Salient object detection using biogeography-based optimization to combine features

In recent studies, ontology related concepts have been introduced into FIPA ACL content language to convey information for agent communication. However, these works have only applied ontology-based knowledge representation in communication message and then demonstrated the advantage of this association. In fact, although ontology can represent semantic implications needed for decidable reasoning support, it has no mechanism for defining complex rule-based representation to support inference. The motivation of this study is to address this issue by developing a semantic-based infrastructure to integrate Semantic Web technologies into ACL message contents. This semantic-based infrastructure defines two different semantic frameworks: the three-tier knowledge representation framework for message content and the Multi-layer Ontology Architecture for content language. The former is developed based on Semantic Web stack to support ontology-based reasoning and rule-based inference. The latter is adopted to develop a Lightweight Ontology-based Content Language (LOCL) to describe agent communication messages in an unambiguous and computer-interpretable way Jena reasoner is used in an application scenario that exploits agent communication with LOCL as content language, OWL as ontology language, and SWRL as rule language to demonstrate the feasibility of the proposed infrastructure.     

OWL-QL—a language for deductive query answering on the Semantic Web

This paper discusses the issues involved in designing a query language for the Semantic Web and presents the OWL query language (OWL-QL) as a candidate standard language and protocol for query–answering dialogues among Semantic Web computational agents using knowledge represented in the W3Cs ontology web language (OWL). OWL-QL is a formal language and precisely specifies the semantic relationships among a query, a query answer, and the knowledge base(s) used to produce the answer. Unlike standard database and Web query languages, OWL-QL supports query–answering dialogues in which the answering agent may use automated reasoning methods to derive answers to queries, as well as dialogues in which the knowledge to be used in answering a query may be in multiple knowledge bases on the Semantic Web, and/or where those knowledge bases are not specified by the querying agent. In this setting, the set of answers to a query may be of unpredictable size and may require an unpredictable amount of time to compute.     

基于本体的视频语义检索系统研究

在语义层次上检索视频内容,可以突破“语义鸿沟”,提高视频内容的利用效率。利用本体的标注和推理能力,研究视频语义检索,充分挖掘视频内容的结构和语义信息,构建层次化的语义索引,能极大提高系统的语义检索能力。该视频语义检索系统(OVSR)的本体架构集成了领域本体、视频本体和核心本体,具有很强的扩展和交互能力。主要论述OVSR的本体架构,视频语义模型和索引模型,研究OVSR的查询重写算法以及本体推理算法。     

基于语义的成语检索系统研究

现有成语检索系统多采用关键词匹配的检索模式。为了让用户能在仅知道要表达的意思的情况下能够检索到所需成语,提出基于语义的成语检索系统。研究了成语所蕴含的概念和其间的语义关系,构建出成语领域本体,并建造相应的检索系统。该系统首先对用户的查询请求进行语法分析和语义分析,然后对成语领域本体采用基于描述逻辑的推理机进行推理,从而检索出满足用户要求的成语集。     

Protus 2.0: Ontology-based semantic recommendation in programming tutoring system

With the development of the Semantic web the use of ontologies as a formalism to describe knowledge and information in a way that can be shared on the web is becoming common. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration and construction of knowledge rich and intensive systems. Semantic web provides huge potential and opportunities for developing the next generation of e-learning systems. In previous work, we presented tutoring system named Protus (PRogramming TUtoring System) that is used for learning the essence of Java programming language. It uses principles of learning style identification and content recommendation for course personalization. This paper presents new approach to perform effective personalization highly based on Semantic web technologies performed in new version of the system, named Protus 2.0. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. Functionality, structure and implementation of a Protus 2.0 ontology as well as syntax of SWRL rules implemented for on-the-fly personalization will be presented in this paper.     

一个自然语言理解系统的设计和实现

在自然语言理解系统的建立过程中,首要的工作是建立单词词典、语义词典,并建立句型规则和词义信息,利用这些词典和规则进行语义分析和词义的辨识。结合许多实例,详细探讨了解决这些问题的各种方法,它们的有机配合可以有效解决本NKZL自然语言理解系统的语义分析和词义辨识问题,这些方法已经在本系统中得支了运用和验证。     

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。     

基于语义Web的受控自然语言系统推理模型

虽然相关研究组织提供了语义Web的一些简化工具,但是对不具备相关背景知识的领域专家来说,语义Web的可用性较低。提出了基于语义Web的受控自然语言系统推理模型,以解决这个问题。首先给出受控自然语言系统推理模型框架;然后分析受控自然语言的语言处理部分,提出基于WordNct的受控自然语言系统的本体词库模型和基于本体词库的受控自然语言解释器,把受控自然语言转换成中间表达语言篇章表述结构;最后通过推理部分把篇章表述结构转换成语义Web的本体和规则,通过模板工具映射成Jess的事实和规则,根据预定义的语义Web的公理和定理对受控自然语言进行推理。试验证明此模型大大提高了知识表示建模的效率,也基本满足简单推理任务,具有实用价值。     

融合多层次特征的中文语义角色标注

随着人工智能和中文信息处理技术的迅猛发展,自然语言处理相关研究已逐步深入到语义理解层次上,而中文语义角色标注则是语义理解领域的核心技术。在统计机器学习仍占主流的中文信息处理领域,传统的标注方法对句子的句法及语义的解析程度依赖较大,因而标注准确率受限较大,已无法满足当前需求。针对上述问题,对基于Bi-LSTM的中文语义角色标注基础模型进行了改进研究,在模型后处理阶段结合了Max pooling技术,训练时融入了词法和句式等多层次的语言学特征,以实现对原有标注模型的深入改进。通过多组实验论证,结合语言学辅助分析,提出针对性的改进方法从而使模型标注准确率得到了显著提升,证明了结合Max pooling技术的Bi-LSTM语义角色标注模型中融入相关语言学特征能够改进模型标注效果。     

Introducing Syntax Information in a Stochastically-Based Semantic Case Grammar Parser

We study the impact of introducing syntax information into a stochastic component for natural language understanding that is based on a purely semantic case grammar formalism. The parser operates in an application for train travel information retrieval, the French ARISE (Automatic Railway Information Systems for Europe) task. This application supports the development of schedule inquiry services by telephone. The semantic case grammar has been chosen in order to enhance robustness facing spontaneous speech effects. However, this robustness is likely to turn into a drawback, if the semantic analysis ignores information that is propagated by syntactic relations. Introducing additional syntax information, whose complexity is well adapted to the size of the stochastic model, may disambiguate and therefore improve the decoding.     

基于贝叶斯网络的概念间语义相似度计算

概念间语义相似度研究是知识表示和信息检索领域中的一个重要内容,也是自然语言处理研究的重要组成部分,是人工智能领域中一个亟待解决的问题。本文在本体的基础上,对传统的相似度计算模型进行改进,提出一种基于贝叶斯网络的概率推理方法,改进概念间语义距离的计算,从而提高了语义相似度计算模型的效果;同时采用D-分离的方法,解决了在推理过中的计算复杂性。     

中文词义关系的定义与判定原则

在英语及其它的欧洲语言里,词汇语意关系已有相当充分的研究。例如,欧语词网( EuroWordNet ,Vossen 1998) 就是一个以语意关系来勾勒词汇词义的数据库。也就是说,词汇意义的掌握是透与其它词汇语意的关连来获致的。为了确保数据库建立的品质与一致性,欧语词网计画就每一个处理的语言其词汇间的词义关系是否成立提出相应的语言测试。实际经验显示,利用这些语言测试,人们可以更容易且更一致地辨识是否一对词义之间确实具有某种词义关系。而且,每一个使用数据库的人也可以据以检验其中关系连结的正确性。换句话说,对一个可检验且独立于语言的词汇语意学理论而言,这些测试提供了一个基石。本文中,我们探究为中文词义关系建立中文语言测试的可能性。尝试为一些重要的语意关系提供测试的句式和规则来评估其可行性。这项研究除了建构中文词汇语意学的理论基础,也对Miller的词汇网络架构(WordNet ,Fellbaum 1998) 提供了一个有力的支持,这个架构在词汇表征和语言本体架构研究上开拓了关系为本的进路。     

基于自然语言的语义推理接口

分析人工智能聊天机器人ALICE的知识组织结构和内部推理机制,指出ALICE系统在语义推理方面的缺陷,提出使用语义网本体理论与ALICE推理机制相结合的解决方案,设计并实现了一个基于ALICE系统的语义推理接口。实验证明,该接口完全支持基于自然语言的语义推理,且具有较高的准确率。     

Semantic Web search based on ontological conjunctive queries

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontology-based Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.     

语义web中以描述逻辑为本体语言的推理

语义Web是worldwideweb的发展方向,它的实现能有效地提高互联网的智能化程度,进而提高其使用效率。描述逻辑作为语义Web的一个逻辑基础,成为其研究和开发的重要内容。该文讨论了语义Web中以描述逻辑为本体语言的推理方法,并对其应用作了简要的分析。     

Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.     

基于本体的UML类图语义推理

统一建模语言(UML)是一个半形式化的语言,其语义部分是采用自然语言描述的,使得它在建模过程中会产生语义不一致等问题。在详细比较UML类图与本体的基础上,提出了一种UML类图的形式化方法;首先将UML类图转换为相应的本体;然后根据本体提供的推理算法(Tableau)对转换得到的本体进行推理,检测其中的不一致性从而修改UML类图,最后达到精确UML类图。     

中文网页语义标注:由句子到RDF表示

语义网远景的实现需要自动化的语义标注方法,提出了一种在领域本体指导下,针对中文网页的语义标注方法,运用统计学方法与自然语言处理技术,以文档中句子为处理对象,采取识别和组合两个阶段来完成句子向RDF表示的映射,它具有以下特点:以统计方法获得领域相关词汇,构造领域词汇标注列表作为外部领域知识,降低对通用语言本体的依赖;显式的属性类型标注方法识别出句子中表达关系的词汇,标注为属性类型,利于后续关系抽取;构造句子的句法依存关系树(森林),按照依存关系对词汇进行组合,形成RDF陈述.实验结果显示此方法较基于主谓宾语法关系的语义标注方法更为有效.