You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
With the growing popularity of large-scale collaborative ontology-engineering projects, such as the creation of the 11th revision of the International Classification of Diseases, we need new methods and insights to help project- and community-managers to cope with the constantly growing complexity of such projects. In this paper, we present a novel application of Markov chains to model sequential usage patterns that can be found in the change-logs of collaborative ontology-engineering projects. We provide a detailed presentation of the analysis process, describing all the required steps that are necessary to apply and determine the best fitting Markov chain model. Amongst others, the model and results allow us to identify structural properties and regularities as well as predict future actions based on usage sequences. We are specifically interested in determining the appropriate Markov chain orders which postulate on how many previous actions future ones depend on. To demonstrate the practical usefulness of the extracted Markov chains we conduct sequential pattern analyses on a large-scale collaborative ontology-engineering dataset, the International Classification of Diseases in its 11th revision. To further expand on the usefulness of the presented analysis, we show that the collected sequential patterns provide potentially actionable information for user-interface designers, ontology-engineering tool developers and project-managers to monitor, coordinate and dynamically adapt to the natural development processes that occur when collaboratively engineering an ontology. We hope that presented work will spur a new line of ontology-development tools, evaluation-techniques and new insights, further taking the interactive nature of the collaborative ontology-engineering process into consideration. HighlightsWe provide a novel application for Markov.Using Markov chains we extract and analyze sequential usage patterns.We categorize the types of analyses that Markov chains enable us to perform.We demonstrate the utility of the Markov chain analysis on a large-scale project.

Cited Abstract(s):
[1]: While in the past taxonomic and ontological knowledge was traditionally produced by small groups of co-located experts, today the production of such knowledge has a radically different shape and form. For example, potentially thousands of health professionals, scientists, and ontology experts will collaboratively construct, evaluate and maintain the most recent version of the International Classification of Diseases (ICD-11), a large ontology of diseases and causes of deaths managed by the World Health Organization. In this work, we present a novel web-based tool — iCAT Analytics — that allows to investigate systematically crowd-based processes in knowledge-production systems. To enable such investigation, the tool supports interactive exploration of pragmatic aspects of ontology engineering such as how a given ontology evolved and the nature of changes, discussions and interactions that took place during its production process. While iCAT Analytics was motivated by ICD-11, it could potentially be applied to any crowd-based ontology-engineering project. We give an introduction to the features of iCAT Analytics and present some insights specifically for ICD-11.

[2]: With the emergence of tools for collaborative ontology engineering, more and more data about the creation process behind collaborative construction of ontologies is becoming available. Today, collaborative ontology engineering tools such as Collaborative Protege offer rich and structured logs of changes, thereby opening up new challenges and opportunities to study and analyze the creation of collaboratively constructed ontologies. While there exists a plethora of visualization tools for ontologies, they have primarily been built to visualize aspects of the final product the ontology and not the collaborative processes behind construction e.g. the changes made by contributors over time. To the best of the authors' knowledge, there exists no ontology visualization tool today that focuses primarily on visualizing the history behind collaboratively constructed ontologies. Since the ontology engineering processes can influence the quality of the final ontology, they believe that visualizing process data represents an important stepping-stone towards better understanding of managing the collaborative construction of ontologies in the future. In this application paper, the authors present a tool-PragmatiX-which taps into structured change logs provided by tools such as Collaborative Protege to visualize various pragmatic aspects of collaborative ontology engineering. The tool is aimed at managers and leaders of collaborative ontology engineering projects to help them in monitoring progress, in exploring issues and problems, and in tracking quality-related issues such as overrides and coordination among contributors. The paper makes the following contributions: i They present PragmatiX, a tool for visualizing the creation process behind collaboratively constructed ontologies ii the authors illustrate the functionality and generality of the tool by applying it to structured logs of changes of two large collaborative ontology-engineering projects and iii they conduct a heuristic evaluation of the tool with domain experts to uncover early design challenges and opportunities for improvement. Finally, the authors hope that this work sparks a new line of research on visualization tools for collaborative ontology engineering projects.
P " [1] , and [2] have created and further developed , a tool to browse an ontology and visualize aspects of its history. PragmatiX also provides quantitative insights into the creation process. The authors applied it to the analysis of the ICD-11 project.