Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach

There is an increasing demand for sharing learning resources between existing learning resource systems to support reusability, exchangeability, and adaptability. The learning resources need to be annotated with ontologies into learning objects that use different metadata standards. These ontologies have introduced the problems of semantic and structural heterogeneity. This research proposes a Semantic Ontology Mapping for Interoperability of Learning Resource Systems. To enable semantic ontology mapping, this research proposes conflict detection and resolution techniques for both semantic and structural conflicts. The Semantic Bridge Ontology has been proposed as a core component for generating mapping rules to reconcile terms defined in local ontologies into terms defined in the target common ontology. This work defines the reasoning rules to classify related learning objects to enhance the powerful deductive reasoning capabilities of the system. As a consequence, ontology-based learning object metadata are generated and used by the semantic query engine to facilitate user queries of learning objects across heterogeneous learning resource systems.     

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.     

A SKOS-based framework for Subject Ontologies to improve learning experiences

Subject Ontologies represent conceptualizations of disciplinary domains in which concepts symbolize topics that are relevant for the considered domain and are associated each other by means of specific relations. Usually, these kind of lightweight ontologies are adopted in knowledge-based educational environments to enable semantic organization and search of resources and, in other cases, to support personalization and adaptation features for learning and teaching experiences. For this reason, applying effective management methodologies for Subject Ontologies is a crucial aspect in engineering the environments. In particular, this paper proposes an approach to use SKOS (a Semantic Web-based vocabulary providing a standard way to represent knowledge organization systems) for modelling subject ontologies. Moreover, the paper underlines the main benefits of SKOS. It focuses on alternative strategies for storing and accessing ontologies in order to support the knowledge sharing, knowledge reusing, planning, assessment, customization and adaptation processes related to learning scenarios. The results of an early experimentation allowed the authors defining a framework able to support, from both methodological and technological viewpoints, the use of Subject Ontologies in the context of a Semantic Web-based Educational System. The defined framework has high performances in terms of response and this may really improve the user experience.     

Advanced ontology management system for personalised e-Learning

The use of ontologies to model the knowledge of specific domains represents a key aspect for the integration of information coming from different sources, for supporting collaboration within virtual communities, for improving information retrieval, and more generally, it is important for reasoning on available knowledge. In the e-Learning field, ontologies can be used to model educational domains and to build, organize and update specific learning resources (i.e. learning objects, learner profiles, learning paths, etc.). One of the main problems of educational domains modeling is the lacking of expertise in the knowledge engineering field by the e-Learning actors. This paper presents an integrated approach to manage the life-cycle of ontologies, used to define personalised e-Learning experiences supporting blended learning activities, without any specific expertise in knowledge engineering.     

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.     

一种自动化的Web服务语义注释方法

分析了XML Schema和DAML文档,发掘二者在组成结构上的相似性,提出了一种联系WSDL文件和DAML本体描述文件的中间数据模型,通过将XML Schema格式的WSDL文件和DAML描述的本体文件映射到这种公共的数据模型上,使二者可以进行比较匹配,从而为自动化的语义注释提供支持。实验结果证明,该方法能为Web服务描述文件自动地添加语义信息。     

Learning domain ontologies for semantic Web service descriptions

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains.

Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.     

Comparing relationships in conceptual modeling: mapping to semantic classifications

Much of the research that deals with understanding the real world and representing it in a conceptual model uses some form of the entity-relationship model as a means of representation. This research proposes an ontology for classifying relationship verb phrases based upon the domain and context of the application within which the relationship appears. The classification categories to which the verb phrases are mapped were developed based upon prior research in databases, ontologies, and linguistics. The usefulness of the ontology for comparing relationships when used in conjunction with an entity ontology is discussed. Together, these ontologies can be effective in comparing two conceptual database designs for integration and validation. Empirical testing of the ontology on a number of relationships from different application domains and contexts illustrates the usefulness of the research.     

A knowledge acquisition methodology to ontology construction for information retrieval from medical documents

Abstract: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi-automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language-independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.     

The foundations of a theory-aware authoring tool for CSCL design

One of the most useful ways to enhance collaboration is to create scenarios where learners are able to interact more effectively. Nevertheless, the design of pedagogically sound and well-thought-out collaborative learning scenarios is a complex issue. This is due to the context of group learning where the synergy among learners’ interactions affects learning processes and, hence, the learning outcome. Although many advances have been made to support the designing of collaborative learning scenarios through technology, a more systematic approach is lacking. With the limitations of the current designing methods and tools, it is difficult to develop intelligent authoring systems that can guide users in order to produce more effective collaboration. One of the main difficulties with creating a more consistent (computer-understandable) approach to designing collaboration is the necessity of proposing better ways to formalize the group learning processes. In this paper, we present an innovative approach that uses ontologies and concepts from learning theories to create a framework that represents collaborative learning and its processes. Ontologies provide the necessary formalization to represent collaboration, while learning theories provide the concepts to justify and support the development of effective learning scenarios. Such an approach contributes to establish the foundations for the development of the next generation of intelligent authoring systems referred to as theory-aware systems. To verify the viability and usefulness of our proposed ontological framework in the context of systematic design, the development and use of an intelligent authoring tool for CSCL design is presented. This system is able to reason on ontologies to give suggestions that help users to create theory-compliant collaborative learning scenarios. We carried out several experiments with teachers in a geometry drawing course and the results indicate that the system helps teachers to create and interchange their scenarios more easily and facilitates the selection of important pedagogical strategies that influence positively the designing and effectiveness of group activities.     

上下文在人工智能中的应用

对上下文的基本概念进行了探讨。阐述了上下文在知识获取、机器学习、通信、数据库和本体中的应用。最后，对人工智能中的上下文所存在的一些问题进行讨论。     

Axiom-based ontology matching

Abstract: Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level.     

Integration of metacognitive skills in the design of learning objects

Recent studies have demonstrated that specific instruction about metacognitive strategies improves achievement, the accuracy of knowledge monitoring, and the application of learning strategies in hypermedia environments. However, there are no models to date for instructional designers who design and develop learning objects for the incorporation of specific scaffolds to aid student reflection about their metacognitive skills; thus making it difficult to identify tasks to orientate learners in improving such skills. In this paper, we propose the use of specific ontologies as the basis for incorporating information about metacognition in learning objects so that a Learning Management System can select and recommend tasks designed for the development and/or improvement of the learners’ metacognitive skills within the context of e-learning.     

Understanding as We Roam

Increasingly, the problem for roaming users isn't discovering local Web services or local context information or even getting their applications to intemperate with them at a syntactic level, but rather achieving interoperation at a semantic level. Developers are beginning to use semantic Web technologies such as ontologies to expose the semantics of context models and Web services. However, different ontologies arise due to natural human diversity in modeling domains. It's therefore important to develop mappings between ontologies. This article describes a practical ontology-mapping process specified in the Unified Modeling Language, as well as its deployment     

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.     

An overview of ontologies and data resources in medical domains

The ongoing rapid growth of diversity of data and their wide use to solve different complex tasks requires more sophisticated techniques of knowledge management and automated reasoning. Recent research efforts resulted in a significant number of semantic reference systems enriched with vocabularies, thesauri, terminologies, and ontologies. The extensive use of ontologies in the mainstream computer science has spread to many other branches of knowledge. These branches are included in a new approach to building modern intelligent systems, reusing and sharing pieces of declarative knowledge.In the meanwhile, a lot of effort has been made to produce standard ontologies for medicine and biology.This paper brings an overview and presentation of the state of the art in terminologies, ontologies and important resources/systems and tools for industry and academia in medicine and biology. It could be useful for researchers involved in multidisciplinary and interdisciplinary research areas and projects that include medicine, biology, and information technology.     

Incorporating ontology-based semantics into conceptual modelling

With the increasing complexity of applications and user needs, recent research has shifted from a data-information level to a human semantic level interaction. Research has begun to address the increasing use and development of ontologies in various applications, strongly motivated by the semantic web initiative. However, existing conceptual models are not rich enough to incorporate ontologies in one single conceptual schema. To improve this situation, it is necessary to refine modelling formalisms and make them more expressive while ensuring they remain semantically sound. We argue that conceptual modelling methodologies would be semantically richer if they were able to express the semantics of a domain that arises in concrete application scenarios. This paper investigates the incorporation of ontologies into three popular conceptual modelling methodologies, presenting the Ontological Entity-Relationship (OntoER) model, Ontological Object Role Modelling (OntoORM) and the Ontological Unified Modelling Language (OntoUML) class diagram. An extended conceptual framework for modelling ontologies and a transformation algorithm for mapping ontological constructs to relational schemata are provided so that querying the database through the conceptualisation of the database can be managed.     

Using OWL ontologies for adaptive patient information modelling and preoperative clinical decision support

We here present our research and experience regarding the design and implementation of a knowledge-based preoperative assessment decision support system. We discuss generic design considerations as well as the practical system implementation. We developed the system using semantic web technology, including modular ontologies developed in the OWL web ontology language, the OWL Java application programming interface and an automated logic reasoner. We discuss how the system enables to tailor patient information collection according to personalized medical context. The use of ontologies at the core of the system’s architecture permits to efficiently manage a vast repository of preoperative assessment domain knowledge, including classification of surgical procedures, classification of morbidities and guidelines for routine preoperative tests. Logical inference on the domain knowledge according to individual patient’s medical context enables personalized patients’ reports consisting of a risk assessment and clinical recommendations such as relevant preoperative tests.