An Automated Document Filing System

TEXPROS (TEXt PROcessing System) is an automatic document processing system which supports text-based information representation and manipulation, conveying meanings from stored information within office document texts. A dual modeling approach is employed to describe office documents and support document search and retrieval. The frame templates for representing document classes are organized to form a document type hierarchy. Based on its document type, the synopsis of a document is extracted to form its corresponding frame instance. According to the user predefined criteria, these frame instances are stored in different folders, which are organized as a folder organization (i.e., repository of frame instances associated with their documents). The concept of linking folders establishes filing paths for automatically filing documents in the folder organization. By integrating document type hierarchy and folder organization, the dual modeling approach provides efficient frame instance access by limiting the searches to those frame instances of a document type within those folders which appear to be the most similar to the corresponding queries.This paper presents an agent-based document filing system using folder organization. A storage architecture is presented to incorporate the document type hierarchy, folder organization and original document storage into a three-level storage system. This folder organization supports effective filing strategy and allows rapid frame instance searches by confining the search to the actual predicate-driven retrieval method. A predicate specification is proposed for specifying criteria on filing paths in terms of user predefined predicates for governing the document filing. A method for evaluating whether a given frame instance satisfies the criteria of a filing path is presented. The basic operations for constructing and reorganizing a folder organization are proposed.     

Personal Document Management and Retrieval: A Knowledge-Based Approach

This paper presents a knowledge-based approach to managing and retrieving personal documents. The dual document models consist of a document type hierarchy and a folder organization. The document type hierarchy is used to capture the layout, logical and conceptual structures of documents. The folder organization mimics the user's real-world document filing system for organizing and storing documents in an office environment. Predicate-based representation of documents is formalized for specifying knowledge about documents. Document filing and retrieval are predicate-driven. The filing criteria for the folders, which are specified in terms of predicates, govern the grouping of frame instances, regardless of their document types. We incorporated the notions of document type hierarchy and folder organization into the multilevel architecture of document storage. This architecture supports various text-based information retrieval techniques and content-based multimedia information retrieval techniques. The paper also proposes a knowledge-based query-preprocessing algorithm, which reduces the search space. For automating the document filing and retrieval, a predicate evaluation engine with a knowledge base is proposed. The learning agent is responsible for acquiring the knowledge needed by the evaluation engine.     

脚本技术在NCRE考试环境配置中的应用

利用WMI和WSH实现了对NCRE服务器中工作组、用户、用户文件夹及其共享和不同用户对用户文件夹的控制权限的设置;实现了NCRE工作站日期时间、TCP/IP及网络驱动器的映射。     

A predicate-driven document filing system

This paper presents a predicate-driven document filing system for organizing and automatically filing documents. A document model consists of two basic elements: frame templates representing document classes, and folders which are repositories of frame instances. The frame templates can be organized to form a document type hierarchy, which helps classify and file documents. Frame instances are grouped into a folder on the basis of user-defined criteria, specified as predicates which determine whether a frame instance belongs to a folder. Folders can naturally organized into a folder organization which represents the user's real world document filing system. The predicate consistency problem is discussed to eliminate two abnormalities from a folder organization: inapplicable edges (filing paths) and redundant folders. An evaluating net (including an association dictionary, an instantiation component and a production system) is then proposed for evaluating whether a frame instance satisfies the predicate of a folder during document filing. And the concept of consistency a rule base is also discussed.This work was supported by the Separately Budgeted Research (SBR) grant (No. 421190) from New Jersey Institute of Technology and the Systems Integration Program grant from AT&T Foundation     

A formal approach to modeling office information systems

This paper formally specifies a document model for office information systems, including formal definitions of document types (frame templates), a document type hierarchy, folders, and folder organizations. Folder Organizations are defined using predicates and directed graphs. AReconstruction Problem for folder organizations is then formulated; viz., under what circumstances it is possible to reconstruct a folder organization from its folder level predicates. The Reconstruction Problem is solved in terms of such graph-theoretic concepts as Associated Digraphs, transitive closure, and redundant/nonredundant filing paths. A Transitive Closure Inversion algorithm is then presented which efficiently recovers a Folder Organization digraph from its Associated Digraph.This work was supported in part by the National Science Foundation under Grant No. IRI-9224602, by the New Jersey Institute of Technology undre Grant No. 421280 and by a grant from AT&T Foundation.     

Using NLP for machine learning of user profiles

As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem.     

Use of executable formal specifications in user validation

Requirements validation through feedback with users is of paramount importance in producing a high quality requirements specification document. Use of an executable formal specification offers an effective combination of formalism and pragmatism. This allows not only the systematic development of a concise specification of a system, but it also enables developers to execute the specification to receive feedback at an early stage. Executable formal specification languages have traditionally been used as an effective prototyping tool to facilitate developer validation, that is the developer can, via specification execution either individually or in a peer review format, explore the consequences of the specification. However, their use in requirements validation is often not user orientated, which may in turn reduce the effectiveness of the approach. This paper reports on work to facilitate the user validation process based on executable formal specifications. A user orientated process with a systematic framework can maximise the effectiveness of the user validation process. Dialogue management based on scenarios enables an effective communication between a system and its users. Our approach also enables the intertwining of equational specifications in a modular algebraic specification language and conventional implementations in a modular programming language. This introduces a judicious choice of rigour, techniques and tools to support the user dialogue with a prototype system to effectively and explicitly address the user validation process. © 1998 John Wiley & Sons, Ltd.     

A personalized search engine based on Web‐snippet hierarchical clustering

We propose a (meta‐)search engine, called SnakeT (SNippet Aggregation for Knowledge ExtracTion), which queries more than 18 commodity search engines and offers two complementary views on their returned results. One is the classical flat‐ranked list, the other consists of a hierarchical organization of these results into folders created on‐the‐fly at query time and labeled with intelligible sentences that capture the themes of the results contained in them. Users can browse this hierarchy with various goals: knowledge extraction, query refinement and personalization of search results. In this novel form of personalization, the user is requested to interact with the hierarchy by selecting the folders whose labels (themes) best fit her query needs. SnakeT then personalizes on‐the‐fly the original ranked list by filtering out those results that do not belong to the selected folders. Consequently, this form of personalization is carried out by the users themselves and thus results fully adaptive, privacy preserving, scalable and non‐intrusive for the underlying search engines. We have extensively tested SnakeT and compared it against the best available Web‐snippet clustering engines. SnakeT is efficient and effective, and shows that a mutual reinforcement relationship between ranking and Web‐snippet clustering does exist. In fact, the better the ranking of the underlying search engines, the more relevant the results from which SnakeT distills the hierarchy of labeled folders, and hence the more useful this hierarchy is to the user. Vice versa, the more intelligible the folder hierarchy, the more effective the personalization offered by SnakeT on the ranking of the query results. Copyright © 2007 John Wiley & Sons, Ltd.     

Collaborative Multiagent Adaptation for Business Environmental Scanning Through the Internet

Businesses should promptly respond to their dynamic environments. Environmental scanners are thus essential for the businesses to discover and monitor environmental information of interest (IOI). In this paper, we explore user-centered, continuous and resource-bounded environmental scanning (UCRES). Upon receiving information preferences of managers, new IOI should be continuously detected in a timely and complete manner without consuming too much resource (e.g. bandwidths of computer networks and services of information servers). We develop a multiagent framework AESA to tackle the challenges of UCRES. Each agent is a simple entity. All agents collaboratively adapt their population and resource consumption to several dynamic aspects of UCRES: information preferences of individual users, resource limitation of environmental scanning, distribution of IOI in the environments, and update behaviors of the IOI. The delivery of AESA to businesses may constantly provide a larger amount of important and timely IOI without exhausting the Intranet and the Internet communities.     

个性化元搜索引擎的研究与设计

随着Web信息的快速增长和人们对信息检索质量要求的提高,传统的搜索引擎已不能很好地满足人们的需求. 本文提出了一种个性化元搜索引擎模型.个性化是指模型可以针对不同的用户建立不同的用户兴趣模型,然后根据用户兴趣,模型对搜索结果进行过滤、重排序处理,使得显示给用户的搜索结果更具有针对性.本文阐述了各主要功能模块工作原理,并详细介绍了根据用户兴趣模型对搜索结果进行排序的算法,实验表明该算法能够有效地提高用户的检索质量.     

Generating queries and replies during information-seeking interactions

Analysis of naturally occurring information-seeking dialogues indicates that they usually consist of a number of distinct discourse segments, such as a greeting segment, a request issued by a user, an optional clarification segment, a transfer of information segment and a final closing segment. The clarification interaction is often initiated by the information provider and it may be due to one of the following reasons: (1) there is confusion regarding the user's intentions, (2) there is insufficient information to formulate a plan to satisfy a recognized intention, or (3) there is difficulty in formulating a plan that satisfies a recognized intention. Once the information provider determines the user's intention and formulates a plan to achieve this intention, the information transfer phase is initiated to inform the user about the proposed plan.In this paper, we present a mechanism for generating queries during the clarification stage and answers during the information transfer stage. Given a hierarchical representation of the alternatives possibly intended by a user and the probabilities of these alternatives, our mechanism determines the hierarchy level at which a query must be directed and the query to be posed in order to determine the alternative intended by the user. Once the user's intentions are ascertained, the mechanism determines whether additional information is required and the manner in which queries may be posed to acquire this information. When a user's intentions cannot be satisfied by means of a single plan, our mechanism enters into a negotiation process to alter the user's specifications until a valid plan is formulated. In the final stages of the interaction, the mechanism determines the information to be transferred and generates an answer to effect the transfer. The mechanisms for negotiation and for the generation of queries and answers described in this paper have been implemented in a system called , a computerized information providing system that functions as a travel agent.     

Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach

This paper proposes an automatic folder allocation system for text documents through the implementation of a hybrid classification method which combines the Bayesian (Bayes) approach and the Support Vector Machines (SVMs). Folder allocation for text documents in computer is typically executed manually by the user. Every time the user creates text documents by using text editors or downloads the documents from the internet, and wishes to store these documents on the computer, the user needs to determine and allocate the appropriate folder in which to store these new documents. This situation is inconvenient as repeating the folder allocation each time a text document is stored becomes tedious especially when the numbers and layers of folders are huge and the structure is complex and continuously growing. This problem can be overcome by implementing Artificial Intelligence machine learning methods to classify the new text documents and allocate the most appropriate folder as the storage for them. In this paper we propose the Bayes-SVMs hybrid classification framework to perform the tedious task of automatically allocating the right folder for text documents in computers.     

基于简化ODP的用户兴趣模型

通过搜集搜索引擎用户的个人兴趣偏好,个性化搜索技术能够对搜索结果中的页面进行分析并与用户的兴趣进行比较,帮助用户从中找出更为感兴趣的结果,从而提高用户的搜索效率。通过利用简化的ODP目录层次结构进行训练以建立基本的用户兴趣树型结构,并在模型使用过程中通过用户的隐式操作反馈,对用户兴趣模型进行动态更新以反映用户不断变化的兴趣偏好。这一用户兴趣建模方法以简化的ODP结构为参考框架,并以用户个人的搜索行为作为模型修正和更新的依据,实现消除词条歧义并且表达用户个人兴趣偏好的目的。     

利用门控网络构建用户动态兴趣的序列推荐模型

在推荐系统中,现有多数序列推荐方法将用户行为视为一个时间有序的序列进行用户兴趣建模,用户兴趣的动态变化导致模型难以从用户行为序列中捕捉准确的用户兴趣信息。针对该问题,同时考虑到项目间成对的共现模式应作为交互信息的补充,提出利用门控网络构建用户动态兴趣的序列推荐模型DCGN。使用门控线性单元捕获交互序列中的用户兴趣,利用带有注意力权重的门控循环网络学习用户的动态兴趣。在此基础上,对用户交互项目间的共现模式进行建模,与用户兴趣信息以及用户信息进行融合后输入深度神经网络,得到最终推荐结果。在ML100K、Amazon 5-Elect、Retailrocket 3个公开数据集上进行实验,使用精确率、归一化折损累积增益和命中率进行性能评估,结果表明,DCGN模型较NARM、GRU4Rec、NLR等主流序列推荐模型性能更优,其归一化折损增益和精确率在Retailrocket数据集上平均提升1.9%和1.22%,在Amazon 5-Elect数据集上平均提升0.82%和1.05%,在ML100K数据集上平均提升0.36%和0.31%。     

基于兴趣聚类的自动建模

针对传统粗兴趣粒度表示的建模方法不能准确描述每个用户的兴趣主题的问题,提出基于兴趣聚类的自动建模方法.利用文档聚类发现用户的多个子兴趣主题,从而提高对用户兴趣偏好描述的准确性.将该方法用于个性化信息检索,取得较好的效果.     

基于Web访问信息的用户兴趣迁移模式的研究

针对用户浏览网页的兴趣会随时间而变化这一现象,设计了一种网络用户兴趣迁移模式的挖掘模型。把用户的访问兴趣通过隐马尔可夫模型抽象成一种时间序列,以此反映用户兴趣的序列性,进而利用GSP算法从用户兴趣序列中挖掘出用户兴趣的迁移模式。实验证明该方法是有效的,从时间属性上更深层次地描述了用户兴趣的变化情况。     

基于内容的推荐与协同过滤融合的新闻推荐方法

针对基于内容的新闻推荐方法中用户兴趣多样性的缺乏问题和混合推荐方法存在的冷启动问题,提出一种基于内容与协同过滤融合的方法进行新闻推荐。首先利用基于内容的方法发现用户既有兴趣;再用内容与行为的混合相似度模式,寻找目标用户的相似用户群,预测用户对特征词的兴趣度,发现用户潜在兴趣;然后将用户既有兴趣与潜在兴趣融合,得到兼具个性化和多样性的用户兴趣模型;最后将候选新闻与融合模型进行相似度计算,形成推荐列表。实验结果显示,与基于内容的推荐方法相比,所提方法的F-measure和整体多样性Diversity均有明显提高;与混合推荐方法相比,性能相当,但候选新闻无需耗时积累足够的用户点击量,不存在冷启动问题。     

A Visual Syntax-directed Editor for the Descartes Specification Language

The front end of the software life cycle is an important phase in the development of quality software systems. Due to the interactive nature of the requirements specification phase, automated tools assist in capturing user concepts and conveying these concepts to the developers. This paper presents a syntax-directed editor developed for the Descartes software specification language. The editor helps in constructing the specifications by detecting and preventing syntactic and static semantic errors. The editor uses the visual notation of Nassi–Shneiderman charts for the external representation of Descartes specifications. The specifications are edited in their visual form directly within the editor. Use of a syntax-directed editor for the construction of Descartes specifications is expected to reduce specification errors, enhance constructibility, and in turn increase productivity. © 1997 John Wiley & Sons, Ltd.     

基于用户兴趣的个性化模型研究

大多数搜索引擎没有考虑到用户的个性和兴趣,大大降低了搜索的准确性。采用Web挖掘技术对存放在Web缓存中的历史页面进行挖掘,获取用户的兴趣信息,使用最优二叉树的形式来表示用户兴趣,利用获取的用户兴趣信息来构建个性化模型,并且利用智能Agent跟踪用户的兴趣变化,不断地对用户兴趣个性化模型进行更新。