Quality Of Ontology Research Articles

Evaluation of the Ontological Knowledge Model/ Ontoloģiskā zināšanu modeļa novērtēšana/ Оценка качества онтологической модели знаний

An ontology is an explicit formal conceptualization of some domain of interest. Ontology application is widely used in multi-agent systems. Ontology provides the channel through which software agents interact; therefore, if the ontology definitions have not been sufficiently evaluated, communication between software agents may not succeed. Ontology evaluation guarantees to end users the correctness and completeness of ontology definitions and software. The goal is to detect wrong, incomplete or missed definitions in the ontology. The study is based on multi-agent system application for raw materials management task. The common ontology is used for agent cooperation; the quality of ontology is measured with ontology evaluation techniques.

Visualizing Information Science Knowledge by Modelling Domain Ontology (OIS)

Information Science (IS) is one of the areas that need a unified ontology model to facilitate information in order to access the heterogeneous data resources, and share a common understanding of the domain knowledge. The aim of the study is to develop a generic model of ontology that serves as a foundation of knowledge modelling for applications and aggregation with other ontologies. This study adopts the Methontology methodology to develop an Ontology of Information Science (OIS). To support ontology development, the virtual community of practice of Information science (Ontocop) was employed. The Ontology was coded using Protégé and the OWL web ontology language. The main achievement of the study is that a new model of Information Science Ontology (OIS) has been constructed, which is a generic model that contains only the key objects and associated attributes with their relationships. The model defines the 706 concept, which will be widely used in Information Science applications. The research reveals that OIS ontology is a model that meets the ontology quality criteria for the subject area. It is concluded that OIS ontology unifies information science knowledge, which is composed of Library, Computer, and Archival science, by creating a theoretical base that is useful for further practical systems. The OIS ontology can be reused as the basis for any domain development.

BUILDING AN ADVANCE DOMAIN ONTOLOGY MODEL OF INFORMATION SCIENCE (OIS)

The paper describes the process of modelling domain knowledge of Information Science (IS) by creating an Ontology of Information Science domain (OIS). It also reports on the life cycle of the ontology building process using Methontology, based on the IEEE standard for development software life cycle process, which mainly consists of: specification, conceptualization, formalization, implementation, maintenance and evaluation. The information resource used in acquisition and evaluation has been obtained from Information Science. The conceptualization consists of identifying IS concepts and grouping them into a hierarchy tree based on a faceted classification scheme. The OIS ontology is formalized by using the ontology editor Protege to generate the ontology code. The achieved result is OIS ontology which has fourteen facets: Actors, Method, Practice, Studies, Mediator, Kinds, Domains, Resources, Legislation, Philosophy & Theories, Societal, Tool, Time and Space. The model is evaluated using ontology quality criteria to check the ontology’s usefulness, and how it could be transferred into application ontology for Information Science education.

RARGE II: An Integrated Phenotype Database of Arabidopsis Mutant Traits Using a Controlled Vocabulary

Arabidopsis thaliana is one of the most popular experimental plants. However, only 40% of its genes have at least one experimental Gene Ontology (GO) annotation assigned. Systematic observation of mutant phenotypes is an important technique for elucidating gene functions. Indeed, several large-scale phenotypic analyses have been performed and have generated phenotypic data sets from many Arabidopsis mutant lines and overexpressing lines, which are freely available online. Since each Arabidopsis mutant line database uses individual phenotype expression, the differences in the structured term sets used by each database make it difficult to compare data sets and make it impossible to search across databases. Therefore, we obtained publicly available information for a total of 66,209 Arabidopsis mutant lines, including loss-of-function (RATM and TARAPPER) and gain-of-function (AtFOX and OsFOX) lines, and integrated the phenotype data by mapping the descriptions onto Plant Ontology (PO) and Phenotypic Quality Ontology (PATO) terms. This approach made it possible to manage the four different phenotype databases as one large data set. Here, we report a publicly accessible web-based database, the RIKEN Arabidopsis Genome Encyclopedia II (RARGE II; http://rarge-v2.psc.riken.jp/), in which all of the data described in this study are included. Using the database, we demonstrated consistency (in terms of protein function) with a previous study and identified the presumed function of an unknown gene. We provide examples of AT1G21600, which is a subunit in the plastid-encoded RNA polymerase complex, and AT5G56980, which is related to the jasmonic acid signaling pathway.

Ontology Quality Assurance with the Crowd

The Semantic Web has the potential to change the Web as we know it. However, the community faces a significant challenge in managing, aggregating, and curating the massive amount of data and knowledge. Human computation is only beginning to serve an essential role in the curation of these Web-based data. Ontologies, which facilitate data integration and search, serve as a central component of the Semantic Web, but they are large, complex, and typically require extensive expert curation. Furthermore, ontology-engineering tasks require more knowledge than is required in a typical crowdsourcing-task. We have developed ontology-engineering methods that leverage the crowd. In this work, we describe our general crowdsourcing workflow. We then highlight our work on applying this workflow to ontology verification and quality assurance. In a pilot study, this method approaches expert ability, finding the same errors that experts identified with 86% accuracy in a faster and more scalable fashion. The work provides a general framework with which to develop crowdsourcing methods for the Semantic Web. In addition, it highlights opportunities for future research in human computation and crowdsourcing.

Visualizing Information Science Knowledge by Modelling Domain Ontology (OIS)

Information Science (IS) is one of the areas that need a unified ontology model to facilitate information in order to access the heterogeneous data resources, and share a common understanding of the domain knowledge. The aim of the study is to develop a generic model of ontology that serves as a foundation of knowledge modelling for applications and aggregation with other ontologies. This study adopts the Methontology methodology to develop an Ontology of Information Science (OIS). To support ontology development, the virtual community of practice of Information science (Ontocop) was employed. The Ontology was coded using Protégé and the OWL web ontology language. The main achievement of the study is that a new model of Information Science Ontology (OIS) has been constructed, which is a generic model that contains only the key objects and associated attributes with their relationships. The model defines the 706 concept, which will be widely used in Information Science applications. The research reveals that OIS ontology is a model that meets the ontology quality criteria for the subject area. It is concluded that OIS ontology unifies information science knowledge, which is composed of Library, Computer, and Archival science, by creating a theoretical base that is useful for further practical systems. The OIS ontology can be reused as the basis for any domain development.

Approaching the feasibility of SBVR as modeling language for ontology development: An exploratory experiment

Ontology development techniques still constitute an open research area despite its importance in semantic aware information systems. Until now, most methods have used UML in supporting ontology development process. Recent works propose the mapping of business rules expressions to ontology statements as a building technique by means of SBVR language. However, there is still no experimental research comparing such approaches.Aim of this work is to evaluate the feasibility of mapping business domain expressions to ontology statements. An exploratory experiment comparing performance of techniques based on UML and SBVR languages is presented. Comparison is rooted in the quality assessment of the ontologies developed by 10 equally sized groups randomly conformed by 30 undergraduate engineering students and applying such techniques.Developed ontologies largely outperform the minimally acceptable quality, according to the considered quality assessment framework. There is no statistical significant difference between the quality scores of the ontologies developed by means of UML and SBVR techniques, in any of the assessed quality dimensions.The feasibility of mapping business domain expressions to ontology statements is shown: ontologies developed by means of a SBVR based approach at least equate the quality of ontologies developed by using an UML based method. Results confirm previous research about the effectiveness of UML approaches for conceptualizing lightweight ontologies while stressing the potential of the SBVR language to express complex notions of a domain of interest. The potential of SBVR to OWL 2 mappings as an ontology development technique worthy of further study is highlighted.

실시간 공정 모니터링을 통한 제품 품질 예측 모델 개발

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

A system for information retrieval in a medical digital library based on modular ontologies and query reformulation

Ontologies have proven to be useful in the area of Information Retrieval and the biomedical informatics community has acknowledged, in recent years, their utility. However, building and updating manually ontologies is a long and tedious task. This paper proposes a system that allows any search engine to develop its semantic layer by applying ontology learning techniques on Web snippets and applies it to a well-known medical digital library, PubMed. The new system (SemPubMed) automatically builds new ontology fragments related to the user's query and then it reformulates queries using the new concepts in order to improve information retrieval. Our system has endured a twofold evaluations. On the one hand, we have evaluated the quality of the modular ontologies built by the system. On the other hand, we have studied how the semantic reformulation of the queries has led to an improvement of the quality of the results given by PubMed, both in terms of precision and recall. Obtained results show that adding semantic layer to PubMed enables an improvement of query reformulation and predicted ranking score.

A Semantic Approach for Transforming XML Data into RDF Ontology

This paper deals with the problem of transforming eXtensible Markup Language (XML) data into the Resource Description Language (RDF) which can be understood by the computer. While it is not difficult to customize XML for arbitrary data, the effective transformation is not straightforward and the result may be not semantically richer than the source document since the redundancy data resulted from the duplicate elements in XML schema. To cope with this problem, we propose an approach to measure the similarity between these duplicates before giving the transforming strategy. The similarity measure is the combination of the children and ancestor factors, which describe the relationship of elements. The experimental results show that the proposed method gives the high degree of accuracy and produces better quality of RDF ontology.

A semantic model for species description applied to the ensign wasps (hymenoptera: evaniidae) of New Caledonia.

Taxonomic descriptions are unparalleled sources of knowledge of life's phenotypic diversity. As natural language prose, these data sets are largely refractory to computation and integration with other sources of phenotypic data. By formalizing taxonomic descriptions using ontology-based semantic representation, we aim to increase the reusability and computability of taxonomists' primary data. Here, we present a revision of the ensign wasp (Hymenoptera: Evaniidae) fauna of New Caledonia using this new model for species description. Descriptive matrices, specimen data, and taxonomic nomenclature are gathered in a unified Web-based application, mx, then exported as both traditional taxonomic treatments and semantic statements using the OWL Web Ontology Language. Character:character-state combinations are then annotated following the entity–quality phenotype model, originally developed to represent mutant model organism phenotype data; concepts of anatomy are drawn from the Hymenoptera Anatomy Ontology and linked to phenotype descriptors from the Phenotypic Quality Ontology. The resulting set of semantic statements is provided in Resource Description Framework format. Applying the model to real data, that is, specimens, taxonomic names, diagnoses, descriptions, and redescriptions, provides us with a foundation to discuss limitations and potential benefits such as automated data integration and reasoner-driven queries. Four species of ensign wasp are now known to occur in New Caledonia: Szepligetella levipetiolata, Szepligetella deercreeki Deans and Mikó sp. nov., Szepligetella irwini Deans and Mikó sp. nov., and the nearly cosmopolitan Evania appendigaster. A fifth species, Szepligetella sericea, including Szepligetella impressa, syn. nov., has not yet been collected in New Caledonia but can be found on islands throughout the Pacific and so is included in the diagnostic key. [Biodiversity informatics; Evaniidae; New Caledonia; new species; ontology; semantic phenotypes; semantic species description; taxonomy.]

FYPO: the fission yeast phenotype ontology

Motivation: To provide consistent computable descriptions of phenotype data, PomBase is developing a formal ontology of phenotypes observed in fission yeast.Results: The fission yeast phenotype ontology (FYPO) is a modular ontology that uses several existing ontologies from the open biological and biomedical ontologies (OBO) collection as building blocks, including the phenotypic quality ontology PATO, the Gene Ontology and Chemical Entities of Biological Interest. Modular ontology development facilitates partially automated effective organization of detailed phenotype descriptions with complex relationships to each other and to underlying biological phenomena. As a result, FYPO supports sophisticated querying, computational analysis and comparison between different experiments and even between species.Availability: FYPO releases are available from the Subversion repository at the PomBase SourceForge project page (https://sourceforge.net/p/pombase/code/HEAD/tree/phenotype_ontology/). The current version of FYPO is also available on the OBO Foundry Web site (http://obofoundry.org/).Contact: mah79@cam.ac.uk or vw253@cam.ac.uk

Effects of Guideline-Based Training on the Quality of Formal Ontologies: A Randomized Controlled Trial

BackgroundThe importance of ontologies in the biomedical domain is generally recognized. However, their quality is often too poor for large-scale use in critical applications, at least partially due to insufficient training of ontology developers.ObjectiveTo show the efficacy of guideline-based ontology development training on the performance of ontology developers. The hypothesis was that students who received training on top-level ontologies and design patterns perform better than those who only received training in the basic principles of formal ontology engineering.MethodsA curriculum was implemented based on a guideline for ontology design. A randomized controlled trial on the efficacy of this curriculum was performed with 24 students from bioinformatics and related fields. After joint training on the fundamentals of ontology development the students were randomly allocated to two groups. During the intervention, each group received training on different topics in ontology development. In the assessment phase, all students were asked to solve modeling problems on topics taught differentially in the intervention phase. Primary outcome was the similarity of the students’ ontology artefacts compared with gold standard ontologies developed by the authors before the experiment; secondary outcome was the intra-group similarity of group members’ ontologies.ResultsThe experiment showed no significant effect of the guideline-based training on the performance of ontology developers (a) the ontologies developed after specific training were only slightly but not significantly closer to the gold standard ontologies than the ontologies developed without prior specific training; (b) although significant differences for certain ontologies were detected, the intra-group similarity was not consistently influenced in one direction by the differential training.ConclusionMethodologically limited, this study cannot be interpreted as a general failure of a guideline-based approach to ontology development. Further research is needed to increase insight into whether specific development guidelines and practices in ontology design are effective.

How ontologies are made: Studying the hidden social dynamics behind collaborative ontology engineering projects

Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to their construction is critical not only in understanding but consequently also in evaluating the ontologies. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.

An analysis of FMA using structural self-bisimilarity

As ontologies are mostly manually created, they tend to contain errors and inconsistencies. In this paper, we present an automated computational method to audit symmetric concepts in ontologies by leveraging self-bisimilarity and linguistic structure in the concept names. Two concepts A and B are symmetric if concept B can be obtained from concept A by replacing a single modifier such as “left” with its symmetric modifier such as “right.” All possible local structural types for symmetric concept pairs are enumerated according to their local subsumption hierarchy, and the pairs are further classified into Non-Matches and Matches. To test the feasibility and validate the benefits of this method, we computed all the symmetric modifier pairs in the Foundational Model of Anatomy (FMA) and selected six of them for experimentation. 9893 Non-Matches and 221 abnormal Matches with potential errors were discovered by our algorithm. Manual evaluation by FMA domain experts on 176 selected Non-Matches and all the 221 abnormal Matches found 102 missing concepts and 40 misaligned concepts. Corrections for them have currently been implemented in the latest version of FMA. Our result demonstrates that self-bisimilarity can be a valuable method for ontology quality assurance, particularly in uncovering missing concepts and misaligned concepts. Our approach is computationally scalable and can be applied to other ontologies that are rich in symmetric concepts.

PragmatiX

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 Protégé 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 our 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, we 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, we present a tool - PragmatiX - which taps into structured change logs provided by tools such as Collaborative Protégé 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) we present PragmatiX, a tool for visualizing the creation process behind collaboratively constructed ontologies (ii) we 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) we conduct a heuristic evaluation of the tool with domain experts to uncover early design challenges and opportunities for improvement. Finally, we hope that this work sparks a new line of research on visualization tools for collaborative ontology engineering projects.

How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects

Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics.We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to its construction is critical not only in understanding but consequently also in evaluating the ontology. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.

A unified approach for debugging is-a structure and mappings in networked taxonomies

BackgroundWith the increased use of ontologies and ontology mappings in semantically-enabled applications such as ontology-based search and data integration, the issue of detecting and repairing defects in ontologies and ontology mappings has become increasingly important. These defects can lead to wrong or incomplete results for the applications.ResultsWe propose a unified framework for debugging the is-a structure of and mappings between taxonomies, the most used kind of ontologies. We present theory and algorithms as well as an implemented system RepOSE, that supports a domain expert in detecting and repairing missing and wrong is-a relations and mappings. We also discuss two experiments performed by domain experts: an experiment on the Anatomy ontologies from the Ontology Alignment Evaluation Initiative, and a debugging session for the Swedish National Food Agency.ConclusionsSemantically-enabled applications need high quality ontologies and ontology mappings. One key aspect is the detection and removal of defects in the ontologies and ontology mappings. Our system RepOSE provides an environment that supports domain experts to deal with this issue. We have shown the usefulness of the approach in two experiments by detecting and repairing circa 200 and 30 defects, respectively.

The Ontology for Experimental Neurophysiology: a first step toward semantic annotations of neurophysiology data and metadata.

The Ontology for Experimental Neurophysiology: a first step toward semantic annotations of neurophysiology data and metadata.

Evaluation of the OQuaRE framework for ontology quality

The increasing importance of ontologies has resulted in the development of a large number of ontologies in both coordinated and non-coordinated efforts. The number and complexity of such ontologies make hard to ontology and tool developers to select which ontologies to use and reuse. So far, there are no mechanism for making such decisions in an informed manner. Consequently, methods for evaluating ontology quality are required. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies. OQuaRE has been applied to identify the strengths and weaknesses of different ontologies but, so far, this framework has not been evaluated itself. Therefore, in this paper we present the evaluation of OQuaRE, performed by an international panel of experts in ontology engineering. The results include the positive and negative aspects of the current version of OQuaRE, the completeness and utility of the quality metrics included in OQuaRE and the comparison between the results of the manual evaluations done by the experts and the ones obtained by a software implementation of OQuaRE.