Automatically detecting and measuring differences between evolving Knowledge Graphs (KGs) has been a topic of investigation for years. With the rising popularity of embedding methods, we investigate the possibility of using embeddings to detect Concept Shift in evolving KGs. Specifically, we go deeper into the usage of nearest neighbour set comparison as the basis for a similarity measure, and show why this approach is conceptually problematic. As an alternative, we explore the possibility of using clustering methods. This paper serves to (i) inform the community about the challenges that arise when using KG embeddings for the comparison of different versions of a KG specifically, (ii) investigate how this is supported by theories on knowledge representation and semantic representation in NLP and (iii) take the first steps into the direction of valuable representation of semantics within KGs for comparison.

Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.

Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.

Updates on ontologies affect the operations built on top of them. But not all changes are equal: some updates drastically change the result of operations; others lead to minor variations, if any. Hence, estimating the impact of a change ex-ante is highly important, as it might make ontology engineers aware of the consequences of their action during editing. However, in order to estimate the impact of changes, we need to understand how to measure them.

Ontologies are becoming a key component of numerous applications and research fields. But knowledge captured within ontologies is not static. Some ontology updates potentially have a wide ranging impact; others only affect very localised parts of the ontology and their applications. Investigating the impact of the evolution gives us insight into the editing behaviour but also signals ontology engineers and users how the ontology evolution is affecting other applications. However, such research is in its infancy. Hence, we need to investigate the evolution itself and its impact on the simplest of applications: the materialisation. In this work, we define impact measures that capture the effect of changes on the materialisation. In the future, the impact measures introduced in this work can be used to investigate how aware the ontology editors are about consequences of changes. By introducing five different measures, which focus either on the change in the materialisation with respect to the size or on the number of changes applied, we are able to quantify the consequences of ontology changes. To see these measures in action, we investigate the evolution and its impact on materialisation for nine open biomedical ontologies, most of which adhere to the EL++ description logic. Our results show that these ontologies evolve at varying paces but no statistically significant difference between the ontologies with respect to their evolution could be identified. We identify three types of ontologies based on the types of complex changes which are applied to them throughout their evolution. The impact on the materialisation is the same for the investigated ontologies, bringing us to the conclusion that the effect of changes on the materialisation can be generalised to other similar ontologies. Further, we found that the materialised concept inclusion axioms experience most of the impact induced by changes to the class inheritance of the ontology and other changes only marginally touch the materialisation.