Ontology Development Projects Research Articles

EAPB: entropy-aware path-based metric for ontology quality

BackgroundEntropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.ResultsWe propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.ConclusionsWe leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate).

Dead simple OWL design patterns

BackgroundBio-ontologies typically require multiple axes of classification to support the needs of their users. Development of such ontologies can only be made scalable and sustainable by the use of inference to automate classification via consistent patterns of axiomatization. Many bio-ontologies originating in OBO or OWL follow this approach. These patterns need to be documented in a form that requires minimal expertise to understand and edit and that can be validated and applied using any of the various programmatic approaches to working with OWL ontologies.ResultsHere we describe a system, Dead Simple OWL Design Patterns (DOS-DPs), which fulfills these requirements, illustrating the system with examples from the Gene Ontology.ConclusionsThe rapid adoption of DOS-DPs by multiple ontology development projects illustrates both the ease-of use and the pressing need for the simple design pattern system we have developed.

Scheduling ontology development projects

In the ontology engineering field, key aspects of real-world business contexts are not normally taken into account. One of these crucial aspects is that of planning and scheduling. Software engineering practitioners use different approaches and tools for planning and scheduling software development projects, whereas their counterparts in ontology engineering encounter many problems to create project plans and schedules. To bridge the gap we have created a method and a tool (the latter called gOntt) for systematizing the scheduling of ontology development projects in the context of the NeOn Methodology. In this paper we try to explain the methodological pillars in which method and tool are grounded.

Special Issue on: Twelfth International Conference on Ontologies, Databases, and Applications of Semantics.

Guest Editorial To foster the dissemination of the best ideas and results, the Journal on Data Semantics (JoDS) has a tradition to publish extended versions of the best papers from selected conferences whose scope encompasses or intersects the scope of the journal. This issue includes the extended versions of four selected best papers from the Twelfth International Conference on Ontologies, Databases, and Applications of Semantics (ODBASE 2013), which took place in September, 2013 in Graz, Austria. Originally, five papers from ODBASE 2013 were selected based on their quality, relevance and significance, and the viability of extending their results. All extended papers went through a strict peer review process, and the authors were required to revise the papers based on the concerns expressed by the reviewers. Finally, four papers were accepted and one was rejected. The first paper, by Lukasiewicz et al., introduces two models for “Preference-Based Query Answering in Probabilistic Datalog+/- Ontologies:” one representing user preferences and one representing the (probabilistic) uncertainty with which inferences are made. The paper proposes four specific operators and studies their semantic and computational properties. The paper also provides an algorithm for ranking answers based on the iteration of the well-known skyline answers to a query and shows that, under certain conditions, it runs in polynomial time in the data complexity. In collaborative and interoperable information systems it is frequently necessary to export a subset of an ontology, in particular to export all instances and axioms subsumed by a shared domain ontology. The second paper, by Kopke et al., proposes methods for “Efficient Projection of Ontologies.” These methods are based on a deep analysis of properties of axiom sets that can be used to efficiently filter redundancies during the export process and provide methods to further minimize redundancies in the result. The paper evaluates the performance of the approach using standard benchmarking ontologies with large Aboxes and a use case of the bio-medical domain. The third paper, by Wang et al., introduces methods for “Analysis and Prediction of User Editing Patterns in Ontology Development Projects.” The paper uses data mining technique, specifically the association rule mining, to investigate how to predict the next editing operation that a user will make based on the change history of two ontologies, ICTM and ICD-11, which have been developed in WebProtege, a web-based open-source ontology editing environment. The paper evaluates the impact of different training and testing window sizes on the prediction accuracies. The paper also evaluates prediction accuracies across different user groups and different ontologies. The fourth paper, by Geibel et al., introduces “Ontology-Based Information Extraction: Identifying Eligible Patients for Clinical Trials in Neurology.” The designed clinical research data warehouse makes use of information extracted from clinical documents like admission reports, radiological findings, and discharge letters. It is supported by clinical application ontologies, which enable the identification of main terms and their properties, as well as semantic search with synonyms, hypernyms, and syntactic variants. The paper provides a thorough evaluation of the deployed systems based on real data related to clinical trials conducted by the neurology departments. Guest Editors

Analysis and Prediction of User Editing Patterns in Ontology Development Projects.

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.

ONTOCOM: A reliable cost estimation method for ontology development projects

We present ONTOCOM, a method to estimate the costs of ontology engineering, as well as project management tools that support the application of the method. ONTOCOM is part of a broader framework we have developed over the five years, whose aim is to assess the business value of semantic technologies through a suite of methods, estimation models and project management tools, by which the costs and benefits of the corresponding projects are defined, measured and analyzed. The framework supports the engineering of different types of knowledge structures, including ontologies, taxonomies and folksonomies, and of information management systems leveraging such knowledge structures. It also includes benefit analysis models whose results can be used in conjunction with cost-related information in order to identify potential cost savings and to assess the feasibility of specific engineering strategies, in particular ontology reuse. The application of the methods proposed in the framework is supported by project management tools which can be used to customize these methods to a given project environment, to evaluate and validate the underlying estimations using empirical data, and to take into account their results for planning and controlling purposes.

Measuring design complexity of semantic web ontologies

Ontology languages such as OWL are being widely used as the Semantic Web movement gains momentum. With the proliferation of the Semantic Web, more and more large-scale ontologies are being developed in real-world applications to represent and integrate knowledge and data. There is an increasing need for measuring the complexity of these ontologies in order for people to better understand, maintain, reuse and integrate them. In this paper, inspired by the concept of software metrics, we propose a suite of ontology metrics, at both the ontology-level and class-level, to measure the design complexity of ontologies. The proposed metrics are analytically evaluated against Weyuker’s criteria. We have also performed empirical analysis on public domain ontologies to show the characteristics and usefulness of the metrics. We point out possible applications of the proposed metrics to ontology quality control. We believe that the proposed metric suite is useful for managing ontology development projects.