Analysis and Prediction of User Editing Patterns in Ontology Development Projects

Abstract

The development of real-world ontologies is a complex undertaking, commonly involving a group of domain experts with different expertise that work together in a collaborative setting. These ontologies are usually large scale and have complex structures. To assist in the authoring process, ontology tools are key at making the editing process as streamlined as possible. Being able to predict confidently what the users are likely to do next as they edit an ontology will enable us to focus and structure the user interface accordingly and to facilitate more efficient interaction and information discovery. In this paper, we use data mining, specifically the association rule mining, to investigate whether we are able to predict the next editing operation that a user will make based on the change history. We simulated and evaluated continuous prediction across time using sliding window model. We used the association rule mining to generate patterns from the ontology change logs in the training window and tested these patterns on logs in the adjacent testing window. We also evaluated the impact of different training and testing window sizes on the prediction accuracies. At last, we evaluated our prediction accuracies across different user groups and different ontologies. Our results indicate that we can indeed predict the next editing operation a user is likely to make. We will use the discovered editing patterns to develop a recommendation module for our editing tools, and to design user interface components that better fit with the user editing behaviors.

Comparing Ontology Authoring Workflows with Protege: In the Laboratory, in the Tutorial and in the ‘Wild’

“…predicting what activity is going to happen next) as long as the ontology is known. This prediction is especially powerful when ontologies are at the maintenance stage, where the repertoire of exhibited behaviours is reduced [35]. This outcome confirms our finding of the predictability of some sequences of events and suggests that ontology authoring is an enterprise containing a significant number of regular activities.…”

Development and Evaluation of Ontologies in Traditional Medicine: A Review Study

“…It is also the first version to use OWL as its representation format and includes a chapter on traditional medicine. 41 The International Classification of Traditional Medicine (ICTM) is another terminology in the WHO Family of International Classifications. Its structure and development process is very similar to developing ICD-11.…”

“…Even though ICTM shares some of the structures with ICD-11, there are many characteristics that are specific only to traditional medicine. 41 It seems that advances in developing these classifications would help to expand the existing ontologies in traditional medicine. Moreover, as the data become more available and structured, a more accurate picture of medical practices will be available for future policy development.…”

Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction Based on Innovation-Adoption Priors

“…For example, Pesquita and Couto [23] introduced a method for suggesting areas of biomedical ontologies that will likely be extended in the future. Similarly Wang et al [26] proposed an approach for forecasting patterns in ontology development, with the aim of suggesting which part of an ontology will be next edited by users. Another relevant approach is iDTM (infinite dynamic topic model) [1], which studies the birth, death and evolution of topics in a text stream.…”

“…To alleviate this task is possible to use approaches for ontology evolution and ontology learning. The first task aims to extend, refine and enrich an ontology based on current domain knowledge [26,23]. For example, an ontology of research areas should be updated regularly by including topics which emerged after the last version of the ontology was published.…”