Abstract
Learning how to program is a difficult task. To acquire the re-quired skills, novice programmers must solve a broad range of programming activities, always supported with timely, rich, and accurate feedback. Automated assessment tools play a major role in fulfilling these needs, being a common pres-ence in introductory programming courses. As programming exercises are not easy to produce and those loaded into these tools must adhere to specific format requirements, teachers often opt for reusing them for several years. There-fore, most automated assessment tools, particularly Mooshak, store hundreds of submissions to the same programming ex-ercises, as these need to be kept after automatically pro-cessed for possible subsequent manual revision. Our dataset consists of the submissions to 16 programming exercises in Mooshak proposed in multiple years within the 2003-2020 timespan to undergraduate Computer Science students at the Faculty of Sciences from the University of Porto. In particular, we extract their code property graphs and store them as CSV files. The analysis of this data can enable, for instance, the generation of more concise and personalized feedback based on similar accepted submissions in the past, the identifica-tion of different strategies to solve a problem, the under -standing of a student's thinking process, among many other findings.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Abstract
Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.

Abstract
The size of massive knowledge graphs (KGs) and the lack of prior information regarding the schemas, ontologies and vocabularies they use frequently makes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they represent. Particularly, IRI namespaces can be used to classify RDF resources. Namespaces, used in some RDF serialization formats as a shortening mechanism for resource IRIs, have no role in the semantics of RDF. Nevertheless, there is often a hidden meaning behind the decision of grouping resources under a common prefix and assigning an alias to it. We improved on previous work on a namespace-based approach to KG summarization that classifies resources using their namespaces. Producing the summary graph is fast, light on computing resources and requires no previous domain knowledge. The summary graph can be used to analyze the namespace inter-dependencies of the original graph. We also present chilon, a tool for calculating namespace-based KG summaries. Namespaces are gathered from explicit declarations in the graph serialization, community contributions or resource IRI prefix analysis. We applied chilon to publicly available KGs, used it to generate interactive visualizations of the summaries, and discuss the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The size of massive knowledge graphs (KGs) and the lack of prior information regarding the schemas, ontologies and vocabularies they use frequently makes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they represent. Particularly, IRI namespaces can be used to classify RDF resources. Namespaces, used in some RDF serialization formats as a shortening mechanism for resource IRIs, have no role in the semantics of RDF. Nevertheless, there is often a hidden meaning behind the decision of grouping resources under a common prefix and assigning an alias to it. We improved on previous work on a namespace-based approach to KG summarization that classifies resources using their namespaces. Producing the summary graph is fast, light on computing resources and requires no previous domain knowledge. The summary graph can be used to analyze the namespace inter-dependencies of the original graph. We also present chilon, a tool for calculating namespace-based KG summaries. Namespaces are gathered from explicit declarations in the graph serialization, community contributions or resource IRI prefix analysis. We applied chilon to publicly available KGs, used it to generate interactive visualizations of the summaries, and discuss the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Semantic measures evaluate and compare the strength of relations between entities. To assess their accuracy, semantic measures are compared against human-generated gold standards. Existing semantic gold standards are mainly focused on concepts. Nevertheless, semantic measures are frequently applied both to concepts and instances. Games with a purpose are used to offload to humans computational or data collection needs, improving results by using entertainment as motivation for higher engagement. We present Grettir, a system which allows the creation of crowdsourced semantic relations datasets for named entities through a game with a purpose where participants are asked to compare pairs of entities. We describe the system architecture, the algorithms and implementation decisions, the first implemented instance – dedicated to the comparison of music artists – and the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.