Relationship between index term specificity and relevance judgment

Concurrent concepts of specificity are discussed and differentiated from each other to investigate the relationship between index term specificity and users’ relevance judgments. The identified concepts are term-document specificity, hierarchical specificity, statement specificity, and posting specificity. Among them, term-document specificity, which is a relationship between an index term and the document indexed with the term, is regarded as a fruitful research area. In an experiment involving three searches with 175 retrieved documents from 356 matched index terms, the impact of specificity on relevance judgments is analyzed and found to be statistically significant. Implications for index practice and for future research are discussed.     

Automatic,semantics-based indexing of natural language texts for information retrieval systems

The fundamental idea of the work reported here is to extract index phrases from texts with the help of a single word concept dictionary and a thesaurus containing relations among concepts. The work is based on the fact, that, within every phrase, the single words the phrase is composed of are related in a certain well denned manner, the type of relations holding between concepts depending only on the concepts themselves. Therefore relations can be stored in a semantic network. The algorithm described extracts single word concepts from texts and combines them to phrases using the semantic relations between these concepts, which are stored in the network. The results obtained show that phrase extraction from texts by this semantic method is possible and offers many advantages over other (purely syntactic or statistic) methods concerning preciseness and completeness of the meaning representation of the text. But the results show, too, that some syntactic and morphologic “filtering” should be included for effectivity reasons.     

The structure of relationally indexed titles and abstracts

Farradane's system of relational indexing has been used to index abstracts in several, mainly scientific subject areas, and provides two-dimensional displays of concepts and explicit relations. Ten of these collections have been examined to compare (a) the use of relations, (b) the use of concept types, (c) the cross sections (or shape) of abstracts, and (d) the properties of “nodes”. Whilst differences between the collections were noted in several instances, there were no obvious correlations between these differences. However, some points arose which could warrant more detailed study with larger collections.     

Query reformulation using automatically generated query concepts from a document space

We propose a new query reformulation approach, using a set of query concepts that are introduced to precisely denote the user’s information need. Since a document collection is considered to be a domain which includes latent primitive concepts, we identify those concepts through a local pattern discovery and a global modeling using data mining techniques. For a new query, we select its most associated primitive concepts and choose the most probable interpretations as query concepts. We discuss the issue of constructing the primitive concepts from either the whole corpus or from the retrieved set of documents. Our experiments are performed on the TREC8 collection. The experimental evaluation shows that our approach is as good as current query reformulation approaches, while being particularly effective for poorly performing queries. Moreover, we find that the approach using the primitive concepts generated from the set of retrieved documents leads to the most effective performance.     

科技发展前沿信息监测与分析平台的构建

设计并实现了动态监测与追踪、反应快速、分析深入、功能集成、可视化展现的科技发展前沿信息监测与分析平台,综合运用数据库技术,网络信息抓取技术,本体技术和文本聚类技术,实现了准确高效的信息获取、不同科技领域概念的组织及其相互关系的揭示、科技主题关联关系及其变化趋势的挖掘等功能,旨在为国家和相关部门的战略决策者提供对科技发展相关知识结构、发展趋势等方面的分析,并以多种可视化方式总结与呈现的高效的战略决策支持服务;为相关情报研究业务提供有效的研究方法和分析工具,提高情报研究能力和效率。     

Neural embedding-based indices for semantic search

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.     

科技服务业助推中国经济高质量发展的影响因素和优化路径研究 ——基于GRA模型与CRITIC权重法的综合分析

以五大发展理念为基本维度构建经济高质量发展指标体系,并从投入、环境、产出三个维度构建了科技服务业指标体系,采用2012—2019年的相关数据,运用GRA模型分析科技服务业助推经济高质量发展的影响因素,并基于CRITIC权重法构建科技服务业助推经济高质量发展的助推效应指数,探究科技服务业三个维度影响因素的助推效应及其变化趋势。研究结果表明,科技服务业各指标与经济高质量发展相关指标的关联性较强,科技服务业对经济高质量发展具有较好的助推作用;科技服务业对我国经济高质量发展的助推效应不断增强,同时综合助推效应指数的变化趋势与投入助推效应指数高度吻合,且投入助推效应指数是促进综合助推效应指数正向增长的核心指标,环境助推效应指数和产出助推效应指数是综合助推效应指数保持稳定变化的基础性指标。结合研究结论提出了进一步增强科技服务业助推我国经济高质量发展的能力和效应的优化路径。     

Document indexing: a concept-based approach to term weight estimation

Traditional index weighting approaches for information retrieval from texts depend on the term frequency based analysis of the text contents. A shortcoming of these indexing schemes, which consider only the occurrences of the terms in a document, is that they have some limitations in extracting semantically exact indexes that represent the semantic content of a document. To address this issue, we developed a new indexing formalism that considers not only the terms in a document, but also the concepts. In this approach, concept clusters are defined and a concept vector space model is proposed to represent the semantic importance degrees of lexical items and concepts within a document. Through an experiment on the TREC collection of Wall Street Journal documents, we show that the proposed method outperforms an indexing method based on term frequency (TF), especially in regard to the few highest-ranked documents. Moreover, the index term dimension was 80% lower for the proposed method than for the TF-based method, which is expected to significantly reduce the document search time in a real environment.     

CIDER: Concept-based image diversification,exploration, and retrieval

Many of the approaches to image retrieval on the Web have their basis in text retrieval. However, when searchers are asked to describe their image needs, the resulting query is often short and potentially ambiguous. The solution we propose is to perform automatic query expansion using Wikipedia as the source knowledge base, resulting in a diversification of the search results. The outcome is a broad range of images that represent the various possible interpretations of the query. In order to assist the searcher in finding images that match their specific intentions for the query, we have developed an image organization method that uses both the conceptual information associated with each image, and the visual features extracted from the images. This, coupled with a hierarchical organization of the concepts, provides an interactive interface that takes advantage of the searchers’ abilities to recognize relevant concepts, filter and focus the search results based on these concepts, and visually identify relevant images while navigating within the image space. In this paper, we outline the key features of our image retrieval system (CIDER), and present the results of a preliminary user evaluation. The results of this study illustrate the potential benefits that CIDER can provide for searchers conducting image retrieval tasks.     

《数据结构》课程中抽象概念与程序语句间的桥梁

在课程教学中,我们经常遇到算法及其程序实现的讲解,一些抽象概念在程序中体现为具体的程序语句,为了将这些程序语句和抽象概念联系起来,通常需要给程序加大量的注解。一种将程序语句与抽象概念联系起来的做法是在程序代码中使用宏,宏的名称以抽象概念命名,这样可以简化对程序的理解,将注意力集中在算法的逻辑层次上。论文以数据结构课程中的二叉树中序遍历算法和堆排序算法为实例,探讨在在程序中使用宏,以帮助建立抽象概念与程序语句的桥梁,达到让学生更容易理解程序的目的。     

基于经典扎根的大型工程资源协同测评指标体系构建

选取我国目前已建成投资额最大的大型工程——广乐高速公路作为研究个案,研究大型工程资源协同测评指标体系。采用经典扎根的研究范式,对资料进行实质性编码和理论性编码。在达到理论饱和的前提下提取出69个标签、30个概念、12个范畴以及3个主范畴。在此基础上构建大型工程资源协同测评的三维概念模型并建立大型工程资源协同测评的指标体系。采用问卷调查及专家打分法对大型工程资源协同测评指标进行信度分析,并采用相关矩阵赋权法计算指标的权重,为大型工程建设中资源协同测评作参考。     

A systematic study of knowledge graph analysis for cross-language plagiarism detection

Cross-language plagiarism detection aims to detect plagiarised fragments of text among documents in different languages. In this paper, we perform a systematic examination of Cross-language Knowledge Graph Analysis; an approach that represents text fragments using knowledge graphs as a language independent content model. We analyse the contributions to cross-language plagiarism detection of the different aspects covered by knowledge graphs: word sense disambiguation, vocabulary expansion, and representation by similarities with a collection of concepts. In addition, we study both the relevance of concepts and their relations when detecting plagiarism. Finally, as a key component of the knowledge graph construction, we present a new weighting scheme of relations between concepts based on distributed representations of concepts. Experimental results in Spanish–English and German–English plagiarism detection show state-of-the-art performance and provide interesting insights on the use of knowledge graphs.     

我国公共图书馆研究领域的知识图谱分析

本文以CSSCI(中文社会科学引文索引)数据库收录的1998-2011年我国公共图书馆研究领域的2 170篇来源文献为数据样本,综合使用了Bibexcel、Pajek、Yahoo map等5种知识图谱工具软件,对十几年来我国公共图书馆领域的科研合作、知识来源以及学术研究的热点和前沿等进行了全景的展示与解析。     

华北平原地下水的功能特征与功能评价

本文针对华北地下水评价中偏重资源而对地下水的生态功能和地质环境功能重视不足的问题,立足于华北平原地下水系统,从地下水的自然属性切入,以综合发挥地下水的资源功能、生态功能和地质环境功能为目标,应用系统论和层次分析法,提出了地下水功能的基本理念及其评价关键技术,包括如何构建评价指标体系和评价成果区划应用分析方法,将地下水的资源供给功能、生态维持功能和地质环境稳定功能统一在水循环系统中科学评价,以达到充分发挥地下水主要功能的综合效益最佳的目标。最后,以华北滹沱河流域作为示范区进行实证分析,结果表明,本文提出的评价方法具有很强的实用性,可划分以资源功能为主或以地质环境功能保护为主的适宜区域,这为调控区域地下水的资源功能与地质环境功能之间矛盾方面提供重要的科学基础。     

A fast retrieving algorithm of hierarchical relationships using trie structures

Case structures are useful for natural language systems, such as word selection of machine translation systems, query understanding of natural language interfaces, meaning disambiguation of sentences and context analyses and so on. The case slot is generally constrained by hierarchical concepts because they are simple knowledge representations. With growing hierarchical structures, they are deeper and the number of concepts to be corresponded to one word increases. From these reasons, it takes a lot of cost to determine whether a concept for a given word is a sub-concept for concepting the case slot or not. This paper presents a faster method to determine the hierarchical relationships by using trie structures. The worst-case time complexity of determining relationships by the presented method could be remarkably improved for the one of linear (or sequential) searching, which depends on the number of concepts in the slot. From the simulation result, it is shown that the presented algorithm is 6 to 30 times faster than linear searching, while keeping the smaller size of tries.     

Gestalt principles of creating learning business ontologies for knowledge codification

This paper presents an approach aimed at creating business ontologies for knowledge codification in company. It is based on the principles of ontological engineering and cognitive psychology. Ontologies that describe the main concepts of knowledge are used both for knowledge creation and codification. The proposed framework is targeted at the development of methodologies that can scaffold the process of knowledge structuring and orchestrating for better understanding and knowledge sharing. The orchestrating procedure is the kernel of ontology development. The main stress is put on using visual techniques of mind mapping. Cognitive bias and some results of Gestalt psychology are highlighted as a general guideline. The ideas of balance, clarity, and beauty are applied to the ontology orchestrating procedures. The examples are taken mainly from the project management practice. The paper contributes to managerial practice by describing the practical recommendations for effective knowledge management based on ontology engineering and knowledge structuring techniques.     

Intrinsically flexible displays: key materials and devices

Continuous progress in flexible electronics is bringing more convenience and comfort to human lives. In this field, interconnection and novel display applications are acknowledged as important future directions. However, it is a huge scientific and technical challenge to develop intrinsically flexible displays due to the limited size and shape of the display panel. To address this conundrum, it is crucial to develop intrinsically flexible electrode materials, semiconductor materials and dielectric materials, as well as the relevant flexible transistor drivers and display panels. In this review, we focus on the recent progress in this field from seven aspects: background and concept, intrinsically flexible electrode materials, intrinsically flexible organic semiconductors and dielectric materials for organic thin film transistors (OTFTs), intrinsically flexible organic emissive semiconductors for electroluminescent devices, and OTFT-driven electroluminescent devices for intrinsically flexible displays. Finally, some suggestions and prospects for the future development of intrinsically flexible displays are proposed.     

Near-duplicate video detection featuring coupled temporal and perceptual visual structures and logical inference based matching

We propose in this paper an architecture for near-duplicate video detection based on: (i) index and query signature based structures integrating temporal and perceptual visual features and (ii) a matching framework computing the logical inference between index and query documents. As far as indexing is concerned, instead of concatenating low-level visual features in high-dimensional spaces which results in curse of dimensionality and redundancy issues, we adopt a perceptual symbolic representation based on color and texture concepts. For matching, we propose to instantiate a retrieval model based on logical inference through the coupling of an N-gram sliding window process and theoretically-sound lattice-based structures. The techniques we cover are robust and insensitive to general video editing and/or degradation, making it ideal for re-broadcasted video search. Experiments are carried out on large quantities of video data collected from the TRECVID 02, 03 and 04 collections and real-world video broadcasts recorded from two German TV stations. An empirical comparison over two state-of-the-art dynamic programming techniques is encouraging and demonstrates the advantage and feasibility of our method.     

Dependency-based IT Governance practices in inter-organisational collaborations: A graph-driven elaboration

In an ever more globalised world IT (Information Technology) managers increasingly have to support value creation within inter-organisational collaboration settings. Such organisational forms with their inherent complexity require specific approaches for their IT management within. Especially important for unleashing the chances of networked arrangements is the right form of IT Governance. Choosing the right arrangement for IT Governance is heavily dependent on understanding the concepts on which such business constellations are built. In this paper we provide therefore first a systematically derived, graph-based perspective on the key terms of inter-organisational collaboration. Based on this understanding of concepts and structured representations of inter-organisational dependencies we present interorganisational governance practices for IT. Specifically, we assign accountabilities to top executive roles from both IT and business. By keeping a holistic perspective, the insights gained in this study are highly relevant for strategic information management in terms of Business-IT Alignment as well as monitoring and controlling of inter-organisational information infrastructures in a rapidly changing business environment.