The Program for Cooperative Cataloging’s Transformative Role as a Leader of Change     

Philip E. Schreur 《Cataloging & classification quarterly》2020,58(3-4):397-402

Abstract

The transformation of library metadata encoded in MARC to linked data will enable libraries to participate in the Semantic Web. This transformation, however, will be an iterative development dependent upon community-based decisions. The PCC, as a community-based organization, is ideally positioned to lead this transformation. As PCC guides this transition, three broad areas must be resolved: the conversion of legacy data to linked data, the use of identifiers to support controlled headings, and the transformation of current workflows to linked-data counterparts. By embracing the Web as a community, PCC can confirm its relevance in a complex web of global data.  相似文献   

Spatial Planning and its Need for National and Regional Bibliographies of Grey Literature     

Christian Erlinger 《Cataloging & classification quarterly》2019,57(1):59-72

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Web document summarization by exploiting social context with matrix co-factorization     

Minh-Tien Nguyen  Viet Cuong Tran  Xuan Hoai Nguyen  Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.  相似文献   

Neural embedding-based indices for semantic search     

Fatemeh Lashkari  Ebrahim Bagheri  Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.  相似文献   

专利被引频次的时间影响研究     

罗文馨  赵亚娟 《图书情报工作》2019,63(9):47-60

[目的/意义]研究时间因素对专利被引频次的影响,可以减少时间因素对技术评价活动的制约,提高评价的准确性和可信度。[方法/过程]采集1975-2017年的美国专利数据,开展基于固定效应法的专利被引频次的修正研究。将专利按照不同公开年份和不同技术领域分组,选定组内均值和6个TOP分位数为被引频次基准,统计当前时间点的被引频次基准线及基准线的历史时序变化情况。建立神经网络模型,拟合基准线的时序变化规律,并预测未来统计时间点的基准线。[结果/结论]专利公开年份和统计年份的时间差异,使得专利被引频次无法直接进行比较。本文建立了基于不同技术领域、不同公开年份和不同统计年份的专利被引频次基准线,为专利评估提供参考。  相似文献   

基于深度学习的中文专利自动分类方法研究   总被引：2，自引：0，他引：2  

吕璐成  韩涛  周健  赵亚娟 《图书情报工作》2020,64(10):75-85

[目的/意义] 面向当前国内专利审查和专利情报分析工作中对于海量专利分类的客观需求，设计了7种基于深度学习的专利自动分类方法，对比各种方法的分类效果，从而助力专利分类效率和效果的提升。[方法/过程] 针对传统机器学习方法存在的缺陷，基于Word2Vec、CNN、RNN、Attention机制等深度学习技术，考虑专利文本语序特征、上下文特征以及分类关键特征，设计Word2Vec+TextCNN、Word2Vec+GRU、Word2Vec+BiGRU、Word2Vec+BiGRU+TextCNN等7种深度学习模型，以中国专利为例，选取IPC主分类号的"部"作为分类依据，对比这7种模型与3种传统分类模型在中文专利分类任务中的效果。[结果/结论] 实证研究效果显示，采用考虑语序特征、上下文特征及强化关键特征的深度学习方法进行中文专利分类具有更优的分类效果。  相似文献   

基于专利引用跨越度计量的两国间间接创新合作机会发现     

李睿  青杨媚  范九江 《图书情报工作》2021,64(23):31-39

[目的/意义] 两个国家间的创新合作机会有些是显性直接的，有些是潜在间接的。在此试图构建一种计量和分析专利引用关系的方法，用于发现两国间潜在的间接创新合作机会。[方法/过程] 全球价值链上不同环节专利之间的引用关系中，蕴含着相互衔接、配套的间接合作关系，而不同环节的专利通常具有不同的功能，即IPC存在一定跨越度。因此，设计"专利引用跨越度"指标及算法，用于计量和筛选专利引用网络中"引用跨越度"达到预设阈值的专利引用关系，作为发现间接创新合作机会的基础数据。以新加坡在中国获得授权的发明专利为样本，基于专利引用跨越度计量并配合人工解读和识别，发现中新两国间一系列的间接创新合作机会。[结果/结论] 基于专利引用跨越度计量的两国间间接创新合作机会发现的方法，被实验检验为有效。  相似文献   

上海市技术比较优势及其演变分析——基于上海市职务发明专利申请的统计分析   总被引：1，自引：0，他引：1  

俞文华  张俊燕 《中国青年科技》2007,(11):6-16

上海市是我国发明专利申请最活跃和技术创新能力最强的城市之一,充分发挥其技术比较优势,不仅有利于其进一步提升技术创新能力,而且也有利于其代表我国在全球科技竞争中占据制高点。本文在对上海职务发明专利申请进行分技术领域统计的基础上,首先对上海职务发明专利申请态势、技术领域分布及其在全国中的地位进行了分析,其次根据其不同技术领域的显性技术比较优势指数,对其技术比较优势及演变进行了研究,最后对上述分析结果的公共政策含义进行了初步探讨。  相似文献   

转型阶段后发企业的双元技术追赶绩效研究——专利的视角     

郭磊  蔡虹  徐露莹 《科技管理研究》2016,(11)

当后发企业由追赶向领先转型时,核心技术能力构建成为主要议题.通过对华为公司的实证分析,从总体技术能力和核心技术能力2个维度考察后发企业转型期的技术追赶绩效.研究结果表明:在两大关键电信技术领域内,华为已经基本实现总体技术追赶,但在核心技术上发力较晚,尚未完全达到领先.另外,华为在新兴领域的技术追赶绩效要优于传统领域.  相似文献   

中国高技术企业国际化中的专利布局研究          下载免费PDF全文

裘江南  张野 《科研管理》2016,37(11):43-51

近年来,高新技术企业已成为我国最重要的新经济增长点,然而,其在国际化发展中却频遭专利诉讼。对此,本文采用探索性案例研究对中兴公司典型的专利诉讼案件进行分析,按照中兴公司的发展阶段,以移动通讯技术的变化纵向展开,深入挖掘诉讼双方的专利申请和布局规律,在可视化企业专利布局基础上,研究2G、3G、4G时期通讯行业不同的竞争态势,并对中兴采取的差异化专利布局策略进行了深入分析。最后,基于回避设计、围堵式、地毯式、围墙式四种专利布局策略及其适用性,为处于初创、成长、成熟期等不同发展阶段的中国高新技术企业提供国际化专利布局策略,旨在为我国企业如何应对外国公司技术垄断,成功国际化提供建议。  相似文献