关联数据及其应用现状综述

自主、异类、异构和分布的海量数据间的语义关联一直是知识组织和知识发现的核心问题,关联数据(Linked Data)技术的出现为此提供一种轻型、渐增化、可伸缩和可扩展的动态机制。基于对大量文献的梳理和分析,描述Linked Data的原则与技术体系,介绍Linked Data发布、Link浏览器、Linked Data搜索引擎、Link更新维护等核心技术,简要介绍若干典型应用,提出在具体领域实施Linked Data应用的关键挑战。     

基于Vitro构建专业领域知识应用环境

以中国科学院国家科学图书馆开展专业领域知识应用环境建设为背景,通过对Vitro组成结构和功能特征的分析,提出基于Vitro的专业领域知识应用环境平台的设计方案,并介绍平台系统实现中对Vitro本地化应用开发的主要方法。     

DC元数据年度进展（2010）

文章介绍了都柏林元数据（DC）2010年年会基本情况,以及DC管理机构DCMI近一年的研究成果,其中主要是对关联数据理论与实践的研究,以及在关联数据和互操作环境下对DCMI推荐文档进行的相关审议和修订。文章也总结了DCMI过去一年工作与组织进展,探索了DCMI的发展方向。     

汉语并列短语标记隐现的认知研究

汉语中的并列短语的标记手段有两种：无标记和有标记,它们的使用并不是任意的,而是有理可据的。该文用认知语言学理论论证：标记的隐现主要相关于并列项之间语义的亲近性和其他一些制约条件。     

网络环境下的知识挖掘

当前网络信息大爆炸，要从大量信息中获得所需知识就需要运用知识挖掘方法。本文首先对数据、信息、知识三个概念进行了区分，阐述了网络知识挖掘的概念及类型，并从数据仓库、语义网络和XML等底层信息加工组织方法上对网络知识挖掘进行探讨。     

汉语取舍句的预设问题

在现代汉语中,有几类常见的取舍句式,它们在语义的表达上有较大差异,这些差异与取舍句的预设有直接的关系。详细分析了这几类取舍句式的语义预设和语用预设,从中得出:除与预设直接相关外,取舍句式在表达上的差异还与取舍者的主观态度有密切的关系。     

Semantic categorization of digital home photo using photographic region templates

In this paper, a semantic categorization method in generic home photos is proposed. In recent years, the semantic categorization of image has been a challenge due to the proliferation of digital home photos. Our approach is to detect semantically meaningful concepts contained in a photo. The proposed method incorporates an intermediate level of concepts, called local concept, so that it catches well semantic meaning of local regions of image as bridging the semantic gap of the low-level features and high-level category concepts. To detect the local concepts from the home photo, region segmentation by photographic region template and concept merging is also proposed. The efficacy of the proposed semantic categorization method was demonstrated with 3828 general home photos. The experiment results showed the proposed categorization method would be useful to detect multiple semantic meaning of the home photos.     

Self-derivation through memory integration: A model for accumulation of semantic knowledge

Semantic knowledge accumulates through explicit means and productive processes (e.g., analogy). These means work in concert when information explicitly acquired in separate episodes is integrated, and the integrated representation is used to self-derive new knowledge. We tested whether (a) self-derivation through memory integration extends beyond general information to science content, (b) self-derived information is retained, and (c) details of explicit learning episodes are retained. Testing was in second-grade classrooms (children 7–9 years). Children self-derived new knowledge; performance did not differ for general knowledge (Experiment 1) and science curriculum facts (Experiment 2). In Experiment 1, children retained self-derived knowledge over one week. In Experiment 2, children remembered details of the learning episodes that gave rise to self-derived knowledge; performance suggests that memory integration is dependent on explicit prompts. The findings support nomination of self-derivation through memory integration as a model for accumulation of semantic knowledge and inform the processes involved.     

Unifying knowledge iterative dissemination and relational reconstruction network for image–text matching

Image–text matching is a crucial branch in multimedia retrieval which relies on learning inter-modal correspondences. Most existing methods focus on global or local correspondence and fail to explore fine-grained global–local alignment. Moreover, the issue of how to infer more accurate similarity scores remains unresolved. In this study, we propose a novel unifying knowledge iterative dissemination and relational reconstruction (KIDRR) network for image–text matching. Particularly, the knowledge graph iterative dissemination module is designed to iteratively broadcast global semantic knowledge, enabling relevant nodes to be associated, resulting in fine-grained intra-modal correlations and features. Hence, vector-based similarity representations are learned from multiple perspectives to model multi-level alignments comprehensively. The relation graph reconstruction module is further developed to enhance cross-modal correspondences by constructing similarity relation graphs and adaptively reconstructing them. We conducted experiments on the datasets Flickr30K and MSCOCO, which have 31,783 and 123,287 images, respectively. Experiments show that KIDRR achieves improvements of nearly 2.2% and 1.6% relative to Recall@1 on Flicr30K and MSCOCO, respectively, compared to the current state-of-the-art baselines.     

基于本体论的语义导航模型研究

本文通过对信息导航的现状分析，提出基于本体论的语义导航思想，研究了基于本体论的信息组织，构建了基于本体论的语义导航模型，并结合实例探讨了语义导航的实现原理。