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。     

多领域视角下的知识标注研究与实现

领域主题词表提供了某个领域或者行业某个视角下的关注的重点.利用领域主题词表进行标注能够揭示某个视角下文档的语义,而多个领域主题词表能够从多个视角联合揭示和挖掘文档的语义.文章通过将多个领域主题词表联合起来进行语义标注,提出了多领域视角下的知识标注方案.文章以皮肤病领域下文档标注为例进行了设计和实现.多领域视角下的知识标注为挖掘文档的知识提供了参考,并为进一步知识库的构建等奠定了基础.     

文献计量分析的我国语义网研究综述

采用文献计量分析方法,利用EXCEL、BICOMB、UCINET等工具软件,对CNKI中国学术期刊网络出版总库中收录的我国学术期刊在2001年1月至2015年9月期间发表的语义网研究论文进行统计分析,包括时序分析、基金资助分析、期刊来源分析、研究机构分析、关键词分析等,旨在了解我国语义网研究的最新现状,为进一步研究提供理论参考.论文还指出,Web服务、数据关联、RDF、数字图书馆、大数据、语义检索将会是现在及未来的研究热点.     

习语中的数字模糊语义及其翻译

习语中出现的数字往往并不表达十分准确的语义,数字语义的模糊性客观地存在于语言中,人们在日常交际中也经常会用到。文章在分析了数词语义的模糊性及其功能后,对数字模糊语义的翻译原则和方法做了初步的探索。     

基于Ontology的远程学习支持系统研究

随着网络技术的高速发展，WEB在学习中变得越来越重要，但是现有的WEB缺乏语义，致使WEB上的学习资源共享性和可移植性相对薄弱，然而Ontology能为各种知识系统之间的知识（资源）共享和互操作提供手段。因而本文提出了多代理结构的学习支持系统中的学习者模型Ontology及其代理对于学习者的有效学习带来的益处。     

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.     

Rare-aware attention network for image–text matching

Image and text matching bridges visual and textual modality differences and plays a considerable role in cross-modal retrieval. Much progress has been achieved through semantic representation and alignment. However, the distribution of multimedia data is severely unbalanced and contains many low-frequency occurrences, which are often ignored and cause performance degradation, i.e., the long-tail effect. In this work, we propose a novel rare-aware attention network (RAAN), which explores and exploits textual rare content for tackling the long-tail effect of image and text matching. Specifically, we first design a rare-aware mining module, which contains global prior information construction and rare fragment detector for modeling the characteristic of rare content. Then, the rare attention matching utilizes prior information as attention to guide the representation enhancement of rare content and introduces the rareness representation to strengthen the similarity calculation. Finally, we design prior information loss to optimize the model together with the triplet loss. We perform quantitative and qualitative experiments on two large-scale databases and achieve leading performance. In particular, we conduct 0-shot test for rare content and improve rSum by 21.0 and 41.5 on Flickr30K (155,000 image and text pairs) and MSCOCO (616,435 image and text pairs), demonstrating the effectiveness of the proposed method for the long-tail effect.