Integrating diverse information resources in a case-based design environment

The success of case-based design aids depends both on the case-based reasoning processes they apply and on effectively integrating those processes into the larger task context: on making the case-based reasoning component present case information at the right time and in the right way, on exploiting additional information resources as needed to supplement the case library and to guide case application, on capturing useful information from current reasoning and providing it to up- and down-stream designers, and on unobtrusively learning new cases during the design process. This article presents a set of principles and techniques for integrated case-based design support systems and illustrates their application through a case study of the Stamping Advisor, a system to support feasibility analysis for sheet metal automotive parts.     

关于信息管理与信息系统专业《信息存储与检索》课程的研究

本文从信息管理与信息系统的专业目标开始分析,确立《信息存储与检索》课程在该专业中的地位,然后阐述《信息存储与检索》课程的跨多学科的特点,分析当前大学的主要教材,选择最适合信息管理与信息系统专业的教材,针对所选教材阐述了该课程的教学内容及教学方式、方法。     

A comprehensive agent-based architecture for intelligent information retrieval in a distributed heterogeneous environment

The Internet has become the global infrastructure supporting information acquisition and retrieval from many heterogeneous data sources containing high-speed text and rich multimedia images, audio, and video. AgentRAIDER is an ongoing research project at Texas Tech University designed to develop a comprehensive architecture for an intelligent information retrieval system with distributed heterogeneous data sources. The system is designed to support intelligent retrieval and integration of information from the Internet. Current systems of this nature focus only on specific aspects of the distributed heterogeneous problem such as database queries or information filtering. Consequently, these concepts and others have never been successfully integrated into a unified, cohesive architecture. This paper discusses the design and implementation of the AgentRAIDER system and identifies areas for applications of the system in various domains.     

HDI: Integrating Health Data and Tools

Health systems globally are looking to make better use of the data they capture in order to improve their services, both for service provision and clinical outcome. One way of doing this is to integrate existing data sources. However, major technical and legal questions exist concerning data integration, data quality, data security and privacy in health data usage. In this paper, we present the HDI^TM tool, that is currently under development at the e-Health Research Centre. Firstly we describe the HDI^TM architecture and its data integration capabilities. We then consider two of its core capabilities (1) privacy-preserving similarity linkage and (2) on-line analytical techniques and report generating. Finally, we discuss how HDI functionality can be used to provide the capabilities of a knowledge based medical system. The e-Health Research Centre is currently working with the Queensland Health department to deploy the software in practice.     

Agents for information retrieval: Issues of mobility and coordination

This paper focuses on agent-based applications for information retrieval on the Web, by specifically analysing mobility and coordination issues. On the one hand, mobile agents well suit the requirements of information retrieval in the new dynamic scenario derived from the Internet. This is due to their capability of moving to the place where the information is stored – therefore saving bandwidth – and to their robustness in the presence of unreliable connections. On the other hand, the search for information by several mobile active agents calls for suitable models to rule the interactions among agents and between agents and execution environments. The paper surveys different coordination approaches and evaluates their impact in information retrieval applications based on mobile agents. The survey outlines the advantages of uncoupled coordination models and points out the suitability of a coordination model based on reactive and programmable tuple spaces: they may increase the safety and the security of the environment while simplifying the task of programming distributed mobile agent applications.     

Integrating Searching and Authoring in Mizar

The vision of a computerized assistant to mathematicians has existed since the inception of theorem-proving systems. The Alcor system has been designed to investigate and explore how a mathematician might interact with such an assistant by providing an interface to Mizar and the Mizar Mathematical Library. Our current research focuses on the integration of searching and authoring while proving. In this paper we use a scenario to elaborate on the nature of the interaction. We abstract from the scenario two distinct styles of searching and describe how the Alcor interface implements these with a keyword and LSI-based search. Though Alcor is still in its early stages of development, there are clear implications for the general problem of integrating searching and authoring, as well as technical issues with Mizar.     

Browsing a document collection represented in two- and three-dimensional virtual information space

This paper reports a study of information retrieval performance using an interface in which documents were represented by objects in a virtual environment. Spatial location was determined by semantic content, with inter-object distance representing semantic similarity of documents. The quality of spatial-semantic mapping was manipulated as was the number of dimensions (two versus three) in which document nodes were arranged. Participants were required to browse the information space and identify all documents relevant to a specified topic. Results indicated that participants were able to use three-dimensional spatial mapping of semantic information to facilitate task performance, with performance being better when the quality of the mapping was higher. Strategy differences were identified, with participants adopting a more ‘exhaustive’ approach when searching two-dimensional node arrangements, and a more ‘focused’ approach for three-dimensional arrangements. Cognitive ability was not strongly associated with task performance, but participants of relatively lower cognitive ability tended to out-perform those of higher cognitive ability in three-dimensional conditions. Possible reasons for these findings are discussed.     

Consensus-based evaluation framework for distributed information retrieval systems

Multi-agent systems have been attacking the challenges of information retrieval tasks on distributed environment. In this paper, we propose a consensus choice selection method based framework to evaluate the performance of cooperative information retrieval tasks of the multiple agents. Thereby, two well-known measurements, precision and recall, are extended to handle consensual closeness (i.e., local and global consensus) between the sets of retrieved results. We show that in a motivating example the proposed criteria are prone to solve the rigidity problem of classical precision and recall. More importantly, the retrieved results can be ranked with respect to the consensual score, and the ranking mechanism has been verified to be more reasonable.

Multiagent systems and information retrieval our experience with X.MAS

Autonomous agents and multiagent systems have been successfully applied to a number of problems and have been largely used in different application fields. In particular, in this paper we are interested in information retrieval. In fact, in this field multiagent solutions are very useful and effective since they decouple the problem in a network of software agents that interact to solve problems that are beyond the individual capabilities or knowledge. In so doing, multiagent systems allow to overwhelm typical problems of single agent and centralized approaches. To discuss the lesson learnt in using the multiagent technology in the field of information retrieval, in this paper, we present our experience in using X.MAS, a generic multiagent architecture aimed at retrieving, filtering and reorganizing information according to user interests. To this end, after presenting X.MAS, we illustrate six applications built upon it. Our conclusion is that multiagent technology is quite effective to design and realize concrete information retrieval applications.     

An attempt to enhance performance in user session based information retrieval

The ongoing surge in the amount of online information has made the process of accurate retrieval much more difficult. Providers of information retrieval systems have come under a lot of pressure to improve their techniques to cater for the modern user. Conventional systems are often limited as they fail to understand the true search intent of the user. This is usually a result of both poor query formulation by the user and an inability of the search engine to process the query adequately. In this paper, an approach is presented that attempts to learn a user’s short-term interests through the clustering of their search results. A profile is maintained for each user to assist in the process of context resolution for a given query. The details of such an approach and experimental results to evaluate its effectiveness are presented in this paper.     

Design information retrieval: a thesauri-based approach for reuse of informal design information

Information is integral to the engineering design process, and gaining access to design knowledge is critical to effective design decision-making. This paper considers the indexing and retrieval of informal, unstructured information captured from electronic design logbooks. One of the key observations of informal design information is its evolutionary nature over time. While this characteristic makes informal information a rich source for reuse, it also makes it difficult to employ traditional information retrieval (IR) approaches. The work described in this paper is based on a framework developed specifically for the information handling requirements of designers. This manual method for indexing information is adapted to meet the evolutionary nature of design through the development of thesauri for design context. Several approaches to building thesauri are examined, including manual and automated methods. It is found that manual methods provide a high level of IR performance, but also have high overhead requirements. Machine methods, however, may provide a viable, low overhead alternative.

Multi-agent plan based information gathering

The evolution of the Web has encouraged the development of new Information Gathering techniques. Artificial Intelligence techniques, such as Planning, have also been used for Information Gathering in order to go beyond merely retrieving Web data. Planning has been used traditionally to generate a sequence of actions that specify how information sources should be accessed. In this paper, planning is used mainly for integrating information found in heterogeneous sources. For instance, two different Web sources about flight and train travels, can be represented by two different planning operators, which will be subsequently combined and integrated by a single plan. We have found that a Multi-Agent framework is very appropriate to implement our technique. In order to evaluate our approach empirically, it has been applied to a tourism domain (MAPWEB-ETOURISM), whose purpose is to help a customer to plan his/her trips. In this domain, several specialized Web agents have been used to query travel Web sources, whose results are subsequently integrated by a planning agent to build complete travel solutions. Experimental results show that, by means of integration, more solutions can be found than by using single information sources or even travel meta-searchers. Also, (MAPWEB-ETOURISM) can find new types of solutions by integrating information gathered from heterogeneous Web sources (i.e. flights and trains).     

Energy-efficient wireless information retrieval

In this paper, we propose an adaptive application-driven power management (AADPM) protocol for wireless information retrieval applications within the IEEE 802.11b infrastructure WLAN environment. Our goal is to minimize energy consumption while achieving low round trip time delay. We discuss the protocol and evaluate its effectiveness using the network simulator NS2. We also draw horizontal comparisons among a variety of PM methods reported in the literature. Experimental results show that, compared to the power save mode supported by 802.11b, AADPM reduces the network interface card energy consumption by 52% while only introducing 3% RTT delay.     

Do grables enable the extraction of quantitative information better than pure graphs or tables?

Previous research is equivocal regarding the most effective methods of presenting quantitative information displays. The differences in results may be due to numerous reasons including the display and inquiry type. This study examines several methods of displaying quantitative information (e.g., line graphs, line grables, bar charts, bar grables, tables, pie charts and pie grables) that were factorially crossed with different kinds of data extraction inquiries (i.e., questions about exact numerical quantities, comparisons, and trends). Grables are displays that combines features of graphs and tables including specific numerical information with each graphically presented category. Results showed that tables, bar grables and line grables produced the fewest errors, and line graphs and bar charts produced the fastest responses across question types. Error rates combining the accuracy and time (i.e., errors/s) were lowest for the three grables and table. Results are discussed with respect to prior theoretical work and the potential benefits of hybrid forms of quantitative displays for multiple kinds of data extraction inquiries.

Relevance to industry

Choosing the best method of displaying information is important for effective decision making. This study evaluates seven types of graphical displays to answer three types of inquiries. Results indicate that in general, the most efficient data extraction (fewest errors per unit time) were produced using grable or table displays across question types. The appropriate display fosters better communication of information.     

Exploiting concept clusters for content-based information retrieval

Current approaches to index weighting for information retrieval from texts are based on statistical analysis of the texts' contents. A key shortcoming of these indexing schemes, which consider only the terms in a document, is that they cannot extract semantically exact indexes that represent the semantic content of a document. To address this issue, we proposed a new indexing formalism that considers not only the terms in a document, but also the concepts. In the proposed method, concepts are extracted by exploiting clusters of terms that are semantically related, referred to as concept clusters. Through experiments on the TREC-2 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 highest-ranked documents. Moreover, the index term dimension was 53.3% 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.     

Event graphs for information retrieval and multi-document summarization

With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text summarization methods rely on shallow document representations that do not account for the semantics of events. In this article, we present event graphs, a novel event-based document representation model that filters and structures the information about events described in text. To construct the event graphs, we combine machine learning and rule-based models to extract sentence-level event mentions and determine the temporal relations between them. Building on event graphs, we present novel models for information retrieval and multi-document summarization. The information retrieval model measures the similarity between queries and documents by computing graph kernels over event graphs. The extractive multi-document summarization model selects sentences based on the relevance of the individual event mentions and the temporal structure of events. Experimental evaluation shows that our retrieval model significantly outperforms well-established retrieval models on event-oriented test collections, while the summarization model outperforms competitive models from shared multi-document summarization tasks.