提出了基于主题聚类的Web资源个性化推荐算法PRWRTC(personalized recommendations of Web resource based on Topic clustering),该算法首先基于Web资源的主题提出Web资源聚类算法,从而提高Web资源聚类的精确度,进而提升Web资源推荐的...提出了基于主题聚类的Web资源个性化推荐算法PRWRTC(personalized recommendations of Web resource based on Topic clustering),该算法首先基于Web资源的主题提出Web资源聚类算法,从而提高Web资源聚类的精确度,进而提升Web资源推荐的准确度;然后,基于用户的浏览行为,提出实时获取用户偏好的算法;最后,针对用户偏好的动态演化,在算法中加入了时效的概念,实现了对Web资源的动态推荐.并通过实验验证了该算法的有效性.展开更多
由于网络地图服务(Web map service,WMS)元数据缺乏显式的领域主题描述机制,用户很难准确、全面地发现目标领域的地图数据资源。提出了一种面向地理信息资源检索的WMS领域主题文本提取及元数据扩展方法。首先,设计了一种非监督文本分类...由于网络地图服务(Web map service,WMS)元数据缺乏显式的领域主题描述机制,用户很难准确、全面地发现目标领域的地图数据资源。提出了一种面向地理信息资源检索的WMS领域主题文本提取及元数据扩展方法。首先,设计了一种非监督文本分类算法,利用地球与环境术语集语义网(semantic Web of Earth and environmental terminology,SWEET)和大型英语词汇语义网WordNet,综合计算WMS元数据能力文档中地学术语、通识型词汇与领域主题的语义相关度,为WMS及其图层提取多标签主题。然后,基于ISO191152003地理信息元数据标准,为WMS元数据组织模型扩展领域主题。实验结果表明,所提出的WMS元数据主题分类算法取得了较高的查准率和查全率,且相较于朴素贝叶斯、线性支持向量机(support vector machine,SVM)和逻辑回归等方法,整体上有较大的优势。该方法有望应用于当前的地理信息门户和目录服务,辅助用户快速、准确地定位目标领域的地图服务资源。展开更多
The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ qu...The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ queries. Most search engines provide short time retrieval to user queries;however, they provide a little guarantee of precision even to the highly detailed users’ queries. In such cases, documents clustering centered on the subject and contents might improve search results. This paper presents a novel method of document clustering, which uses semantic clique. First, we extracted the Features from the documents. Later, the associations between frequently co-occurring terms were defined, which were called as semantic cliques. Each connected component in the semantic clique represented a theme. The documents clustered based on the theme, for which we designed an aggregation algorithm. We evaluated the aggregation algorithm effectiveness using four kinds of datasets. The result showed that the semantic clique based document clustering algorithm performed significantly better than traditional clustering algorithms such as Principal Direction Divisive Partitioning (PDDP), k-means, Auto-Class, and Hierarchical Clustering (HAC). We found that the Semantic Clique Aggregation is a potential model to represent association rules in text and could be immensely useful for automatic document clustering.展开更多
Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a...Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.展开更多
文摘提出了基于主题聚类的Web资源个性化推荐算法PRWRTC(personalized recommendations of Web resource based on Topic clustering),该算法首先基于Web资源的主题提出Web资源聚类算法,从而提高Web资源聚类的精确度,进而提升Web资源推荐的准确度;然后,基于用户的浏览行为,提出实时获取用户偏好的算法;最后,针对用户偏好的动态演化,在算法中加入了时效的概念,实现了对Web资源的动态推荐.并通过实验验证了该算法的有效性.
文摘由于网络地图服务(Web map service,WMS)元数据缺乏显式的领域主题描述机制,用户很难准确、全面地发现目标领域的地图数据资源。提出了一种面向地理信息资源检索的WMS领域主题文本提取及元数据扩展方法。首先,设计了一种非监督文本分类算法,利用地球与环境术语集语义网(semantic Web of Earth and environmental terminology,SWEET)和大型英语词汇语义网WordNet,综合计算WMS元数据能力文档中地学术语、通识型词汇与领域主题的语义相关度,为WMS及其图层提取多标签主题。然后,基于ISO191152003地理信息元数据标准,为WMS元数据组织模型扩展领域主题。实验结果表明,所提出的WMS元数据主题分类算法取得了较高的查准率和查全率,且相较于朴素贝叶斯、线性支持向量机(support vector machine,SVM)和逻辑回归等方法,整体上有较大的优势。该方法有望应用于当前的地理信息门户和目录服务,辅助用户快速、准确地定位目标领域的地图服务资源。
文摘The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ queries. Most search engines provide short time retrieval to user queries;however, they provide a little guarantee of precision even to the highly detailed users’ queries. In such cases, documents clustering centered on the subject and contents might improve search results. This paper presents a novel method of document clustering, which uses semantic clique. First, we extracted the Features from the documents. Later, the associations between frequently co-occurring terms were defined, which were called as semantic cliques. Each connected component in the semantic clique represented a theme. The documents clustered based on the theme, for which we designed an aggregation algorithm. We evaluated the aggregation algorithm effectiveness using four kinds of datasets. The result showed that the semantic clique based document clustering algorithm performed significantly better than traditional clustering algorithms such as Principal Direction Divisive Partitioning (PDDP), k-means, Auto-Class, and Hierarchical Clustering (HAC). We found that the Semantic Clique Aggregation is a potential model to represent association rules in text and could be immensely useful for automatic document clustering.
基金supported by National Natural Science Foundation of China[No.41971349,No.41930107,No.42090010 and No.41501434]National Key Research and Development Program of China[No.2017YFB0503704 and No.2018YFC0809806].
文摘Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.
