Information Retrieval Technology Research Articles

Automatic Student Plagiarism Detection: Future Perspectives

The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to evaluation and comparison of plagiarism detection tools revealed that these contain limitations in detecting complex forms of plagiarism such as extensive paraphrasing and use of technical tricks, such as replacing original characters with similar-looking characters from foreign alphabets. This article investigates limitations in automatic detection of student plagiarism and proposes ways on how these issues could be tackled in future systems by applying various natural language processing and information retrieval technologies. A classification of types of plagiarism is presented, and an analysis is provided of the most promising technologies that have the potential of dealing with the limitations of current state-of-the-art systems. Furthermore, the article concludes with a discussion on legal and ethical issues related to the use of plagiarism detection software. The article, hence, provides a “roadmap” for developing the next generation of plagiarism detection systems.

New technologies for information retrieval to achieve situational awareness and higher patient safety in the surgical operating room: the MRI institutional approach and review of the literature

Technical progress in the operating room (OR) increases constantly, but advanced techniques for error prevention are lacking. It has been the vision to create intelligent OR systems ("autopilot") that not only collect intraoperative data but also interpret whether the course of the operation is normal or deviating from the schedule ("situation awareness"), to recommend the adequate next steps of the intervention, and to identify imminent risky situations. Recently introduced technologies in health care for real-time data acquisition (bar code, radiofrequency identification [RFID], voice and emotion recognition) may have the potential to meet these demands. This report aims to identify, based on the authors' institutional experience and a review of the literature (MEDLINE search 2000-2010), which technologies are currently most promising for providing the required data and to describe their fields of application and potential limitations. Retrieval of information on the functional state of the peripheral devices in the OR is technically feasible by continuous sensor-based data acquisition and online analysis. Using bar code technologies, automatic instrument identification seems conceivable, with information given about the actual part of the procedure and indication of any change in the routine workflow. The dynamics of human activities also comprise key information. A promising technology for continuous personnel tracking is data acquisition with RFID. Emotional data capture and analysis in the OR are difficult. Although technically feasible, nonverbal emotion recognition is difficult to assess. In contrast, emotion recognition by speech seems to be a promising technology for further workflow prediction. The presented technologies are a first step to achieving an increased situational awareness in the OR. However, workflow definition in surgery is feasible only if the procedure is standardized, the peculiarities of the individual patient are taken into account, the level of the surgeon's expertise is regarded, and a comprehensive data capture can be obtained.

Evaluation of information retrieval for E-discovery

The effectiveness of information retrieval technology in electronic discovery (E-discovery) has become the subject of judicial rulings and practitioner controversy. The scale and nature of E-discovery tasks, however, has pushed traditional information retrieval evaluation approaches to their limits. This paper reviews the legal and operational context of E-discovery and the approaches to evaluating search technology that have evolved in the research community. It then describes a multi-year effort carried out as part of the Text Retrieval Conference to develop evaluation methods for responsive review tasks in E-discovery. This work has led to new approaches to measuring effectiveness in both batch and interactive frameworks, large data sets, and some surprising results for the recall and precision of Boolean and statistical information retrieval methods. The paper concludes by offering some thoughts about future research in both the legal and technical communities toward the goal of reliable, effective use of information retrieval in E-discovery.

A service-based framework for pharmacogenomics data integration

Data are central to scientific research and practices. The advance of experiment methods and information retrieval technologies leads to explosive growth of scientific data and databases. However, due to the heterogeneous problems in data formats, structures and semantics, it is hard to integrate the diversified data that grow explosively and analyse them comprehensively. As more and more public databases are accessible through standard protocols like programmable interfaces and Web portals, Web-based data integration becomes a major trend to manage and synthesise data that are stored in distributed locations. Mashup, a Web 2.0 technique, presents a new way to compose content and software from multiple resources. The paper proposes a layered framework for integrating pharmacogenomics data in a service-oriented approach using the mashup technology. The framework separates the integration concerns from three perspectives including data, process and Web-based user interface. Each layer encapsulates the heterogeneous issues of one aspect. To facilitate the mapping and convergence of data, the ontology mechanism is introduced to provide consistent conceptual models across different databases and experiment platforms. To support user-interactive and iterative service orchestration, a context model is defined to capture information of users, tasks and services, which can be used for service selection and recommendation during a dynamic service composition process. A prototype system is implemented and cases studies are presented to illustrate the promising capabilities of the proposed approach.

Mobile merchandise evaluation service using novel information retrieval and image recognition technology

Consumers’ purchasing behavior has obviously changed in recent years with developments in social economics. This change has been evident in the decreased ratio of planned purchases but not in the increase of planned (or spontaneous) purchases. This act of spontaneous or otherwise unplanned purchasing is called “impulse buying”. However, buying under these conditions costs more money always comes with negative responses, such as complaints and regret. Therefore, we propose and have designed a new merchandise recommendation system, the Mobile Merchandise Evaluation Service Platform (MMESP). This is a three-tier system composed of Real-time Merchandise Identifying System (RMIS), Real-time Merchandise Evaluation System (RMES), and Real-time Merchandise Recommendation System (RMRS). With this system, Mobile Users (MUs) take pictures of merchandise and send them to MMESP, RMIS integrates Region Adjacency Graph (RAG) and Self-Organizing Maps (SOM) to gather information on the merchandise through those photographs, and. RMES and RMRS provide Intelligence Agents (IAs) and Multiple Document Summarization (MDS) to summarize recommendations on merchandise for MUs, all in real time.

An application of document filtering in an operational system

This paper describes an applied document filtering system embedded in an operational watch center that monitors disease outbreaks worldwide. At the initial time of this writing, the system effectively supported monitoring of 23 geographic regions by filtering documents in several thousand daily news sources in 11 different languages. This paper describes the filtering algorithm, statistical procedures for estimating Precision and Recall in an operational environment, summarizes operational performance data and suggests lessons learned for other applications of document filtering technology. Overall, these results are interpreted as supporting the general utility of document filtering and information retrieval technology and offers recommendations for future applications of this technology.

AQUA: A Closed-Domain Question Answering System

This article describes AQUA, an experimental question answering system. AQUA combines Natural Language processing (NLP), Ontologies, Logic, and Information Retrieval technologies in a uniform framework. AQUA makes intensive use of an ontology in several parts of the question answering system. The ontology is used in the refinement of the initial query, the reasoning process and in the novel similarity algorithm. The similarity algorithm is a key feature of AQUA. It is used to find similarities between relations/concepts in the translated query and relations/concepts in the ontological structures. The similarities detected then allow the interchange of concepts or relations in a logic formula corresponding to the user query.

Sports Information Retrieval for Video Annotation

The rapid evolution of digital equipments has led to the explosive proliferation of multimedia data in education, entertainment, sport and various applications. The development of automatic or semi-automatic systems and tools for digital content analysis and understanding becomes compelling. As important multimedia content, sports video has been attracting increasing attention due to commercial benefits, entertaining functionalities and audience requirements. Much research on shot classification, highlight extraction and event detection in sports video has been done to provide interactive video viewing systems for quick browsing, indexing and summarization. More keenly than ever, the audience desires professional insights into the games. The coach and players demand automatic tactics analysis and performance evaluation with the aid of multimedia information retrieval technologies. Therefore, sports video analysis is certainly a research issue worth investigation. In this article, we review current research and give an insight into sports video analysis.

Model for Internet News Force Evaluation Based on Information Retrieval Technologies

利用信息检索领域中的相关算法,分析研究通过信息检索相关技术得到的相关信息,建立了一个网络新闻影响力模型来定量地计算一则新闻的影响力,从而估计它对社会安全产生影响的程度.在对大量实验结果的统计分析中发现,此方法可以有效地对新闻文章进行排序,发现不同新闻类型中最值得关注的新闻,其结果与人的定性判断结果具有较高的一致性.;Evaluating the force of Internet news is an important aspect in the social security field. This paper establishes a quantitative analysis model for calculating the Internet news force with information retrieval methods based on the correlative information got through information retrieval technologies. This method can help people understand how great the news affects the social security. A lot of experiments, lead to a conclusion: this method can rank a stream of news effectively, and also can find most important news items from a number of news which belong to different types; its result is very close to people’s judgment.

Performance evaluation of density-based clustering methods

With the development of the World Wide Web, document clustering is receiving more and more attention as an important and fundamental technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. A good document clustering approach can assist computers in organizing the document corpus automatically into a meaningful cluster hierarchy for efficient browsing and navigation, which is very valuable for complementing the deficiencies of traditional information retrieval technologies. In this paper, we study the performance of different density-based criterion functions, which can be classified as internal, external or hybrid, in the context of partitional clustering of document datasets. In our study, a weight was assigned to each document, which defined its relative position in the entire collection. To show the efficiency of the proposed approach, the weighted methods were compared to their unweighted variants. To verify the robustness of the proposed approach, experiments were conducted on datasets with a wide variety of numbers of clusters, documents and terms. To evaluate the criterion functions, we used the WebKb, Reuters-21578, 20Newsgroups-18828, WebACE and TREC-5 datasets, as they are currently the most widely used benchmarks in document clustering research. To evaluate the quality of a clustering solution, a wide spectrum of indices, three internal validity indices and seven external validity indices, were used. The internal validity indices were used for evaluating the within-cluster scatter and between cluster separations. The external validity indices were used for comparing the clustering solutions produced by the proposed criterion functions with the “ground truth” results. Experiments showed that our approach significantly improves clustering quality. In this paper, we developed a modified differential evolution (DE) algorithm to optimize the criterion functions. This modification accelerates the convergence of DE and, unlike the basic DE algorithm, guarantees that the received solution will be feasible.

Fast information retrieval from web pages

In this paper, a new fast algorithm for information retrieval is presented. Such algorithm relies on performing cross correlation in the frequency domain between input data and the input weights of fast neural networks (FNNs). It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). The main objective of Internet users is to find the required information with high efficiency and effectiveness. Finding information on an object's visual features is useful when specific keywords for the object are not known. Since intelligent mobile agent technology is expected to be a promising technology for information retrieval, there is a number of intelligent mobile agent based-information retrieval approaches have been proposed in recent years. Here, the work presented in [25] for image-based information retrieval using mobile agents is greatly enhanced. Multiple information agents continuously traverse the Internet and collect images that are subsequently indexed based on image information such as the URL location, size, type and the date of indexation. In the search phase, the intelligent mobile agent receives the image of object as a query and searches the set of web pages that contain information about the object. This is done by matching the query to images on web pages faster than the work presented in [25]. Furthermore, by applying cross correlation, object detection becomes position independent. Moreover, by using neural networks, the object can be detected even with rotation, scaling, noise, distortion or deformation in shape.

The Cyber Sublime and the Virtual Mirror: Information and Media in the Works of Oshii Mamoru and Kon Satoshi

L’influence des médias et de l’informatique sur les sociétés humaines constitue un des principaux thèmes façonnant les stratégies visuelles et les orientations philosophiques des animes de Oshii Mamoru et Kon Satoshi. L’analyse des Ghost in the Shell et Innocence d’Oshii révèle ainsi que le développement d’une esthétique du sublime cybernétique se trouve simultanément fondé sur un univers hiérarchique et animé par un rêve de transcendance. L’étude des films de Kon, tels Perfect Blue, Millennium Actress et Paprika, dévoile quant à elle une approche très différente des questions de représentation : celle du « miroir virtuel ». Dans ces œuvres, la réalité quotidienne est saturée de portails ressemblant à des miroirs, et menant à des lieux identitaires alternatifs où l’on se prête à des jeux intersubjectifs ; des espaces nonhiérarchiques qui, contrairement à la vision d’Oshii, suggèrent une vue immanente de l’univers.

Cross-language search: The case of Google Language Tools

This paper presents a case study of Google Language Tools, especially its cross-language search service. Cross-language search integrates machine translation (MT) and cross-language information retrieval (CLIR) technologies and allows Web users to search and read pages written in languages different from their search terms. In addition to cross-language search, Google Language Tools provides various language support services to multilingual information access. Our study examines the functions of Google Language Tools and the performance of its cross-language search. The results and analysis show that Google Language Tools are useful for Web users. Its cross-language search service provides quality query translation while the automatic translation of result pages needs further improvement. The paper suggests that cross-language search could be used by different types of Web users. The authors also discuss the strategies and important issues with regard to implementing multilingual information access services for information systems.

A methodology for engineering ontology acquisition and validation

AbstractWhen engineering content is created and applied during the product life cycle, it is often stored and forgotten. Current information retrieval approaches based on statistical methods and keyword matching are not effective in understanding the context of engineering content. They are not designed to be directly applicable to the engineering domain. Therefore, engineers have very limited means to harness and reuse past designs. The overall objective of our research is to develop an engineering ontology (EO)-based computational framework to structure unstructured engineering documents and achieve more effective information retrieval. This paper focuses on the method and process to acquire and validate the EO. The main contributions include a new, systematic, and more structured ontology development method assisted by a semiautomatic acquisition tool. This tool is integrated with Protégé ontology editing environment; an engineering lexicon (EL) that represents the associated lexical knowledge of the EO to bridge the gap between the concept space of the ontology and the word space of engineering documents and queries; the first large-scale EO and EL acquired from established knowledge resources for engineering information retrieval; and a comprehensive validation strategy and its implementations to justify the quality of the acquired EO. A search system based on the EO and EL has been developed and tested. The retrieval performance test further justifies the effectiveness of the EO and EL as well as the ontology development method.

DB&IR integration

This paper is based on a five-day workshop on "Ranked XML Querying" that took place in Schloss Dagstuhl in Germany in March 2008 and was attended by 27 people from three different research communities: database systems (DB), information retrieval (IR), and Web. The seminar title was interpreted in an IR-style "andish" sense (it covered also subsets of {Ranking, XML, Querying}, with larger sets being favored) rather than the DB-style strictly conjunctive manner. So in essence, the seminar really addressed the integration of DB and IR technologies with Web 2.0 being an important target area.

Genetic algorithm for text clustering based on latent semantic indexing

In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with the vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Latent semantic indexing (LSI) is a successful technology in information retrieval which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. Meanwhile, LSI takes into account the effects of synonymy and polysemy, which constructs a semantic structure in textual data. GA belongs to search techniques that can efficiently evolve the optimal solution in the reduced space. We propose a variable string length genetic algorithm which has been exploited for automatically evolving the proper number of clusters as well as providing near optimal data set clustering. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. The superiority of GAL approach over conventional GA applied in VSM model is demonstrated by providing good Reuter document clustering results.

DB&IR integration

This paper is based on a five-day workshop on "Ranked XML Querying" that took place in Schloss Dagstuhl in Germany in March 2008 and was attended by 27 people from three different research communities: database systems (DB), information retrieval (IR), and Web. The seminar title was interpreted in an IR-style "andish" sense (it covered also subsets of (Ranking, XML, Querying), with larger sets being favored) rather than the DB-style strictly conjunctive manner. So in essence, the seminar really addressed the integration of DB and IR technologies with Web 2.0 being an important target area.

AMAZING: A sentiment mining and retrieval system

With the rapid growth of e-commerce, there are a great number of customer reviews on the e-commerce websites. Generally, potential customers usually wade through a lot of on-line reviews in order to make an informed decision. However, retrieving sentiment information relevant to customer’s interest still remains challenging. Developing a sentiment mining and retrieval system is a good way to overcome the problem of overloaded information in customer reviews. In this paper, we propose a sentiment mining and retrieval system which mines useful knowledge from consumer product reviews by utilizing data mining and information retrieval technology. A novel ranking mechanism taking temporal opinion quality (TOQ) and relevance into account is developed to meet customers’ information need. Besides the trend movement of customer reviews and the comparison between positive and negative evaluation are presented visually in the system. Experimental results on a real-world data set show the system is feasible and effective.

Integrating Data Warehouses with Web Data: A Survey

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query and retrieve web data, and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semi-structured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources and the XML extensions of On-Line Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich documents collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as, to identify open research lines.

Content-Based Information Retrieval and Digital Libraries

This paper discusses the applications and importance of content-based information retrieval technology in digital libraries. It generalizes the process and analyzes current examples in four areas of the technology. Content-based information retrieval has been shown to be an effective way to search for the type of multimedia documents that are increasingly stored in digital libraries. As a good complement to traditional textbased information retrieval technology, content-based information retrieval will be a significant trend for the development of digital libraries.