Search

This summary is about a special issue of the International Journal on Digital Libraries (IJDL) that features selected papers from the 23rd International Conference on Asia-Pacific Digital Libraries (ICADL 2021). ICADL is an annual interdisciplinary conference that connects digital libraries, computer science, and library and information science communities. The special issue includes five papers that have undergone a rigorous review process and cover topics such as improving scientific publication retrieval, multi-label document classification, extracting leaderboards from research papers, and AI-powered peer review systems. The articles offer diverse perspectives and high-quality research within these fields. [Extracted from the article]

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, in our former work, we identified 'potential citation papers' through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigated which sections of papers can be leveraged to represent papers effectively. While this initial approach works well for researchers vested in a single discipline, it generates poor predictions for scientists who work on several different topics in the discipline (hereafter, 'intra-disciplinary'). We thus extend our previous work in this paper by proposing an adaptive neighbor selection method to overcome this problem in our imputation-based collaborative filtering framework. On a publicly-available scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when using our adaptive neighbor selection method. While recommendation performance is enhanced for all researchers, improvements are more marked for intra-disciplinary researchers, showing that our method does address the targeted audience. [ABSTRACT FROM AUTHOR]

Finding relevant publications in the scientific domain can be quite tedious: Accessing large-scale document collections often means to formulate an initial keyword-based query followed by many refinements to retrieve a sufficiently complete, yet manageable set of documents to satisfy one's information need. Since keyword-based search limits researchers to formulating their information needs as a set of unconnected keywords, retrieval systems try to guess each user's intent. In contrast, distilling short narratives of the searchers' information needs into simple, yet precise entity-interaction graph patterns provides all information needed for a precise search. As an additional benefit, such graph patterns may also feature variable nodes to flexibly allow for different substitutions of entities taking a specified role. An evaluation over the PubMed document collection quantifies the gains in precision for our novel entity-interaction-aware search. Moreover, we perform expert interviews and a questionnaire to verify the usefulness of our system in practice. This paper extends our previous work by giving a comprehensive overview about the discovery system to realize narrative query graph retrieval. [ABSTRACT FROM AUTHOR]

This paper presents ARCA, a software system that enables semantic search and exploration over a book catalog. The main purpose of this work is twofold: to propose a general paradigm for a semantic enrichment workflow and to evaluate a visual approach to information retrieval based on extracted information and existing knowledge graphs. ARCA has been designed and implemented following a user-centered design approach. Two different releases of the system have incrementally and iteratively developed and evaluated. The first release has evaluated the quality and usefulness of the extracted data. The second release, whose design was a refinement based on the previous evaluation results, was assessed by several users. Moreover, a comparative test with other information retrieval systems was conducted in order to study the potential added-value of the system. ARCA is employed in a real editorial scenario to visually search and explore the books of a publishing house. [ABSTRACT FROM AUTHOR]

Video retrieval methods, e.g., for visual concept classification, person recognition, and similarity search, are essential to perform fine-grained semantic search in large video archives. However, such retrieval methods often have to be adapted to the users' changing search requirements: which concepts or persons are frequently searched for, what research topics are currently important or will be relevant in the future? In this paper, we present VIVA, a software tool for building content-based video retrieval methods based on deep learning models. VIVA allows non-expert users to conduct visual information retrieval for concepts and persons in video archives and to add new people or concepts to the underlying deep learning models as new requirements arise. For this purpose, VIVA provides a novel semi-automatic data acquisition workflow including a web crawler, image similarity search, as well as review and user feedback components to reduce the time-consuming manual effort for collecting training samples. We present experimental retrieval results using VIVA for four use cases in the context of a historical video collection of the German Broadcasting Archive based on about 34,000 h of television recordings from the former German Democratic Republic (GDR). We evaluate the performance of deep learning models built using VIVA for 91 GDR specific concepts and 98 personalities from the former GDR as well as the performance of the image and person similarity search approaches. [ABSTRACT FROM AUTHOR]

This paper investigates how Linked Open Data (LOD) can be used for recommendations and information retrieval within digital libraries. While numerous studies on both research paper recommender systems and Linked Data-enabled recommender systems have been conducted, no previous attempt has been undertaken to explore opportunities of LOD in the context of search and discovery interfaces. We identify central advantages of Linked Open Data with regard to scientific search and propose two novel recommendation strategies, namely flexible similarity detection and constraint-based recommendations. These strategies take advantage of key characteristics of data that adheres to LOD principles. The viability of Linked Data recommendations was extensively evaluated within the scope of a web-based user experiment in the domain of economics. Findings indicate that the proposed methods are well suited to enhance established search functionalities and are thus offering novel ways of resource access. In addition to that, RDF triples from LOD repositories can complement local bibliographic records that are sparse or of poor quality. [ABSTRACT FROM AUTHOR]

Scientific literature is currently the most important resource for scholars, and their citations have provided researchers with a powerful latent way to analyze scientific trends, influences and relationships of works and authors. This paper is focused on automatic citation analysis and summarization for the scientific literature of computational linguistics, which are also the shared tasks in the 2016 workshop of the 2nd Computational Linguistics Scientific Document Summarization at BIRNDL 2016 (The Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries). Each citation linkage between a citation and the spans of text in the reference paper is recognized according to their content similarities via various computational methods. Then the cited text span is classified to five pre-defined facets, i.e., Hypothesis, Implication, Aim, Results and Method, based on various features of lexicons and rules via Support Vector Machine and Voting Method. Finally, a summary of the reference paper from the cited text spans is generated within 250 words. hLDA (hierarchical Latent Dirichlet Allocation) topic model is adopted for content modeling, which provides knowledge about sentence clustering (subtopic) and word distributions (abstractiveness) for summarization. We combine hLDA knowledge with several other classical features using different weights and proportions to evaluate the sentences in the reference paper. Our systems have been ranked top one and top two according to the evaluation results published by BIRNDL 2016, which has verified the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a system for summarization of scientific and structured documents that has three components: section mixture models are used for estimation of the weights of terms; a hypothesis test to select a subset of these terms; and a sentence extractor based on techniques for combinatorial optimization. The section mixture models approach is an adaptation of a bigram mixture model based on the main sections of a scientific document and a collection of citing sentences (citances) from papers that reference the document. The model was adapted from earlier work done on Biomedical documents used in the summarization task of the 2014 Text Analysis Conference (TAC 2014). The mixture model trained on the Biomedical data was used also on the data for the Computational Linguistics scientific summarization task of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (CL-SciSumm 2016). This model gives rise to machine-generated summaries with ROUGE scores that are nearly as strong as those seen on the Biomedical data and was also the highest scoring submission to the task of generating a human summary. For sentence extraction, we use the OCCAMS algorithm (Davis et al., in: Vreeken, Ling, Zaki, Siebes, Yu, Goethals, Webb, Wu (eds) ICDM workshops, IEEE Computer Society, pp 454-463, 2012) which takes the sentences from the original document and the assignment of weights of the terms computed by the language models and outputs a set of minimally overlapping sentences whose combined term coverage is maximized. Finally, we explore the importance of an appropriate background model for the hypothesis test to select terms to achieve the best quality summaries. [ABSTRACT FROM AUTHOR]

The Dutch Song Database is a digital repository documenting Dutch song culture in past and present. It contains more than 173 thousand references to song occurrences in the Dutch and Flemish language, from the Middle Ages up to the present, as well as over 18 thousand descriptions of song sources, such as song books, manuscripts and field recordings, all adhering to high quality standards. In this paper, we present the history and functionality of the database, and we demonstrate how the Dutch Song Database facilitates and enables musicological research by presenting its contents and search functionalities in a number of exemplary cases. We discuss difficulties and impediments that were encountered during the development of the database, and we sketch a future prospect for further development in the European context. [ABSTRACT FROM AUTHOR]

Information about manuscripts and printed music indexed in RISM (Répertoire International des Sources Musicales), a large, international project that records and describes musical sources, was for decades available solely through book publications, CD-ROMs, or subscription services. Recent initiatives to make the data available on a wider scale have resulted in, most significantly, a freely accessible online database and the availability of its data as open data and linked open data. Previously, the task of increasing the amount of data was primarily carried out by RISM national groups and the Zentralredaktion (Central Office). The current opportunities available by linking to other freely accessible databases and importing data from other resources open new perspectives and prospects. This paper describes the RISM data and their applications for digital libraries and digital musicological projects. We discuss the possibilities and challenges in making available a large and growing quantity of data and how the data have been utilized in external library and musicological projects. Interactive functions in the RISM OPAC are planned for the future, as is closer collaboration with the projects that use RISM data. Ultimately, RISM would like to arrange a "take and give" system in which the RISM data are used in external projects, enhanced by the project participants, and then delivered back to the RISM Zentralredaktion. [ABSTRACT FROM AUTHOR]

The need for academic researchers to retrieve patents and research papers is increasing, because applying for patents is now considered an important research activity. However, retrieving patents using keywords is a laborious task for researchers, because the terms used in patents for the purpose of enlarging the scope of the claims are generally more abstract than those used in research papers. Therefore, we have constructed a framework that facilitates patent retrieval for researchers, and have integrated research papers and patents by analysing the citation relationships between them. We obtained cited research papers in patents using two steps: (1) detection of sentences containing bibliographic information, and (2) extraction of bibliographic information from those sentences. To investigate the effectiveness of our method, we conducted two experiments. In the experiment involving Step 1, we prepared 42,073 sentences, among which a human subject manually identified 1,476 sentences containing citations of papers. For Step 2, we prepared 3,000 sentences, in which the titles, authors, and other bibliographic information were manually identified. We obtained a precision of 91.6%, and a recall of 86.9% in Step 1, and a precision of 86.2% and a recall of 85.1% in Step 2. Finally, we constructed an information retrieval system that provided two methods of retrieving research papers and patents. One method was retrieval by query, and another was from the citation relationships between research papers and patents. [ABSTRACT FROM AUTHOR]

When scientists are searching for information, they generally have a precise objective in mind. Instead of looking for documents “about a topic T”, they try to answer specific questions such as finding the definition of a concept, finding results for a particular problem, checking whether an idea has already been tested, or comparing the scientific conclusions of two articles. Answering these precise or complex queries on a corpus of scientific documents requires precise modelling of the full content of the documents. In particular, each document element must be characterised by its discourse type (hypothesis, definition, result, method, etc.). In this paper, we present a scientific document model (SciAnnotDoc ontology), developed from an empirical study conducted with scientists, that models the discourse types. We developed an automated process that analyses documents effectively identifying the discourse types of each element. Using syntactic rules (patterns), we evaluated the process output in terms of precision and recall using a previously annotated corpus in Gender Studies. We chose to annotate documents in Humanities, as these documents are well known to be less formalised than those in “hard science”. The process output has been used to create a SciAnnotDoc representation of the corpus on top of which we built a faceted search interface. Experiments with users show that searches using with this interface clearly outperform standard keyword searches for precise or complex queries. [ABSTRACT FROM AUTHOR]

The aggregation of heterogeneous data from different institutions in cultural heritage and e-science has the potential to create rich data resources useful for a range of different purposes, from research to education and public interests. In this paper, we present the X3ML framework, a framework for information integration that handles effectively and efficiently the steps involved in schema mapping, uniform resource identifier (URI) definition and generation, data transformation, provision and aggregation. The framework is based on the X3ML mapping definition language for describing both schema mappings and URI generation policies and has a lot of advantages when compared with other relevant frameworks. We describe the architecture of the framework as well as details on the various available components. Usability aspects are discussed and performance metrics are demonstrated. The high impact of our work is verified via the increasing number of international projects that adopt and use this framework. [ABSTRACT FROM AUTHOR]

Decisions in agriculture are increasingly data-driven. However, valuable agricultural knowledge is often locked away in free-text reports, manuals and journal articles. Specialised search systems are needed that can mine agricultural information to provide relevant answers to users’ questions. This paper presents AgAsk—an agent able to answer natural language agriculture questions by mining scientific documents. We carefully survey and analyse farmers’ information needs. On the basis of these needs, we release an information retrieval test collection comprising real questions, a large collection of scientific documents split in passages, and ground truth relevance assessments indicating which passages are relevant to each question. We implement and evaluate a number of information retrieval models to answer farmers questions, including two state-of-the-art neural ranking models. We show that neural rankers are highly effective at matching passages to questions in this context. Finally, we propose a deployment architecture for AgAsk that includes a client based on the Telegram messaging platform and retrieval model deployed on commodity hardware. The test collection we provide is intended to stimulate more research in methods to match natural language to answers in scientific documents. While the retrieval models were evaluated in the agriculture domain, they are generalisable and of interest to others working on similar problems. The test collection is available at: https://github.com/ielab/agvaluate. [ABSTRACT FROM AUTHOR]

Despite ongoing advances in information retrieval algorithms, people continue to experience difficulties when conducting online searches within digital libraries. Because their information-seeking goals are often complex, searchers may experience difficulty in precisely describing what they are seeking. Current search interfaces provide limited support for navigating and exploring among the search results and helping searchers to more accurately describe what they are looking for. In this paper, we present a novel visual library search interface, designed with the goal of providing interactive support for common library search tasks and behaviours. This system takes advantage of the rich metadata available in academic collections and employs information visualization techniques to support search results evaluation, forward and backward citation exploration, and interactive query refinement. [ABSTRACT FROM AUTHOR]

This paper presents a framework for providing dynamically deployable services in ubiquitous computing settings. The goal of the framework is to provide people. places, and objects with computational functionalities to support and annotate them. Using RFID-based tracking systems, the framework detects the locations of physical entities, such as people or things, and deploys services bound to the entities at proper computing devices near where they are located. It enables location-based and personalized information services to be implemented as mobile agents and operated at stationary or mobile computing devices, which are at appropriate locations, even if the services do not have any location-information. This paper presents the rationale, design, implementation, and applications of our prototype infrastructure. [ABSTRACT FROM AUTHOR]

The problem of video classification can be viewed as discovering the signature patterns in the elemental features of a video class. In order to solve this problem, a large and diverse set of video features is proposed in this paper. The contributions of the paper further lie in dealing with high-dimensionality induced by the feature space and in presenting an algorithm based on two-phase grid searching for automatic parameter selection for support vector machine (SVM). The framework thus is directed to bridge the gap between low-level features and semantic video classes. The experimental results and comparison with state-of-the-art learning tools on more than 5000 video segments show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The ever-growing volumes of textual information from various sources have fostered the development of digital libraries, making digital content readily accessible but also easy for malicious users to plagiarize, thus giving rise tosecurityproblems. In this paper, we introduce aduplicate detectionscheme that is able to determine, with a particularly high accuracy, the degree to which one document is similar to another. Our pairwise document comparison scheme detects the resemblance between the content of documents by considering document chunks, representing contexts of words selected from the text. The resulting duplicate detection technique presents a good level of security in the protection of intellectual property while improving the availability of the data stored in the digital library and the correctness of the search results. Finally, the paper addresses efficiency and scalability issues by introducing new data reduction techniques. [ABSTRACT FROM AUTHOR]

Web archives have become a significant repository of our recent history and cultural heritage. Archival integrity and accuracy is a precondition for future cultural research. Currently, there are no quantitative or content-based tools that allow archivists to judge the quality of the Web archive captures. In this paper, we address the problems of detecting when a particular page in a Web archive collection has gone off-topic relative to its first archived copy. We do not delete off-topic pages (they remain part of the collection), but they are flagged as off-topic so they can be excluded for consideration for downstream services, such as collection summarization and thumbnail generation. We propose different methods (cosine similarity, Jaccard similarity, intersection of the 20 most frequent terms, Web-based kernel function, and the change in size using the number of words and content length) to detect when a page has gone off-topic. Those predicted off-topic pages will be presented to the collection's curator for possible elimination from the collection or cessation of crawling. We created a gold standard data set from three Archive-It collections to evaluate the proposed methods at different thresholds. We found that combining cosine similarity at threshold 0.10 and change in size using word count at threshold −0.85 performs the best with accuracy = 0.987, $$F_{1}$$ score = 0.906, and AUC $$=$$ 0.968. We evaluated the performance of the proposed method on several Archive-It collections. The average precision of detecting off-topic pages in the collections is 0.89. [ABSTRACT FROM AUTHOR]

In a database, a similar information search means finding data records which contain the majority of search keywords. Due to the rapid accumulation of information nowadays, the size of databases has increased dramatically. An efficient information searching scheme can speed up information searching and retrieve all relevant records. This paper proposes a Hilbert curve-based similarity searching scheme (HCS). HCS considers a database to be a multidimensional space and each data record to be a point in the multidimensional space. By using a Hilbert space filling curve, each point is projected from a high-dimensional space to a low-dimensional space, so that the points close to each other in the high-dimensional space are gathered together in the low-dimensional space. Because the database is divided into many clusters of close points, a query is mapped to a certain cluster instead of searching the entire database. Experimental results prove that HCS dramatically reduces the search time latency and exhibits high effectiveness in retrieving similar information. [ABSTRACT FROM AUTHOR]

Web archives attempt to preserve the fast changing web, yet they will always be incomplete. Due to restrictions in crawling depth, crawling frequency, and restrictive selection policies, large parts of the Web are unarchived and, therefore, lost to posterity. In this paper, we propose an approach to uncover unarchived web pages and websites and to reconstruct different types of descriptions for these pages and sites, based on links and anchor text in the set of crawled pages. We experiment with this approach on the Dutch Web Archive and evaluate the usefulness of page and host-level representations of unarchived content. Our main findings are the following: First, the crawled web contains evidence of a remarkable number of unarchived pages and websites, potentially dramatically increasing the coverage of a Web archive. Second, the link and anchor text have a highly skewed distribution: popular pages such as home pages have more links pointing to them and more terms in the anchor text, but the richness tapers off quickly. Aggregating web page evidence to the host-level leads to significantly richer representations, but the distribution remains skewed. Third, the succinct representation is generally rich enough to uniquely identify pages on the unarchived web: in a known-item search setting we can retrieve unarchived web pages within the first ranks on average, with host-level representations leading to further improvement of the retrieval effectiveness for websites. [ABSTRACT FROM AUTHOR]

Cultural institutions and museums have realized that annotations contribute valuable metadata for search and retrieval, which in turn can increase the visibility of the digital items they expose via their digital library systems. By exploiting annotations created by others, visitors can discover content they would not have found otherwise, which implies that annotations must be accessible and processable for humans and machines. Currently, however, there exists no widely adopted annotation standard that goes beyond specific media types. Most institutions build their own in-house annotation solution and employ proprietary annotation models, which are not interoperable with those of other systems. As a result, annotation data are usually stored in closed data silos and visible and processable only within the scope of a certain annotation system. As the main contribution of this paper, we present the LEMO Annotation Framework. It (1) provides a uniform annotation model for multimedia contents and various types of annotations, (2) can address fragments of various content-types in a uniform, interoperable manner and (3) pulls annotations out of closed data silos and makes them available as interoperable, dereferencable Web resources. With the LEMO Annotation Framework annotations become part of the Web and can be processed, linked, and referenced by other services. This in turn leads to even higher visibility and increases the potential value of annotations. [ABSTRACT FROM AUTHOR]

Content-oriented XML retrieval systems support access to XML repositories by retrieving, in response to user queries, XML document components (XML elements) instead of whole documents. The retrieved XML elements should not only contain information relevant to the query, but also provide the right level of granularity. In INEX, the INitiative for the Evaluation of XML retrieval, a relevant element is defined to be at the right level of granularity if it is exhaustive and specific to the query. Specificity was specifically introduced to capture how focused an element is on the query (i.e., discusses no other irrelevant topics). To score XML elements according to how exhaustive and specific they are given a query, the content and logical structure of XML documents have been widely used. One source of evidence that has led to promising results with respect to retrieval effectiveness is element length. This work aims at examining a new source of evidence deriving from the semantic decomposition of XML documents. We consider that XML documents can be semantically decomposed through the application of a topic segmentation algorithm. Using the semantic decomposition and the logical structure of XML documents, we propose a new source of evidence, the number of topic shifts in an element, to reflect its relevance and more particularly its specificity. This paper has three research objectives. Firstly, we investigate the characteristics of XML elements reflected by their number of topic shifts. Secondly, we compare topic shifts to element length, by incorporating each of them as a feature in a retrieval setting and examining their effects in estimating the relevance of XML elements given a query. Finally, we use the number of topic shifts as evidence for capturing specificity to provide a focused access to XML repositories. [ABSTRACT FROM AUTHOR]

Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection. Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution copyright-protected images. [ABSTRACT FROM AUTHOR]

Methods for content-based similarity search are fundamental for managing large multimedia repositories, as they make it possible to conduct queries for similar content, and to organize the repositories into classes of similar objects. 3D objects are an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in 3D similarity search has arisen, and a growing number of competing algorithms for the retrieval of 3D objects have been proposed. The contributions of this paper are to survey a body of recently proposed methods for 3D similarity search, to organize them along a descriptor extraction process model, and to present an extensive experimental effectiveness and efficiency evaluation of these methods, using several 3D databases. [ABSTRACT FROM AUTHOR]

The design and implementation of Harbin Institute of Technology—Digital Music Library (HIT-DML) is presented in this paper. Firstly, a novel framework, a music data model, and a query language are proposed as the theoretical foundation of the library. Secondly, music computing algorithms used in the library for feature extracting and matching are described. In addition, indices are introduced for both mining themes of music objects and accelerating content-based information retrieval. Finally, experimental results on the indices and the current development of the library are provided. HIT-DML is distinguished by the following points. First, it is inherently based on database systems, and combines database technologies with multimedia technologies seamlessly. Musical data are structurally stored. Second, it has a solid theoretical foundation, from framework and data model to query language. Last, it can retrieve musical information based on content against different kinds of musical instruments. The indices used, also power the library. [ABSTRACT FROM AUTHOR]

This paper proposes an effective query-translation approach that enables a cross-language information retrieval (CLIR) service to be more easily supported in digital library systems that only contain monolingual content. A query-translation engine calledLiveTransis used to process the translation requests of cross-lingual queries from connected digital library systems. To automatically extract translations not covered by standard dictionaries, the engine is developed based on a novel integration of dictionary resources and Web mining approaches, including anchor-text and search-result methods. The engine exploits a broad range of multilingual Web resources used as live bilingual corpora to alleviate translation difficulties. It is shown to be particularly effective for extracting multilingual translation equivalents of query terms containing proper names or new terminology. The obtained results show the feasibility of and great potential for creating English-Chinese CLIR services in existing digital libraries and new applications in cross-language Web searching, although difficulties still remain that need to be investigated further. [ABSTRACT FROM AUTHOR]

Text retrieval systems require an index to allow efficient retrieval of documents at the cost of some storage overhead. This paper proposes a novel full-text indexing model for Chinese text retrieval based on the concept of adjacency matrix of directed graph. Using this indexing model, on one hand, retrieval systems need to keep only the indexing data, instead of the indexing data and the original text data as the traditional retrieval systems always do. On the other hand, occurrences of index term are identified by labels of the so-calleds-stringswhere the index term appears, rather than by its positions as in traditional indexing models. Consequently, system space cost as a whole can be reduced drastically while retrieval efficiency is maintained satisfactory. Experiments over several real-world Chinese text collections are carried out to demonstrate the effectiveness and efficiency of this model. In addition to Chinese, The proposed indexing model is also effective and efficient for text retrieval of other Oriental languages, such as Japanese and Korean. It is especially useful for digital library application areas where storage resource is very limited (e.g., e-books and CD-based text retrieval systems). [ABSTRACT FROM AUTHOR]

Searching a digital library is typically a tedious task. A system can improve information access by building on knowledge about a user acquired in a user profile in order to customize information access both in terms of the information returned in response to a query (query personalization) as well as in terms of the presentation of the results (presentation personalization). In this paper, we focus on query personalization in digital libraries; in particular, we address structured queries involving metadata stored in relational databases. We describe the specification of user preferences at the level of a user profile and the process of query personalization with the use of query-rewriting rules. [ABSTRACT FROM AUTHOR]

Knowledge and libraries – this conceptual symbiosis is as old as the idea of libraries itself: libraries collect artifacts of explicated knowledge and make them available for building up new knowledge in all areas of everyday life. Learning and qualification management support are further essential links in the knowledge supply chain, which starts at the expert knowledge of an author and ends at the targeted enhancement of another person’s individual knowledge. Digital libraries and e-learning systems are thus important enabling technologies for the knowledge supply chain in the digital age. The e-Qualification project presented in this paper combines digital library, e-learning, and authoring support into an adaptable qualification management framework that provides integrated support and mediation for the various stakeholders involved in the qualification management process. [ABSTRACT FROM AUTHOR]

Conventional full-text systems represent documents as sets of index terms, and queries to these systems often retrieve irrelevant material when search terms occur in inappropriate contexts. We have developed document representations that capture the semantic contexts in which text words occur. Many bodies of literature contain stereotypic categories of information. For example, articles describing medical research consistently discuss interventions and outcomes. These semantic themes provide context for terms in the text, and thus, can facilitate precise full-text searches. We have used a contextual model of clinical research articles, case reports, and review articles as the basis for a document representation in a full-text retrieval system. In this paper, we describe the creation of context models for medical publications and the evaluation of these models using interindexer consistency. We demonstrate that such models are easily understood and employed by readers of the literature (and thus, the searchers). Accordingly, these models may constitute a powerful representation for information retrieval. We discuss the suitability of this technique for other domains. [ABSTRACT FROM AUTHOR]

Data of interest to biomedical researchers associated with the Human Genome Project (HGP) is stored all over the world in a number of different electronic data formats and accessible through a variety of interfaces and retrieval languages. These data sources include conventional relational databases with SQL interfaces, formatted text files on top of which indexing is provided for efficient retrieval (ASN.1-Entrez), and binary files that can be interpreted textually or graphically via special purpose interfaces (ACeDB). Researchers within the HGP want to combine data from these different data sources, add value through sophisticated data analysis techniques (such as the biosequence comparison software BLAST and FASTA), and view it using special purpose scientific visualization tools. However, currently there are no commercial tools for enabling such an integrated digital library, and a fundamental barrier to developing such tools appears to be one of language design and optimization: The data formats and software packages found throughout the HGP contain a number of data types not available in conventional databases, such as lists, variants and arrays; furthermore, these types may be deeply nested. We present in this paper a framework for providing read access to multiple data sources with complex structured data, and illustrate its use in an application called BioKleisli which accesses data sources critical to the HGP. The three primary components of this framework are: (1) a powerful language for querying and transforming complex structured data; (2) an extensible architecture for implementing the query primitives; and (3) optimization techniques that extend many known techniques to these more complex data types. [ABSTRACT FROM AUTHOR]

In the context of collaborative eScience, digital libraries are one of many distributed, interoperable resources available to scientists that facilitate both human and machine collaboration: machine collaboration in the form of standards such as the Open Archives Initiative Protocol for Metadata Harvesting and human collaboration in the form of collaborative workspaces. This paper describes a set of collaborative workspaces created at the Los Alamos National Laboratory Research Library, initial patterns of use, and additional user requirements determined based on these initial patterns of use. [ABSTRACT FROM AUTHOR]

Abstract. This paper investigates load balancing strategies for clustered Alexandria Digital Library (ADL) servers. The ADL system, which provides online information searching and browsing of' spatially-referenced materials through the World Wide Web, involves intensive database I/O and heterogeneous CPU activities. Clustering servers can improve the scalability of the ADL system in response to a large number of simultaneous access requests. One difficulty addressed is that clustered workstation nodes may be non-uniform in terms of CPU and I/O speeds. An optimization scheme is proposed in this paper to dynamically monitor the resource availability, use a low-cost communication strategy for updating load information among nodes, and schedule requests based on both I/O and computation load indices. Since the accurate cost estimation for processing database-searching requests is difficult, a sampling and prediction scheme is used to identify the relative efficiency of nodes for satisfying I/O and CPU demands of these requests. A set of experiments using the ADL traces have been conducted to verify the effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]

Abstract. In this paper, we investigate the issues of performance management in large scale, autonomous and federated digital library systems, performing the tasks of indexing, searching and retrieval of information objects. We have defined a management architecture and performance framework for measuring and monitoring the behavior of digital libraries as they operate. Our architecture and mechanisms are easily applicable to other digital library systems of similar flavor and architecture. We implemented this architecture over a testbed of Dienst servers using real data and workload from the operational NCSTRL system. We have defined the relevant parameters for investigating the performance of the servers and have developed visualization tools to monitor the parameters. In addition, our performance framework provides mechanisms for load-balancing search requests in a network of digital library servers. We have demonstrated this by building a testbed for investigating a few novel load balancing policies. Given that network delays and outages are unpredictable over the Internet, we have developed new adaptive mechanisms to detect timeouts and provide quality of service. [ABSTRACT FROM AUTHOR]

Abstract. We present an open and extensible architecture, ImAge, for content-based image retrieval in a distributed environment. The architecture proposes the use of system components with standard public interfaces for implementing retrieval functionality. The standardization of the components and their encapsulation in autonomous software agents result in functional stratification and easy extensibility. Collaboration of the independent retrieval resources in ImAge results in enhanced system capability. Reuse of existing retrieval resources is achieved by encapsulating thrum in agents with standard interfaces. The addition of independent agents with domain knowledege adds the capability of processing conceptual queries, while reusing the existing system components for feature-based retrieval. A communication protocol allows the declaration of the capabilities of the system components and negotiations for optimal resource selection for solving a retrieval problem. The use of mobile agents alleviates network bottlenecks. This paper describes a prototype implementation that validates the architecture. [ABSTRACT FROM AUTHOR]