Abstract:The World Wide Web and the Semantic Web are designed as a network of distributed services and datasets. The distributed character of the Web brings manifold collaborative possibilities to interchange data. The commonly adopted collaborative solutions for RDF data are centralized (e. g. SPARQL endpoints and wiki systems). But to support distributed collaboration, a system is needed, that supports divergence of datasets, brings the possibility to conflate diverged states, and allows distributed datasets to be sy… Show more
“…Representative approaches are included in Rebele et al (2016), Calbimonte et al (2017), Narula et al (2018), Salatino et al (2018), Tommasini et al (2018) and in the recent related work of Arndt et al (2019).…”
The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.
“…Representative approaches are included in Rebele et al (2016), Calbimonte et al (2017), Narula et al (2018), Salatino et al (2018), Tommasini et al (2018) and in the recent related work of Arndt et al (2019).…”
The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.
“…It is therefore essential to extend current implementations to incorporate semantically-aware conflict detection and reconciliation between datasets with different metadata classification systems. Current state of the art relies on a supervised approach to identify and handle cases where users have made changes to parallel copies of data records (e.g., Arndt et al, 2019 ).…”
Section: A Model For Decentralized But Globally Coordinated Data Aggrmentioning
confidence: 99%
“…Inspired by big science efforts like the Human Genome Project, major biodiversity initiatives have set the goal of aggregating all data about where and when different biological entities-most typically "species" in our context-are located, in order to provide critical insight into global problems such as rapid biodiversity loss and climate change (Peterson et al, 2010;Devictor and Bensaude-Vincent, 2016;IPBES, 2019;Wagner, 2020). However, there are an exceptionally large and heterogeneous set of stakeholders for this emerging biodiversity knowledge commons (Adams et al, 2002;Strandburg et al, 2017), making effective governance a critical, ongoing challenge (Alphandéry andFortier, 2010, Turnhout et al, 2014). The present moment marks a pivotal opportunity to examine how a new, decentralized approach may better provide the "flexibility both to accommodate and to benefit from this diversity [of contributors], rather than seeking to implement a prescriptive programme of planned deliverables" (Hobern et al, 2019, p. 9)-as recommended by a recent report from the second Global Biodiversity Informatics Conference.…”
Centralized biodiversity data aggregation is too often failing societal needs due to pervasive and systemic data quality deficiencies. We argue for a novel approach that embodies the spirit of the Web ("small pieces loosely joined") through the decentralized coordination of data across scientific languages and communities. The upfront cost of decentralization can be offset by the long-term benefit of achieving sustained expert engagement, higher-quality data products, and ultimately more societal impact for biodiversity data. Our decentralized approach encourages the emergence and evolution of multiple self-identifying communities of practice that are regionally, taxonomically, or institutionally localized. Each community is empowered to control the social and informational design and versioning of their local data infrastructures and signals. With no single aggregator to exert centralized control over biodiversity data, decentralization generates loosely connected networks of mid-level aggregators. Global coordination is nevertheless feasible through automatable data sharing agreements that enable efficient propagation and translation of biodiversity data across communities. The decentralized model also poses novel integration challenges, among which the explicit and continuous articulation of conflicting systematic classifications and phylogenies remain the most challenging. We discuss the development of available solutions, challenges, and outline next steps: the global effort of coordination should focus on developing shared languages for data signal translation, as opposed to homogenizing the data signal itself.
“…The semantic diff of Archivo based on (OWL) axiom diffs goes a step further. Quit [ 2 ] implements an RDF versioning and collaboration system on top of Git. It provides unified access via SPARQL 1.1 on each version of an ontology and the versioning history.…”
While thousands of ontologies exist on the web, a unified system for handling online ontologies – in particular with respect to discovery, versioning, access, quality-control, mappings – has not yet surfaced and users of ontologies struggle with many challenges. In this paper, we present an online ontology interface and augmented archive called DBpedia Archivo, that discovers, crawls, versions and archives ontologies on the DBpedia Databus. Based on this versioned crawl, different features, quality measures and, if possible, fixes are deployed to handle and stabilize the changes in the found ontologies at web-scale. A comparison to existing approaches and ontology repositories is given
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