XML Structural Constructs

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