Understanding Ontology Evolution: A Change Detection Approach

Plessers, Peter; Troyer, Olga De; Casteleyn, Sven

doi:10.2139/ssrn.3199348

Cited by 15 publications

(30 citation statements)

References 11 publications

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“…Our main contribution is supporting interrelated complex changes providing a language for defining complex changes over simple or other complex changes and an appropriate detection algorithm. (Plessers, De Troyer and Casteleyn, 2007) and (Roussakis et al, 2015) do not support interrelated complex changes.…”

Section: Related Workmentioning

confidence: 83%

“…In (Roussakis et al, 2015), an extension of (Papavasileiou et al, 2013) is proposed, providing a more generic change definition framework, based on SPARQL queries. In (Plessers, De Troyer and Casteleyn, 2007), Change Definition Language is proposed for defining and detecting changes over a version log using temporal queries. In (Auer and Herre, 2007) a framework for supporting evolution in RDF knowledge bases is discussed.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to (Klein, 2004), (Papavasileiou et al, 2013), (Roussakis et al, 2015) and (Stojanovic, 2004) we assume primitive changes, as simple changes, and groupings of them, as complex changes. Instead of providing a predefined list of complex changes, we support userdefined complex changes in order to capture richer semantics and multiple interpretations of evolution, as (Plessers, De Troyer and Casteleyn, 2007) and (Roussakis et al, 2015). Our main contribution is supporting interrelated complex changes providing a language for defining complex changes over simple or other complex changes and an appropriate detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Language for Defining and Detecting Interrelated Complex Changes on RDF(S) Knowledge Bases

Galani

Papastefanatos

Stavrakas

2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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The dynamic nature of web data brings forward the need for maintaining data versions as well as identifying changes between them. In this paper, we deal with problems regarding understanding evolution, focusing on RDF(S) knowledge bases, as RDF is a de-facto standard for representing data on the web. We argue that revisiting past snapshots or the differences between them is not enough for understanding how and why data evolved. Instead, changes should be treated as first-class-citizens. In our view, this involves supporting semantically rich, user-defined changes that we call complex changes, as well as identifying the interrelations between them. In this paper, we present our perspective regarding complex changes, propose a declarative language for defining complex changes for RDF(S) knowledge bases, and show how this language is used to detect complex change instances among dataset versions.

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Section: Related Workmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Language for Defining and Detecting Interrelated Complex Changes on RDF(S) Knowledge Bases

Galani

Papastefanatos

Stavrakas

2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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“…In Plessers et al (), the authors aim to help ontology engineers' work in a collaborative way by understanding and detecting ontology changes. To this end, they proposed a Change Definition Language and a version log.…”

Section: Related Workmentioning

confidence: 99%

“… Timestamp‐based (TB) approach (Bedi & Marwaha, ; Chen & Matthews, ; Eder & Koncilia, ; Grandi, ; Grandi, ; Grandi & Scalas, ; Kirsten, Hartung, Groß, & Rahm, ; Klarman, Hoekstra, & Bron, ; Liu, ; Plessers, De Troyer, & Casteleyn, ; Zekri et al, ) This technique is adopted from temporal databases (Jensen & Dyreson, ) It is very efficient from space point of view as the entities and axioms are stored only once. To keep track of their evolution, they are marked by a valid/transaction time interval.…”

Section: Introductionmentioning

confidence: 99%

Efficient management and storage of a multiversion OWL 2 DL domain ontology

2018

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Ontology is the chief technology to model domain knowledge and fix its heterogeneity. Knowledge evolution is unavoidable in all fields, and ontology should reflect such an evolution while preserving its consistency. The access to an ontology evolution history is also a crucial need. Thereby, ontology versions should be stored efficiently. To address these requirements, some works focused only on managing ontology inconsistency. To this end, they adopted an a posteriori approach that checks inconsistency after its occurrence. Others have mainly focused on some ontology versioning aspects other than the storage and the consistency preservation issues. Specifically, the state of the art storage strategies perform well some typical queries at the cost of a high storage space. Strategies with low space overhead lose the main specificity of ontology: the knowledge semantics modelling. So, it is useful to address all these shortcomings. This paper presents an approach that allows generating consistent OWL 2 DL ontology versions in a priori way. It also proposes an efficient storage strategy that preserves the semantics aspect of ontology and performs well typical queries with a low storage space. A protégé plug‐in is developed to show the feasibility of the main ideas presented in this paper.

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Knowledge Engineering

Aussenac-Gilles

Charlet

Reynaud

2020

A Guided Tour of Artificial Intelligence Research

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Knowledge engineering refers to all technical, scientific and social aspects involved in designing, maintaining and using knowledge-based systems. Research in this domain requires to develop studies on the nature of the knowledge and its representation, either the users' knowledge or the knowledge-based system's knowledge. It also requires the analysis of what type of knowledge sources is considered, what human-machine interaction is envisaged and more generally the specific end use. To that end, knowledge engineering needs to integrate innovation originating from artificial intelligence, knowledge representation, software engineering as well as modelling. This integration enables both users and software systems to manage and use the knowledge for inference reasoning. Other advances are fuelling new methods, software tools and interfaces to support knowledge modelling that are enabled by conceptual or formal knowledge representation languages. This chapter provides an overview of the main issues and major results that are considered as milestones in the domain, with a focus on recent advances marked by the raise of the semantic web, of ontologies and the social web.

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Understanding Ontology Evolution: A Change Detection Approach

Cited by 15 publications

References 11 publications

A Language for Defining and Detecting Interrelated Complex Changes on RDF(S) Knowledge Bases

A Language for Defining and Detecting Interrelated Complex Changes on RDF(S) Knowledge Bases

Efficient management and storage of a multiversion OWL 2 DL domain ontology

Knowledge Engineering

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