Defining and Detecting Complex Changes on RDF(S) Knowledge Bases

Galani, Theodora; Papastefanatos, George; Stavrakas, Yannis; Vassiliou, Yannis

doi:10.1007/s13740-021-00136-9

Cited by 2 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All 34 documents that mention complex changes have at least one mention of complex change applied to classes. Almost half of the analyzed documents mentioned both changes applied to classes and to properties (Stojanovic et al, 2002;Klein, 2004;Rogozan and Paquette, 2005;Stuckenschmidt and Van Harmelen, 2005;Tang and Yang, 2007;Khattak et al, 2008;Najla et al, 2009;Rogozan and Paquette, 2009;Djedidi and Aufaure, 2010;Gröner et al, 2010;Kondylakis et al, 2010;Javed et al, 2012Javed et al, , 2013Papavasileiou et al, 2013;Rahnama and Barforoush, 2015;Mahfoudh et al, 2015;Djamel et al, 2015;Galani et al, 2015;and Herrmannsdoerfer et al, 2010). A small number of documents Klein (2004), Hartung et al (2010Hartung et al ( , 2013, Dos Reis et al (2013), Papavasileiou et al (2013), Dos , Dinh et al (2014), and Kondylakis et al (2010) mentioned changes applied to annotations.…”

Section: Number Of Complex Changes Per Documentmentioning

confidence: 99%

“…From the set of 34 documents, only 8 documents Rahnama and Barforoush (2015), Xie et al (2011), Liu et al (2014), Hartung et al (2010), Mahfoudh et al (2015), Kondylakis et al (2010), Khelladi et al (2015), and Galani et al (2015) present some formalization about the changes they mention and 9 documents Rogozan and Paquette (2005), Najla et al (2009), Khattak et al (2008, Kirsten et al (2011), Maedche et al (2002), Palma et al (2009), , and do not present any textual description or formalization for the changes. In addition, even the 15 documents that present some textual description of the changes do not do so for all the mentioned changes.…”

Section: Textual Description and Formalization Of Complex Changesmentioning

confidence: 99%

“…The numbers in these tables refer to the following references: [1](Stojanovic, 2004);[2](Stojanovic et al, 2002);[3](Klein & Alexander, 2004);[4](Rahnama & Barforoush, 2015);[5](Papavasileiou et al, 2013); [6](Hartung et al, 2013);[7](Rogozan & Paquette, 2005); [8](Najla et al, 2009); [9](Xie et al, 2011); [10](Dos [11](Khattak et al, 2008);[12](Tang & Yang, 2007);[13](Kirsten et al, 2011); [14](Javed et al, 2013);[15](Maedche et al, 2002);[16](Gröner et al, 2010);[17](Stuckenschmidt & Van Harmelen, 2005); [18](Palma et al, 2009);[19](Liu et al, 2014);[20] [21](Dinh et al, 2014); [22](Dos Reis et al, 2013); [23](Javed et al, 2012); [24](Hartung et al, 2010); [25](Mahfoudh et al, 2015);[26](Rogozan & Paquette et al, 2009); [27](Kondylakis et al, 2010);[28](Djedidi & Aufaure, 2010); [29](Khelladi et al, 2015) [30](Galani et al, 2016) [31](Galani et al, 2015) [32] [33] [34](Herrmannsdoerfer et al, 2001).…”

mentioning

confidence: 99%

See 2 more Smart Citations

OWL ontology evolution: understanding and unifying the complex changes

Silva

Santos

Thiago

et al. 2022

The Knowledge Engineering Review

View full text Add to dashboard Cite

Knowledge-based systems and their ontologies evolve due to different reasons. Ontology evolution is the adaptation of an ontology and the propagation of these changes to dependent artifacts such as queries and other ontologies. Besides identifying basic/simple changes, it is imperative to identify complex changes between two versions of the same ontology to make this adaptation possible. There are many definitions of complex changes applied to ontologies in the literature. However, their specifications across works vary both in formalization and textual description. Some works also use different terminologies to refer to a change, while others use the same vocabulary to refer to distinct changes. Therefore, there is a lack of a unified list of complex changes. The main goals of this paper are: (i) present the primary documents that identify complex changes; (ii) provide critical analyses about the set of the complex changes proposed in the literature and the documents mentioning them; (iii) provide a unified list of complex changes mapping different sets of complex changes proposed by several authors; (iv) present a classification for those complex changes; and (v) describe some open directions of the area. The mappings between the complex changes provide a mechanism to relate and compare different proposals. The unified list is thus a reference for the complex changes published in the literature. It may assist the development of tools to identify changes between two versions of the same ontology and enable the adaptation of artifacts that depend on the evolved ontology.

show abstract

Section: Number Of Complex Changes Per Documentmentioning

confidence: 99%

Section: Textual Description and Formalization Of Complex Changesmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

OWL ontology evolution: understanding and unifying the complex changes

Silva

Santos

Thiago

et al. 2022

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…In [ 22 ], the method introduced focuses on RDF data (not on ontologies) to detect and express elementary and complex changes. Similarly, in [ 23 ] the authors proposed a declarative language for defining complex changes on RDF(S) knowledge bases and discussed how this language can be used to detect complex change instances among dataset versions (which can be queried for analysing evolution).…”

Section: Introductionmentioning

confidence: 99%

Analysis and implementation of the DynDiff tool when comparing versions of ontology

Diaz Benavides,

Cardoso,

Da Silveira

et al. 2023

J Biomed Semant

View full text Add to dashboard Cite

Background Ontologies play a key role in the management of medical knowledge because they have the properties to support a wide range of knowledge-intensive tasks. The dynamic nature of knowledge requires frequent changes to the ontologies to keep them up-to-date. The challenge is to understand and manage these changes and their impact on depending systems well in order to handle the growing volume of data annotated with ontologies and the limited documentation describing the changes. Methods We present a method to detect and characterize the changes occurring between different versions of an ontology together with an ontology of changes entitled DynDiffOnto, designed according to Semantic Web best practices and FAIR principles. We further describe the implementation of the method and the evaluation of the tool with different ontologies from the biomedical domain (i.e. ICD9-CM, MeSH, NCIt, SNOMEDCT, GO, IOBC and CIDO), showing its performance in terms of time execution and capacity to classify ontological changes, compared with other state-of-the-art approaches. Results The experiments show a top-level performance of DynDiff for large ontologies and a good performance for smaller ones, with respect to execution time and capability to identify complex changes. In this paper, we further highlight the impact of ontology matchers on the diff computation and the possibility to parameterize the matcher in DynDiff, enabling the possibility of benefits from state-of-the-art matchers. Conclusion DynDiff is an efficient tool to compute differences between ontology versions and classify these differences according to DynDiffOnto concepts. This work also contributes to a better understanding of ontological changes through DynDiffOnto, which was designed to express the semantics of the changes between versions of an ontology and can be used to document the evolution of an ontology.

show abstract

Defining and Detecting Complex Changes on RDF(S) Knowledge Bases

Cited by 2 publications

References 11 publications

OWL ontology evolution: understanding and unifying the complex changes

OWL ontology evolution: understanding and unifying the complex changes

Analysis and implementation of the DynDiff tool when comparing versions of ontology

Contact Info

Product

Resources

About