Ontology Engineering Tasks Research Articles

DeepOnto: A Python package for ontology engineering with deep learning

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).

Global Ontology Entities Embeddings

Ontologies are among the most widely used types of knowledge representation formalisms. The application of deep learning techniques in the field of ontology engineering has reinforced the need to learn and generate representations of ontological data. This allows ontologies to be exploited by such models, and thus automate various ontology engineering tasks, where most of the existing tools and machine learning approaches require a numerical feature vectors associated with each concept. This paper outlines a novel approach for learning global ontology entities embeddings by exploiting the structure and the various taxonomic and semantic relationships present in ontologies, taking into account all the information present in the ontological graph and carried by the OWL/RDF triples. Thus, producing global ontology entities embeddings capturing the global ontological graph semantics and similarities enclosed in the source ontology. Three different neural network models have been proposed based on two architectures: multi-input and multi-output, trained using the contrastive estimation technique. The evaluation on OWL/RDF ontologies and word semantic similarity tasks using various graph and WordNet based similarity measures, show that our approach yields competitive results outperforming the state-of-the-art ontology and word embedding models.

Visualising the effects of ontology changes and studying their understanding with ChImp

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.

Crowd: A Visual Tool for Involving Stakeholders into Ontology Engineering Tasks

We present crowd, a web-based visual tool for ontology engineering tasks. Its aim is to involve ontology developers and domain experts into a collaborative comprehension and design of conceptual models, enhancing the communication between them and assessing their quality by fully integrating automatic reasoning in the tool. In this paper we briefly describe the initial requirements, architecture and user interface, and make an evaluation based on a use case and a comparison with related tools.

Query inseparability for [formula omitted] ontologies

We investigate the problem whether two ALC ontologies are indistinguishable (or inseparable) by means of queries in a given signature, which is fundamental for ontology engineering tasks such as ontology versioning, modularisation, update, and forgetting. We consider both knowledge base (KB) and TBox inseparability. For KBs, we give model-theoretic criteria in terms of (finite partial) homomorphisms and products and prove that this problem is undecidable for conjunctive queries (CQs), but 2ExpTime-complete for unions of CQs (UCQs). The same results hold if (U)CQs are replaced by rooted (U)CQs, where every variable is connected to an answer variable. We also show that inseparability by CQs is still undecidable if one KB is given in the lightweight DL EL and if no restrictions are imposed on the signature of the CQs. We also consider the problem whether two ALC TBoxes give the same answers to any query over any ABox in a given signature and show that, for CQs, this problem is undecidable, too. We then develop model-theoretic criteria for HornALC TBoxes and show using tree automata that, in contrast, inseparability becomes decidable and 2ExpTime-complete, even ExpTime-complete when restricted to (unions of) rooted CQs.

A two-staged approach to developing and evaluating an ontology for delivering personalized education to diabetic patients

ABSTRACTOntologies are often used in biomedical and health domains to provide a concise and consistent means of attributing meaning to medical terminology. While they are novices in terms of ontology engineering, the evaluation of an ontology by domain specialists provides an opportunity to enhance its objectivity, accuracy, and coverage of the domain itself. This paper provides an evaluation of the viability of using ontology engineering novices to evaluate and enrich an ontology that can be used for personalized diabetic patient education. We describe a methodology for engaging healthcare and information technology specialists with a range of ontology engineering tasks. We used 87.8% of the data collected to validate the accuracy of our ontological model. The contributions also enabled a 16% increase in the class size and an 18% increase in object properties. Furthermore, we propose that ontology engineering novices can make valuable contributions to ontology development. Application-specific evaluation of the ontology using a semantic-web-based architecture is also discussed.

The Protégé Project: A Look Back and a Look Forward.

Few funded research projects in computer science go on for decades. It’s even questionable whether any project should be sustained that long. Nevertheless, the Protege project at Stanford University began in the 1980s and is still going strong, helping developers to construct reusable ontologies and to build knowledge-based systems. The recognition of our work at the International Semantic Web Conference in October 2014 with the “Ten Years” Award was a great honor. The award has provided an opportunity f o r reflection—both on the Protege project itself and on the need for computational infrastructure in the AI community. Protege has become the most widely used software for building and maintaining ontologies. It is by no means the only solution, and there are known problems with Protege, but we appreciate that the system is extremely popular. Although his study is now a bit dated, Cardoso (2007) surveyed the Semantic Web community and found that two-thirds of his respondents used Protege. To date, more than 250,000 people have registered to use the software. Many Fortune 500 companies use Protege to build their ontologies. Important government projects, such as the development of the National Cancer Institute Thesaurus (Noy et al., 2008; Figure 1) and the World Health Organization’s International Classification of Diseases (ICD-11; Tudorache et al., 2010) depend on the software. Figure 1 The Protege 5 Desktop System. The figure shows the most recent version of the Protege system used to edit the National Cancer Institute Thesaurus. In the Figure, the user has selected the class Antigen Gene. The visualization ... Protege currently exists in a variety of frameworks. A desktop system (Protege 5) supports many advanced features to enable the construction and management of OWL ontologies (see Figure 1). A Web-based system (WebProtege) offers distributed access over the Internet using any Web browser and, by design, is much simpler to use for many ontology-engineering tasks (Figure 2). The Web-based version has become extremely popular, and it recently exceeded the desktop-client in its degree of usage. It is extremely handy to be able to point a Web browser to an appropriate server and to begin editing. Like a Google doc, a WebProtege ontology can be easily shared with a distributed group of users who can engage in collaborative authoring activities from wherever they happen to be logged in. The development environment used by the World Health Organization to manage ICD-11 is based on WebProtege (Tudorache et al., 2010). Figure 2 WebProtege. The Web-based version of Protege offers users and their collaborators the opportunity to share and edit ontologies online, much like a Google doc. Here we see the Ontology for Parasite Lifecycle (OPL), an ontology ... Older versions of the Protege desktop system have included support for editing ontologies represented in a frame language (namely, in the OKBC framework developed by the DARPA Knowledge Sharing Initiative in the 1990s; Neches et al., 1991). Comparable functionality has not yet been migrated to current versions of Protege, however. All versions of Protege may be downloaded from the project’s Web site (http://protege.stanford.edu). They are available under an open-source license. The paper for which the Protege team won the “Ten Years” Award (Knublauch et al., 2004) describes the first Protege system to support the World Wide Web Consortium’s recommended Web Ontology Language (OWL). We had been tracking the emerging OWL specification, and Holger Knublauch, then a post-doctoral fellow in our laboratory, worked with the rest of the team to extend Protege to create what was, at the time, the only ontology-development platform that could accommodate nearly the complete OWL specification. Protege’s support for OWL has been enhanced over the years, particularly through a very successful collaboration with Alan Rector’s CO-ODE project at the University of Manchester, and recent versions of Protege fully support the latest OWL 2.0 specification. When people think of Protege, they think of an editor for ontologies, and they think of OWL. Protege did not start out this way, however. Gennari et al. (2003) traced the history of the first 15 years of the Protege project, documenting the many shifts in perspective as Protege progressed from a student project for a Ph.D. dissertation that focused on problems of building knowledge-based systems (Musen, 1989) to a major open-source platform supported by a huge community of diverse users (Musen, 2005).

Semantic metrics

In the context of the Semantic Web, many ontology-related operations, e.g., ontology ranking, segmentation, alignment, articulation, reuse, evaluation, etc., can be boiled down to one fundamental operation: computing the similarity and/or dissimilarity among ontological entities, and in some cases among ontologies. In this paper, we review conventional metrics for computing distance and we propose a series of metrics conceived purposely for semantics rich entities. We give a formal account of semantic metrics drawn from a variety of research disciplines and enrich them with formalisation based on Description Logics. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level. We speculate on the usability of our ideas in potential ontology engineering tasks.

Human-centered ontology engineering: The HCOME methodology

The fast emergent and continuously evolving areas of the Semantic Web and Knowledge Management make the incorporation of ontology engineering tasks in knowledge-empowered organizations and in the World Wide Web more than necessary. In such environments, the development and evolution of ontologies must be seen as a dynamic process that has to be supported through the entire ontology life cycle, resulting to living ontologies. The aim of this paper is to present the Human-Centered Ontology Engineering Methodology (HCOME) for the development and evaluation of living ontologies in the context of communities of knowledge workers. The methodology aims to empower knowledge workers to continuously manage their formal conceptualizations in their day-to-day activities and shape their information space by being actively involved in the ontology life cycle. The paper also demonstrates the Human Centered ONtology Engineering Environment, HCONE, which can effectively support this methodology.