Towards a Machine of a Process (MOP) ontology to facilitate e-commerce of industrial machinery

Adapting to user's requirements is a key factor for enterprise success. Despite the existence of several approaches that point in this direction, simplifying integration and interoperability among users, suppliers and the enterprise during product lifecycle, is still an open issue. Ontologies have been used in some manufacturing applications and they promise to be a valid approach to model manufacturing resources of enterprises (e.g. machinery and raw material). Nevertheless, in this domain, most of the ontologies have been developed following methodologies based on development from scratch, thus ontologies previously developed have been discarded. Such ontological methodologies tend to hold the interoperability issues in some level. In this paper, a method that integrates ontology reuse with ontology validation and learning is presented. An upper (top-level) ontology for manufacturing was used as a reference to evaluate and to improve specific domain ontology. The evaluation procedure was based on the systemic methodology for ontology learning (SMOL). As a result of the application of SMOL, an ontology entitled Machine of a Process (MOP) was developed. The terminology included in MOP was validated by means of a text mining procedure called Term Frequency–Inverse Document Frequency (TF–IDF) which was carried out on documents from the domain in this study. Competency questions were performed on preexisting domain ontologies and MOP, proving that this new ontology has a performance better than the domain ontologies used as seed.     

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.     

一种面向语义网服务的本体映射框架*

本体的异构性阻碍了语义网服务的互操作。从解决语义网服务中本体的异构问题出发,同时考虑到目前的本体映射系统大多效率不高、映射结果不够准确的问题,提出了一种适用于语义网服务的本体映射方法及系统框架。该方法利用机器学习技术来提高本体映射的自动化程度,利用综合评判技术修正映射结果,以提高本体映射的准确率。采用OAEI 2007的基准测试数据集benchmarks进行实验测试,结果表明本系统的性能基本达到预期效果,能够有效地解决语义网服务中的本体异构问题。     

An autonomous service‐platform to support distributed ontology‐based context‐aware agents

The use of semantic technology has recently witnessed a huge increase. One of the areas in which this technology is being used increasingly more often is that of context‐aware agents. However, the use of ontologies in general and reasoning in particular can rapidly become resource intensive. Certainly if the data set, called the A‐Box, used by these agents grows considerably over time. Moreover, in order to create context‐aware applications, taking into account a wide range of different data sets and context parameters, agents have to be provided to expose that data. The collaboration between the agents in the system is necessary to correlate the information and augment the intelligence and added value of the context‐aware agents. Therefore, there is a need to have a distributed approach by means of a service‐platform, where the different agents in a context‐aware environment can collaborate. The main focus of this article is on the research on the design of a service‐platform for semantic ontology‐based context‐aware collaboration. The platform architecture to allow the collaboration and scheduling, together with the associated algorithms, will be presented. The engineering and implementation details will be highlighted. By means of detailed UML sequence diagrams, we will present the workflow and collaboration between the different modules in the platform. Additionally, supporting developments, such as the meta‐ontology and our ontology generator, OTAGen, will be presented. Furthermore, we will detail how the platform can operate in an autonomous way, taking into account the changing context of the agents in the platform.     

Incremental Classification of Description Logics Ontologies

The development of ontologies involves continuous but relatively small modifications. However, existing ontology reasoners do not take advantage of the similarities between different versions of an ontology. In this paper, we propose a collection of techniques for incremental reasoning—that is, reasoning that reuses information obtained from previous versions of an ontology. We have applied our results to incremental classification of OWL ontologies and found significant improvement over regular classification time on a set of real-world ontologies.     

Semantic oriented ontology cohesion metrics for ontology-based systems

Ontologies play a core role to provide shared knowledge models to semantic-driven applications targeted by Semantic Web. Ontology metrics become an important area because they can help ontology engineers to assess ontology and better control project management and development of ontology based systems, and therefore reduce the risk of project failures. In this paper, we propose a set of ontology cohesion metrics which focuses on measuring (possibly inconsistent) ontologies in the context of dynamic and changing Web. They are: Number of Ontology Partitions (NOP), Number of Minimally Inconsistent Subsets (NMIS) and Average Value of Axiom Inconsistencies (AVAI). These ontology metrics are used to measure ontological semantics rather than ontological structure. They are theoretically validated for ensuring their theoretical soundness, and further empirically validated by a standard test set of debugging ontologies. The related algorithms to compute these ontology metrics also are discussed. These metrics proposed in this paper can be used as a very useful complementarity of existing ontology cohesion metrics.     

Ontology design with a granular approach

Ontology design for complex applications is quite a challenge. The quality of an ontology is highly dependent upon the capabilities of designers, and the collaborative design process is hampered by the difficulty of balancing the viewpoints of different designers. In this paper, we present a granular view of ontology: ontologies are granular, ontologies are granular approximations of conceptualizations and conceptual-relation granules of an ontology are ordered tuples. We then propose a corresponding granular ontology design approach. In our granular ontology design approach, the unified granular cognition level and hierarchies of sub-concepts are initialized before ontological terms are designed in detail, which reduces the subjective effects of the capabilities of designers. Our approach also introduces the idea of optimization to choose an optimal subset, which can best approximate the real concept domain, from the knowledge rule set presented by different domain experts. The optimal subset is chosen on the basis of the principle of granular ontology knowledge structure.     

Cohesion and coupling metrics for ontology modules

In recent years, an increasing number of ontologies and semantic web applications have been developed and used. A conscious effort has been made to develop methods to modularize ontologies. These methods contribute to building a new ontology. However, few studies have focused on the evaluative methods for ontology modules. In this study, we propose novel metrics to measure ontology modularity. To evaluate the ontology modules, we introduce cohesion and coupling based on the theory of software metrics. A cohesion metric and two coupling metrics were used to measure cohesion and coupling for ontology modules. The proposed metrics provide more detailed support in considering the different types of relationships between classes in ontology modules. In addition, the new coupling metrics contribute to checking the consistency between the ontology modules and their original ontology. The proposed metrics were validated using well-known verification frameworks and empirical experiments to complement the previous investigations. The results of this study offer ontology engineers valuable criteria with which to evaluate ontology modules and help ontology users select qualifying ontology modules.     

Ontology Evolution: Not the Same as Schema Evolution

As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.     

Thesaurus or Logical Ontology,Which One Do We Need for Text Mining?

Ontologies are recognised as important tools, not only for effective and efficient information sharing, but also for information extraction and text mining. In the biomedical domain, the need for a common ontology for information sharing has long been recognised, and several ontologies are now widely used. However, there is confusion among researchers concerning the type of ontology that is needed for text mining , and how it can be used for effective knowledge management, sharing, and integration in biomedicine. We argue that there are several different ways to define an ontology and that, while the logical view is popular for some applications, it may be neither possible nor necessary for text mining. We propose a text-centered approach for knowledge sharing, as an alternative to formal ontologies. We argue that a thesaurus (i.e. an organised collection of terms enriched with relations) is more useful for text mining applications than formal ontologies.     

Understanding ontology evolution: A change detection approach

In this article, we propose a change detection approach in the context of an ontology evolution framework for OWL DL ontologies. The framework allows ontology engineers to request and apply changes to the ontology they manage. Furthermore, the framework assures that the ontology and its depending artifacts remain consistent after changes have been applied. Innovative is that the framework includes a change detection mechanism that allows generating automatically a detailed overview of changes that have occurred based on a set of change definitions. In addition, different users (such as maintainers of depending artifacts) may have their own set of change definitions, which results into different overviews of the changes, each providing a different view on how the ontology has been changed. Using these change definitions, also different levels of abstraction are supported. Both features will enhance the understanding of the evolution of an ontology for different users.     

BiOSS: A system for biomedical ontology selection

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service.     

Aondê: An ontology Web service for interoperability across biodiversity applications

Biodiversity research requires associating data about living beings and their habitats, constructing sophisticated models and correlating all kinds of information. Data handled are inherently heterogeneous, being provided by distinct (and distributed) research groups, which collect these data using different vocabularies, assumptions, methodologies and goals, and under varying spatio-temporal frames. Ontologies are being adopted as one of the means to alleviate these heterogeneity problems, thus helping cooperation among researchers. While ontology toolkits offer a wide range of operations, they are self-contained and cannot be accessed by external applications. Thus, the many proposals for adopting ontologies to enhance interoperability in application development are either based on the use of ontology servers or of ontology frameworks. The latter support many functions, but impose application recoding whenever ontologies change, whereas the first supports ontology evolution, but for a limited set of functions.This paper presents Aondê—a Web service geared towards the biodiversity domain that combines the advantages of frameworks and servers, supporting ontology sharing and management on the Web. By clearly separating storage concerns from semantic issues, the service provides independence between ontology evolution and the applications that need them. The service provides a wide range of basic operations to create, store, manage, analyze and integrate multiple ontologies. These operations can be repeatedly invoked by client applications to construct more complex manipulations. Aondê has been validated for real biodiversity case studies.     

Where to publish and find ontologies? A survey of ontology libraries

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries affects their features, content, and potential exploitation in applications. From reviewing 11 ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address.     

Repairing and reasoning with inconsistent and uncertain ontologies

With the development of semantic web, the quality and correctness of ontologies play more and more important roles in semantic representation and knowledge sharing. However, ontologies are often inconsistent and uncertain in real situations. Because of the difficulty in ensuring the quality of ontologies, there is an increasing need for dealing with the inconsistency and uncertainty in real-world applications of ontological reasoning and management. This paper adopts two methods to handle the inconsistent and uncertain ontologies: the first one is to repair the inconsistency, algorithms RIO and RIUO are proposed to compute the candidate repair set, the consistency of ontology could be recovered through deleting or modifying the axioms in candidate repair set; the second one is to develop a non-standard reasoning method to obtain meaningful answers, algorithms RMU and RMIU are proposed to perform query-specific reasoning methods for inconsistent and uncertain ontologies without changing the original ontologies. Finally the prototype system is constructed and the experiment results validate the usability and effectiveness of our approaches.     

Ontology selection ranking model for knowledge reuse

Ontology reuse is recommended as a key factor to develop cost-effective and high-quality ontologies because it could reduce development costs by avoiding rebuilding existing ontologies. Selecting the desired ontology from existing ontologies is essential for ontology reuse. Until now, much research on ontology selection has focused on lexical-level support. However, in these cases, it is almost impossible to find an ontology that includes all the concepts matched by the search terms at the semantic level. Finding an ontology that meets users’ needs requires a new ontology selection and ranking mechanism based on semantic similarity matching. We propose an ontology selection and ranking model consisting of selection standards and metrics based on better semantic matching capabilities. The model we propose presents two novel features different from previous research models. First, it enhances the ontology selection and ranking method practically and effectively by enabling semantic matching of taxonomy or relational linkage between concepts. Second, it identifies what measures should be used to rank ontologies in the given context and what weight should be assigned to each selection measure.     

受限本体相似

在从不同的语义Web上得到用本体表达的文档资源以后,这些文档资源通常被转换成基于同一个本体的本体描述,这样既便于对文档的分析,又便于在此基础上进行信息抽取.这些文档本体之间仅仅在实例和关系层上彼此相互不同,在类、属性、规则、谓词方面都基本相同.对这种文档的检索,一个最普通的操作就是计算本体之间的相似性.很多计算本体相似性的方法基本上都是以分别属于不同本体的实体之间配对比较来实现,而且往往要考虑所有相关的元素.这不仅增加了计算复杂度,还会遇到循环计算的问题.在对语义网本体语言的推理能力进行研究以后,提出了一种基于知识推理的二阶本体相似技术,解决了循环计算的问题.     

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.     

Ontology based text indexing and querying for the semantic web

This publication shows how the gap between the HTML based internet and the RDF based vision of the semantic web might be bridged, by linking words in texts to concepts of ontologies. Most current search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. However, the indexes do not contain synonyms, cannot differentiate between homonyms (‘mouse’ as a pointing vs. ‘mouse’ as an animal) and users receive different search results when they use different conjugation forms of the same word. In this publication, we present a system that uses ontologies and Natural Language Processing techniques to index texts, and thus supports word sense disambiguation and the retrieval of texts that contain equivalent words, by indexing them to concepts of ontologies.

For this purpose, we developed fully automated methods for mapping equivalent concepts of imported RDF ontologies (for this prototype WordNet, SUMO and OpenCyc). These methods will thus allow the seamless integration of domain specific ontologies for concept based information retrieval in different domains.

To demonstrate the practical workability of this approach, a set of web pages that contain synonyms and homonyms were indexed and can be queried via a search engine like query frontend. However, the ontology based indexing approach can also be used for other data mining applications such text clustering, relation mining and for searching free text fields in biological databases. The ontology alignment methods and some of the text mining principles described in this publication are now incorporated into the ONDEX system http://ondex.sourceforge.net/.     

Ontologies application in organizational learning: A literature review

Although ontologies and organizational learning are issues that have been discussed for many years, there is not an approach on literature that gives an overview about how both issues have been applied together. This literature review has the objective of exploring how ontologies are being applied in the organizational learning process recently; as a consequence, only studies from the year of 2005 onwards have been searched. The identification process produced 353 papers from 11 different databases. After applying the exclusion criteria, the set was reduced to 11 papers, which clearly fitted to the criteria defined for accomplishment of the systematic review, which were then analyzed and classified. The papers have been classified according to the structure and level of the ontologies. Furthermore, the Information Technology (IT) used in conjunction with ontology was identified, as well as the way ontologies and IT can act as a means of facilitating the organizational learning process. It was observed that although ontologies are rather important, a very few number of researches have applied ontologies in the organizational learning processes. In a general way, ontologies and IT encourage the sharing of knowledge and formalization.