Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks

ABSTRACT

Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot handle uncertain knowledge about an application field, which is unsuitable for the medical domain. In this paper, we focus on modeling incomplete knowledge in the classical OWL ontologies, using Bayesian networks, all keeping the semantic of the first ontology, and applying algorithms dedicated to learn parameters of Bayesian networks in order to generate the Bayesian networks. We use EM algorithm for learning conditional probability tables of different nodes of Bayesian network automatically, contrary to different tools of Bayesian networks where probabilities are inserted manually. To validate our work, we have applied our model on the diagnosis of liver cancer using classical ontology containing incomplete instances, in order to handle medical uncertain knowledge, for predicting a liver cancer.     

Implementing situation-aware and user-adaptive music recommendation service in semantic web and real-time multimedia computing environment

With the advent of the ubiquitous era, many studies have been devoted to various situation-aware services in the semantic web environment. One of the most challenging studies involves implementing a situation-aware personalized music recommendation service which considers the user’s situation and preferences. Situation-aware music recommendation requires multidisciplinary efforts including low-level feature extraction and analysis, music mood classification and human emotion prediction. In this paper, we propose a new scheme for a situation-aware/user-adaptive music recommendation service in the semantic web environment. To do this, we first discuss utilizing knowledge for analyzing and retrieving music contents semantically, and a user adaptive music recommendation scheme based on semantic web technologies that facilitates the development of domain knowledge and a rule set. Based on this discussion, we describe our Context-based Music Recommendation (COMUS) ontology for modeling the user’s musical preferences and contexts, and supporting reasoning about the user’s desired emotions and preferences. Basically, COMUS defines an upper music ontology that captures concepts on the general properties of music such as titles, artists and genres. In addition, it provides functionality for adding domain-specific ontologies, such as music features, moods and situations, in a hierarchical manner, for extensibility. Using this context ontology, we believe that logical reasoning rules can be inferred based on high-level (implicit) knowledge such as situations from low-level (explicit) knowledge. As an innovation, our ontology can express detailed and complicated relations among music clips, moods and situations, which enables users to find appropriate music. We present some of the experiments we performed as a case-study for music recommendation.     

面向Web信息资源的领域本体模型自动构建机制的研究

领域本体的构建是本体工程研究与应用的重要内容。面向网络Web信息资源,获取领域相关文本信息,通过对文本的概念分析,构建领域本体模型。提出一套本体自动构建机制,该本体构建基于数据挖掘和机器学习技术,内容主要包括基于贝叶斯(Bayes)分类原理;提出多个分类器方式的概念分类过程和算法;提出概念关联分析和概念自学习算法,建立本体原型;提出面向OWL本体模型的转换映射机制,构建基于OWL的本体模型。此外,还提出了从网络资源获取、领域本体建模到本体实施应用的一套完整的本体构建和应用实施的解决方案。     

A Methodology to Create Legal Ontologies in a Logic Programming Based Web Information Retrieval System

Web legal information retrieval systems need the capability to reason with the knowledge modeled by legal ontologies. Using this knowledge it is possible to represent and to make inferences about the semantic content of legal documents. In this paper a methodology for applying NLP techniques to automatically create a legal ontology is proposed. The ontology is defined in the OWL semantic web language and it is used in a logic programming framework, EVOLP+ISCO, to allow users to query the semantic content of the documents. ISCO allows an easy and efficient integration of declarative, object-oriented and constraint-based programming techniques with the capability to create connections with external databases. EVOLP is a dynamic logic programming framework allowing the definition of rules for actions and events. An application of the proposed methodology to the legal web information retrieval system of the Portuguese Attorney General’s Office is described.     

Automatic extraction of OWL ontologies from UML class diagrams: a semantics-preserving approach

Full implementation of the Semantic Web requires widespread availability of OWL ontologies. Manual ontology development using current OWL editors remains a tedious and cumbersome task that requires significant understanding of the new ontology language and can easily result in a knowledge acquisition bottleneck. On the other hand, abundant domain knowledge has been specified by existing database schemata such as UML class diagrams. Thus developing an automatic tool for extracting OWL ontologies from existing UML class diagrams is helpful to Web ontology development. In this paper we propose an automatic, semantics-preserving approach for extracting OWL ontologies from existing UML class diagrams. This approach establishes a precise conceptual correspondence between UML and OWL through a semantics-preserving schema translation algorithm. The experiments with our implemented prototype tool, UML2OWL, show that the proposed approach is effective and a fully automatic ontology extraction is achievable. The proposed approach and tool will facilitate the development of Web ontologies and the realization of semantic interoperations between existing Web database applications and the Semantic Web.     

iConAwa - An intelligent context-aware system

Context-awareness becomes an increasingly important concept in the development of mobile and ubiquitous systems. Applications and services, which run in these kinds of highly dynamic environments, should be aware of and adapt to their contexts. Context-aware applications improve and enrich people’s interactions with devices, computers and other people.In this paper, design and development of iConAwa, which is an intelligent context-aware multi-agent system proactively providing mobile users with context-aware information and services, is described. In iConAwa, mobile users can get information and services about nearby resources (attraction points) according to their context and also communicate with each other by exchanging messages. Context and point of interest ontologies are developed in OWL. Context and points of interest are modelled in a flexible and extensible way by the developed ontology models. Knowledge sharing and knowledge reuse are also provided by using these ontology models. iConAwa makes use of rule-based context reasoning which provides derivation of high level implicit context from low level explicit context. With this approach context reasoning is decoupled from the source code of the system. JADE agent development framework is used to develop the agents and Jena semantic web framework is used to manipulate ontologies and for rule based reasoning.     

A parallel computing architecture for high-performance OWL reasoning

The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. We present a novel thread-level parallel architecture for ontology classification, which is ideally suited for shared-memory SMP servers, but does not rely on locking techniques and thus avoids possible race conditions. We evaluated our prototype implementation with a set of real-world ontologies. Our experiments demonstrate a very good scalability resulting in a speedup that is linear to the number of available cores.     

Enhancing electronic medical record retrieval through semantic query expansion

There are currently many active movements towards computerizing patient healthcare information. As Electronic Medical Record (EMR) systems are being increasingly adopted in healthcare facilities, however, there is a big challenge in effectively utilizing this massive information source. It is very time-consuming for healthcare providers to dig into the voluminous medical records of a patient to find the few that are indeed relevant to the patient’s current problem. Due to the complex semantic relationships among medical concepts and use of many synonyms, antonyms, and hypernym/hyponym, simple word-based information retrieval does not produce satisfactory results. In this paper, we propose an EMR retrieval system that leverages semantic query expansion to retrieve medical records that are relevant to the patient’s current symptom/problem. The proposed framework integrates various technologies, including information retrieval, domain ontologies, automatic semantic relationship learning, as well as a body of domain knowledge elicited from healthcare experts. Knowledge of semantic relationships among medical concepts, such as symptoms, exams and tests, diagnoses, and treatments, as well as knowledge of synonyms and hypernym/hyponyms, is used to expand and enhance initial queries posed by a user. We have implemented a preliminary prototype and conducted a pilot testing using sample nursing notes drawn from the EMR system of a community health center.     

Contextualizing ontologies

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party’s subjective view of a domain. In this paper, we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.     

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.     

An Ontology for G2G Collaboration in Public Policy Making,Implementation and Evaluation

This paper concerns the development and use of ontologies for electronically supporting and structuring the highest-level function of government: the design, implementation and evaluation of public policies for the big and complex problems that modern societies face. This critical government function usually necessitates extensive interaction and collaboration among many heterogeneous government organizations (G2G collaboration) with different backgrounds, mentalities, values, interests and expectations, so it can greatly benefit from the use of ontologies. In this direction initially an ontology of public policy making, implementation and evaluation is described, which has been developed as part of the project ICTE-PAN of the Information Society Technologies (IST) Programme of the European Commission, based on sound theoretical foundations mainly from the public policy analysis domain and contributions of experts from the public administrations of four European Union countries (Denmark, Germany, Greece and Italy). It is a ‘horizontal’ ontology that can be used for electronically supporting and structuring the whole lifecycle of a public policy in any vertical (thematic) area of government activity; it can also be combined with ‘vertical’ ontologies of the specific vertical (thematic) area of government activity we are dealing with. In this paper is also described the use of this ontology for electronically supporting and structuring the collaborative public policy making, implementation and evaluation through ‘structured electronic forums’, ‘extended workflows’, ‘public policy stages with specific sub-ontologies’, etc., and also for the semantic annotation, organization, indexing and integration of the contributions of the participants of these forums, which enable the development of advanced semantic web capabilities in this area.     

一种基于本体的团队知识共享模型

针对在Internet环境下进行项目开发的特点，提出一种团队知识共享模型，通过使用OWL表示的领域本体和应用本体来组织分散在Internet环境中的知识，并在此基础上定义了基本知识操作，使得Internet下的团队成员能够进行项目知识的查询、发布及订阅，方便了团队成员之间的知识共享。     

一种本体知识文摘方法的研究和实现

本体作为领域知识的表示方法，已经成为语义Web的基础。本体通常由领域专家建立，用于表示领域中概念以及概念与概念之间的关系。但这也使得普通用户难以理解本体中描述的信息。普通用户往往希望本体中的信息能够以自然语言的形式描述。这正是本文讨论的主要问题。本文采用分治策略，利用基于嵌套复杂模板的解决方案，设计并实现了本体知识文摘的算法。我们开发了一个原型系统SWARMS，并将该文摘算法进行了运用。初步的实验表明，本文提出的方法取得较好的结果。     

从ER模式到OWL DL本体的语义保持的翻译

提出了一种从ER模式到OWL DL本体的语义保持的翻译方法.该方法在形式化表示ER模式的基础上,建立ER模式和OWL DL本体之间精确的概念对应,通过一个翻译算法按照一组预定义的映射规则实现模式翻译.理论分析表明,该方法是语义保持的和有效的;算法实现和案例研究进一步证实,完全自动的机器翻译是可实现的.该文方法是原创性的,为Web本体的开发以及数据库和语义Web之间语义互操作的实现开辟了一条有效途径.     

Modelling and Reasoning in Metamodelling Enabled Ontologies

Ontologies are expected to play an important role in many application domains, as well as in software engineering in general. One problem with using ontologies within software engineering is that while UML, a widely used standard for specifying and constructing the models for a software-intensive system, has a four-layer metamodelling architecture, the standard Web Ontology Language (OWL) does not support reasoning over layered metamodels. OWL 2 provides simple metamodelling by using a punning approach, however, the interpretation function is different based on the context, which leads to non-intuitive results. The OWL FA Language has a well defined metamodelling architecture. However, there is no study and tool for supporting reasoning over OWL FA. In this paper, we discuss some reasoning tasks in OWL FA. We also introduce the OWL FA Tool kit, a simple tool kit for manipulating and reasoning with OWL FA.     

A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption

Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches, which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions — “What is C?” or “Which of the following is an example of C?” (where $C$ is a concept symbol) — or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. We propose two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the open-world assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. The proposed system is implemented, and experiments on specific ontologies have shown the effectiveness of the approaches. We also did an empirical study by generating question items from a real-world ontology and validated our results with the help of domain experts.     

基于语义分析的OWL文档聚类研究

借助目前丰富的网络资源，将同一主题的现存Ontology知识聚类，提供给领域专家或用户进行二次精化和集成是Ontology研究领域的一个重要课题．OWL是目前用于表示和交换Ontology信息的基本标准．本文从OWL的语义本质出发，考虑了知识之间的继承性及复杂类比较和模糊集运算的相似性，提出一种计算OWL文档语义相似性的方式，并和层次聚类算法集成完成了对OWL文档集的聚类实验．实验结果说明本文提出的算法对自动生成和手工建立的OWL文档集都有很好的效果。     

Ontology‐Based Representation and Modelling of Synthetic 3D Content: A State‐of‐the‐Art Review

An indispensable element of any practical 3D/VR/AR application is synthetic three‐dimensional (3D) content. Such content is characterized by a variety of features—geometry, structure, space, appearance, animation and behaviour—which makes the modelling of 3D content a much more complex, difficult and time‐consuming task than in the case of other types of content. One of the promising research directions aiming at simplification of modelling 3D content is the use of the semantic web approach. The formalism provided by semantic web techniques enables declarative knowledge‐based modelling of content based on ontologies. Such modelling can be conducted at different levels of abstraction, possibly domain‐specific, with inherent separation of concerns. The use of semantic web ontologies enables content representation independent of particular presentation platforms and facilitates indexing, searching and analysing content, thus contributing to increased content re‐usability. A range of approaches have been proposed to permit semantic representation and modelling of synthetic 3D content. These approaches differ in the methodologies and technologies used as well as their scope and application domains. This paper provides a review of the current state of the art in representation and modelling of 3D content based on semantic web ontologies, together with a classification, characterization and discussion of the particular approaches.     

Ontology driven semantic profiling and retrieval in medical information systems

We propose the application of a novel sub-ontology extraction methodology for achieving interoperability and improving the semantic validity of information retrieval in the medical information systems (MIS) domain. The system offers advanced profiling of a user’s field of specialization by exploiting the concept of sub-ontology extraction, i.e., each sub-ontology may subsequently represent a particular user profile. Semantic profiling of a user’s field of specialization or interest is necessary functionality in any medical domain information retrieval system; this is because the (structural and semantic) extent of information sources is massive and individual users are only likely to be interested in specific parts of the overall knowledge documents on the basis of their area of specialization. The prototypical system, OntoMOVE, has been specifically designed for application in the medical information systems domain. OntoMOVE utilizes semantic web standards like RDF(S) and OWL in addition to medical domain standards and vocabularies encompassed by the UMLS knowledge sources.