共查询到18条相似文献，搜索用时 140 毫秒
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通过实验采集用户的图像标注结果,对3种图像语义标注模式——基于标签打分的图像标注模式、单标签下基于图像比较的标注模式以及多标签下基于图像比较的标注模式的标注效果进行对比研究。研究发现:1基于标签打分的图像标注模式和单标签下基于图像比较的标注模式能够帮助用户对图像各标签的语义强度进行有效标注;2多标签下基于图像比较的标注模式可以帮助用户对图像各标签语义强度的比例关系进行有效标注;3标注界面中是否同时显示图像的所有标签,可能影响到用户对图像在各标签上语义强度比例关系的判断。 相似文献
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协同标注系统的语义丰富 总被引：1，自引：0，他引：1
提出利用语义网技术语义丰富协同标注系统的方法,通过对协同标注系统的标签进行标准化处理,利用标签的共现分析出标签的意思组,并将其与相关本体的SWTS(概念、属性、实例)映射,从而丰富标签的语义,以改善协同标注系统的检索结果. 相似文献
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国外标签本体研究进展 总被引：1，自引：0，他引：1
为解决folksonomies的问题,提出给标签、标注行为增加语义的标签本体,并利用语义网本体建模标注行为和folksonomies。标签本体的发展从关注标注活动发展到关注folksonomy(协同标注活动),并从标签含义的角度,创建MOAT跨越标注行为与语义检索的鸿沟。标签本体正走向统一、共享的新阶段。 相似文献
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【目的/意义】在线健康信息的有效组织对提升全民身体素质具有重要的社会价值。【方法/过程】在分析健
康信息主题、关联关系和资源标引的基础上,构建基于主题图的在线健康信息标签语义挖掘模型,从而构建了健康
信息标签主题图并实现了其可视化导航、浏览和检索等功能。【结果/结论】基于主题图的在线健康信息标签语义挖
掘模型能够准确的发现在线健康信息与信息标签间的深层关系,可以更好地揭示在线健康信息标签的语义关联,
为用户提供信息的可视化浏览和导航功能、提升健康信息的组织效果,帮助用户健康信息获取。【创新/局限】本文
将主题图与健康信息标签相结合,提高了健康信息的检索效率和利用效率,但本文也存在着不足,例如标签样本量
和样本范围较小,缺乏专业医学研究者的参与。 相似文献
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Social tagging systems enable users to assign arbitrary tags to various digital resources. However, they face vague-meaning problems when users retrieve or present resources with the keyword-based tags. In order to solve these problems, this study takes advantage of Semantic Web technology and the topological characteristics of knowledge maps to develop a system that comprises a semantic tagging mechanism and triple-pattern and visual searching mechanisms. A field experiment was conducted to evaluate the effectiveness and user acceptance of these mechanisms in a knowledge sharing context. The results show that the semantic social tagging system is more effective than a keyword-based system. The visualized knowledge map helps users capture an overview of the knowledge domain, reduce cognitive effort for the search, and obtain more enjoyment. Traditional keyword tagging with a keyword search still has the advantage of ease of use and the users had higher intention to use it. This study also proposes directions for future development of semantic social tagging systems. 相似文献
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[目的/意义] 构建基于用户兴趣标签的网络社团识别模型（Fuzzy Interests and User Hybrid Model,FIUHM）,揭示用户兴趣与社团形式概念间的模糊层级关系,实现多粒度属性与社团拓扑结构的层次聚类。[方法/过程] 通过抽取豆瓣电影社区数据,实现基于用户标签的兴趣强度语义标注,利用用户相似度,获取社区用户间兴趣语义距离;将网络社区的领接矩阵映射为社团形式背景,构建社团模糊概念格,建立社团形式概念及其偏序关系集,完成社团形式概念建模;通过计算社团稳定指数,识别网络社团边界,并聚类最大独立社团,实现兴趣社团的在线检测。[结果/结论] 通过对比实验,验证了FIUHM模型的有效性,实验表明将模糊形式概念分析引入网络社团识别研究,利用模糊概念格的偏序关系建模用户节点间的兴趣相似度,有利于提高社团识别的分辨率。 相似文献
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非物质文化遗产数据库对非遗资源的分类,必须遵从《非物质文化遗产数字化保护专业标准》,但这种分类方式存在一些问题。为了充分揭示非遗资源的特性和非遗资源之间的文化关联性,以建设承德地区非遗数据库为例,引入大众标注标引资源的方式来补充专家分类的不足：用户协作建设基础标签库,使用基础标签作为推荐标签,使用时间、空间、文化场所标签作为文化空间标签,由推荐标签等形成的高频标签作为专家分类的二级类目的备选,构建基于用户协作的非遗数字资源混合分类模式。 相似文献
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Chunlin Li Jingpan Bai Zhao Wenjun Yang Xihao 《Information processing & management》2019,56(1):91-109
Recently, social network has been paid more and more attention by people. Inaccurate community detection in social network can provide better product designs, accurate information recommendation and public services. Thus, the community detection (CD) algorithm based on network topology and user interests is proposed in this paper. This paper mainly includes two parts. In first part, the focused crawler algorithm is used to acquire the personal tags from the tags posted by other users. Then, the tags are selected from the tag set based on the TFIDF weighting scheme, the semantic extension of tags and the user semantic model. In addition, the tag vector of user interests is derived with the respective tag weight calculated by the improved PageRank algorithm. In second part, for detecting communities, an initial social network, which consists of the direct and unweighted edges and the vertexes with interest vectors, is constructed by considering the following/follower relationship. Furthermore, initial social network is converted into a new social network including the undirected and weighted edges. Then, the weights are calculated by the direction and the interest vectors in the initial social network and the similarity between edges is calculated by the edge weights. The communities are detected by the hierarchical clustering algorithm based on the edge-weighted similarity. Finally, the number of detected communities is detected by the partition density. Also, the extensively experimental study shows that the performance of the proposed user interest detection (PUID) algorithm is better than that of CF algorithm and TFIDF algorithm with respect to F-measure, Precision and Recall. Moreover, Precision of the proposed community detection (PCD) algorithm is improved, on average, up to 8.21% comparing with that of Newman algorithm and up to 41.17% comparing with that of CPM algorithm. 相似文献
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The number of patent documents is currently rising rapidly worldwide, creating the need for an automatic categorization system to replace time-consuming and labor-intensive manual categorization. Because accurate patent classification is crucial to search for relevant existing patents in a certain field, patent categorization is a very important and useful field. As patent documents are structural documents with their own characteristics distinguished from general documents, these unique traits should be considered in the patent categorization process. In this paper, we categorize Japanese patent documents automatically, focusing on their characteristics: patents are structured by claims, purposes, effects, embodiments of the invention, and so on. We propose a patent document categorization method that uses the k-NN (k-Nearest Neighbour) approach. In order to retrieve similar documents from a training document set, some specific components to denote the so-called semantic elements, such as claim, purpose, and application field, are compared instead of the whole texts. Because those specific components are identified by various user-defined tags, first all of the components are clustered into several semantic elements. Such semantically clustered structural components are the basic features of patent categorization. We can achieve a 74% improvement of categorization performance over a baseline system that does not use the structural information of the patent. 相似文献
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《Information processing & management》2020,57(3):102183
Semantic representation reflects the meaning of the text as it may be understood by humans. Thus, it contributes to facilitating various automated language processing applications. Although semantic representation is very useful for several applications, a few models were proposed for the Arabic language. In that context, this paper proposes a graph-based semantic representation model for Arabic text. The proposed model aims to extract the semantic relations between Arabic words. Several tools and concepts have been employed such as dependency relations, part-of-speech tags, name entities, patterns, and Arabic language predefined linguistic rules. The core idea of the proposed model is to represent the meaning of Arabic sentences as a rooted acyclic graph. Textual entailment recognition challenge is considered in order to evaluate the ability of the proposed model to enhance other Arabic NLP applications. The experiments have been conducted using a benchmark Arabic textual entailment dataset, namely, ArbTED. The results proved that the proposed graph-based model is able to enhance the performance of the textual entailment recognition task in comparison to other baseline models. On average, the proposed model achieved 8.6%, 30.2%, 5.3% and 16.2% improvement in terms of accuracy, recall, precision, and F-score results, respectively. 相似文献
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A growing number of tagging applications have begun to provide users the ability to socialise their own keywords. Tagging, which assigns a set of keywords to resources, has become a powerful way for organising, browsing, and publicly sharing personal collections of resources on the Web. It is called folksonomies. These systems on current social websites, however, have deficiencies in defining tag's meaning, and are often blocked to users in order to reuse, share, and exchange the tags across heterogeneous websites. In this paper, we describe a semantic model for expressing folksonomies in social websites. This model, called Social Semantic Cloud of Tags, aims to provide a consistent format of representing folksonomies and some features in terms of tagging activities. We describe core concepts and relevant properties such as a popularity and usage of tags, along with deduced relationships between tags. We will discuss how this model helps to reduce drawbacks regarding tag sharing between users, applications, or folksonomies. 相似文献