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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]

Millions of new research papers are published each year, making it extremely difficult for researchers to find out what they really want. Existing paper recommendation algorithms cannot effectively address the recommendation of newly published papers due to lack of historical information(e.g., citation information; view log), the so-called cold start problem. Furthermore, in most of these studies, papers are considered in homogeneous or bipartite networks. However, in a real bibliographic network, there are multiple types of objects (e.g., researchers, papers, venues, topics) and multiple types of links among these objects. In this paper, we study the problem of new paper recommendation in the heterogeneous bibliographic network, and a new method called HIPRec, i.e., meta-graph based recommendation model, is proposed to solve this problem. First, the top-K most interesting meta-paths are selected based on the training data. Secondly, a greedy method is proposed to select the most significant meta-graphs generated by merging the meta-paths, which can describe more sophisticated semantics between researchers and papers than simple meta-paths. In the meantime, meta-path and meta-graph based topological features are systematically extracted from the network. Lastly, a supervised model is used to learn the best weights associated with different topological features in deciding the researcher-new paper recommendations. We present experiments on a real bibliographic network, the DBLP network, which show the effectiveness of our approach compared to state-of-the-art new paper recommendation methods. [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]

Proper citation is of great importance in academic writing for it enables knowledge accumulation and maintains academic integrity. However, citing properly is not an easy task. For published scientific entities, the ever-growing academic publications and over-familiarity of terms easily lead to missing citations. To deal with this situation, we design a special method Citation Recommendation for Published Scientific Entity (CRPSE) based on the cooccurrences between published scientific entities and in-text citations in the same sentences from previous researchers. Experimental outcomes show the effectiveness of our method in recommending the source papers for published scientific entities. We further conduct a statistical analysis on missing citations among papers published in prestigious computer science conferences in 2020. In the 12,278 papers collected, 475 published scientific entities of computer science and mathematics are found to have missing citations. Many entities mentioned without citations are found to be well-accepted research results. On a median basis, the papers proposing these published scientific entities with missing citations were published 8 years ago, which can be considered the time frame for a published scientific entity to develop into a well-accepted concept. For published scientific entities, we appeal for accurate and full citation of their source papers as required by academic standards. [ABSTRACT FROM AUTHOR]

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search. [ABSTRACT FROM AUTHOR]

Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images and compared with geo-referenced databases. Location information is then inferred from the matching results. Conventional schemes mainly use low-level visual features. These approaches offer good accuracy but suffer from scalability issues. In order to assist localization in large urban areas, this work explores a different path by utilizing high-level semantic information. It is found that object information in a street view can facilitate localization. A novel descriptor scheme called "semantic signature" is proposed to summarize this information. A semantic signature consists of type and angle information of visible objects at a spatial location. Several metrics and protocols are proposed for signature comparison and retrieval. They illustrate different trade-offs between accuracy and complexity. Extensive simulation results confirm the potential of the proposed scheme in large-scale applications. This paper is an extended version of a conference paper in CBMI'18. A more efficient retrieval protocol is presented with additional experiment results. [ABSTRACT FROM AUTHOR]

This white paper explores the considerations of standards-based interoperability of medical images between organizations, patients, and providers. In this paper, we will look at three different standards-based image exchange implementations that have been deployed to facilitate exchange of images between provider organizations. The paper will describe how each implementation uses applicable technology and standards; the image types that are included; and the governance policies that define participation, access, and trust. Limitations of the solution or non-standard approaches to solve challenges will also be identified. Much can be learned from successes elsewhere, and those learnings will point to recommendations of best practices to facilitate the adoption of image exchange. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to update the review of Bornmann and Daniel (J Doc 64(1):45–80, 2008) presenting a narrative review of studies on citations in scientific documents. The current review covers 41 studies published between 2006 and 2018. Bornmann and Daniel (2008) focused on earlier years. The current review describes the (new) studies on citation content and context analyses as well as the studies that explore the citation motivation of scholars through surveys or interviews. One focus in this paper is on the technical developments in the last decade, such as the richer meta-data available and machine-readable formats of scientific papers. These developments have resulted in citation context analyses of large datasets in comprehensive studies (which was not possible previously). Many studies in recent years have used computational and machine learning techniques to determine citation functions and polarities, some of which have attempted to overcome the methodological weaknesses of previous studies. The automated recognition of citation functions seems to have the potential to greatly enhance citation indices and information retrieval capabilities. Our review of the empirical studies demonstrates that a paper may be cited for very different scientific and non-scientific reasons. This result accords with the finding by Bornmann and Daniel (2008). The current review also shows that to better understand the relationship between citing and cited documents, a variety of features should be analyzed, primarily the citation context, the semantics and linguistic patterns in citations, citation locations within the citing document, and citation polarity (negative, neutral, positive). [ABSTRACT FROM AUTHOR]

Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval. [ABSTRACT FROM AUTHOR]

Mobile devices have been used as tools for navigation and geographic information retrieval with some success. However, screen size, glare, and the cognitive demands of the interface are often cited as weaknesses when compared with traditional tools such as paper maps and guidebooks. In this paper, a simple mixed media approach is presented which tries to address some of these concerns by combining paper maps with electronic guide resources. Information about a landmark or region is accessed by waving a handheld computer equipped with an radio frequency identification (RFID) reader above the region of interest on a paper map. We discuss our prototyping efforts, including lessons learned about using RFID for mixed media interfaces. We then present and discuss evaluations conducted in the field and in a comparative, exploratory study. Results indicate that the method is promising for tourism and other activities requiring mobile, geographically-related information access. [ABSTRACT FROM AUTHOR]

It has been argued that reporting software engineering experiments in a standardized way helps researchers find relevant information, understand how experiments were conducted and assess the validity of their results. Various guidelines have been proposed specifically for software engineering experiments. The benefits of such guidelines have often been emphasized, but the actual uptake and practice of reporting have not yet been investigated since the introduction of many of the more recent guidelines. In this research, we utilize a mixed-method study design including sequence analysis techniques for evaluating to which extent papers follow such guidelines. Our study focuses on the four most prominent software engineering journals and the time period from 2000 to 2020. Our results show that many experimental papers miss information suggested by guidelines, that no de facto standard sequence for reporting exists, and that many papers do not cite any guidelines. We discuss these findings and implications for the discipline of experimental software engineering focusing on the review process and the potential to refine and extend guidelines, among others, to account for theory explicitly. [ABSTRACT FROM AUTHOR]

The present methods of data preservation and representation for barrel finishing processes which include paper and electronic documents have several disadvantages such as restrictions in size and complexity, and limitations on query and updation speed. Aiming at these disadvantages, a new database platform for barrel finishing data has been constructed by using database technology and case-based reasoning. The design procedure of the database platform is expounded in detail, covering analysis of database platform requirements, establishment for conceptual model of database data structure, designs for logical model of database data structure, determination for physical model of database data structure, choice for network structure of database platform, data management and storage method. The application results demonstrate that the database platform can ensure the safe and convenient storage as well as the sharing of experimental data of the barrel finishing process. It can also provide guidance and technical information for scientific researchers, experts, technicians, and production site operators to choose the processing technology and the processing parameters reasonably. [ABSTRACT FROM AUTHOR]

Author name disambiguation is an important research topic in the academic information retrieval community. Existing methods rely either on feature engineering on rich attributes information or on relationship information to obtain documents' similarity, but seldom consider the complementarity and the correlation between them. The feature engineering on attributes, especially on rich text information, could capture the global semantic concepts, while the relationship information could encode local structural proximity in multiple academic networks. To bridge the gap between semantic and relationship information in author name disambiguation, this paper presents a joint representation learning approach, which could encode both semantic and relationship information into a common low dimensional space. Specifically, the proposed method consists of four modules: (1) semantic embedding module; (2) relationship embedding module; (3) semantic and relationship joint embedding module; and (4) clustering module. Experimental results demonstrate that the proposed joint representation learning approach consistently outperforms the state-of-the-art methods on three benchmarks. [ABSTRACT FROM AUTHOR]

This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline. [ABSTRACT FROM AUTHOR]

This paper studies binary reverse multi-delimiter (RMD) data compression codes. RMD codes have a range of useful properties, such as unique decodability, completeness, universality, synchronizability, recognition using a finite automaton, and the ability for rapid data retrieval within an encoded file. The authors have constructed a simple monotonic mapping from the set of non-negative integers to the codeword set. Based on this mapping, they have developed a fast byte-aligned decoding algorithm. Computer experiments demonstrate that we can decode RMD codes nearly as quickly as the SCDC code and several times faster than the Fibonacci code. Compared to known codes of a similar type, RMD codes exhibit a better compression ratio for natural language texts (more than four times closer to the entropy bound than SCDC). Additionally, the paper describes a technology for preprocessing natural language texts, which, combined with encoding using RMD codes, enhances the efficiency of powerful modern archivers. [ABSTRACT FROM AUTHOR]

Textual Semantic Similarity is a crucial part of text matching tasks, and it has a very wide range of applications in natural language processing (NLP) tasks such as search engines, question-answering systems, information retrieval, natural language inference. Although there are a variety of approaches about textual semantic similarity, many do not succeed in achieving the semantic representation of a sentence or text that represents it well, and ignore that different words serve different roles in expressing the meaning of the whole sentence in different degrees. Therefore, our paper proposes a Siamese Bert network model to obtain textual semantic similarity. Firstly, we utilize the Bert network model to obtain the semantic features of each word in the sentence as input and utilize the merit of the Siamese network, reducing the training parameters, sharing the same encoder and feature weight information with each other. Then we use the attention mechanism to obtain more advanced semantic features. Furthermore, the similarity between two sentences can be derived by the methods of calculating the distance or concatenating their high-level semantic representations. In this paper, we apply the network structure to three related semantic similarity datasets, which perform better than other approaches. [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]

Recommender systems have been used since the beginning of the Web to assist users with personalized suggestions related to past preferences for items or products including books, movies, images, research papers and web pages. The availability of millions research articles on various digital libraries makes it difficult for a researcher to find relevant articles to his/er research. During the last years, a lot of research have been conducted through models and algorithms that personalize papers recommendations. With this survey, we explore the state-of-the-art citation recommendation models which we categorize using the following seven criteria: platform used, data factors/features, data representation methods, methodologies and models, recommendation types, problems addressed, and personalization. In addition, we present a novel k-partite graph-based taxonomy that examines the relationships among surveyed algorithms and corresponding k-partite graphs used. Moreover, we present (a) domain's popular issues, (b) adopted metrics, and (c) commonly used datasets. Finally, we provide some research trends and future directions. [ABSTRACT FROM AUTHOR]

To illustrate the availability, effectiveness, and practical use of Open-Source tools in developing a radiology paper from its beginning to its presentation and publication. Practical use of a complete set of Open-Source applications for writing, e-mail corresponding, slide show, image retrieval, and manipulation is shown by simulating a scientific paper development. Open-Source software proved to be an inexpensive, effective, widely compatible, and user-friendly alternative to commercial toolkits in developing and deploying a scientific paper, either on paper or on slide show. [ABSTRACT FROM AUTHOR]

Textpresso is a text-mining system for scientific literature. Its two major features are access to the full text of research papers and the development and use of categories of biological concepts as well as categories that describe or relate objects. A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature. Here we describe Textpresso for Neuroscience, part of the core Neuroscience Information Framework (NIF). The Textpresso site currently consists of 67,500 full text papers and 131,300 abstracts. We show that using categories in literature can make a pure keyword query more refined and meaningful. We also show how semantic queries can be formulated with categories only. We explain the build and content of the database and describe the main features of the web pages and the advanced search options. We also give detailed illustrations of the web service developed to provide programmatic access to Textpresso. This web service is used by the NIF interface to access Textpresso. The standalone website of Textpresso for Neuroscience can be accessed at . [ABSTRACT FROM AUTHOR]

A growing number of researchers are exploring the use of citation relationships such as direct citation, bibliographic coupling, and co-citation for information retrieval in scientific databases and digital libraries. In this paper, I propose a method of ranking the relevance of citation-based search results to a set of key, or seed, papers by measuring the number of citation relationships they share with those key papers. I tested the method against 23 published systematic reviews and found that the method retrieved 87% of the studies included in these reviews. The relevance ranking approach identified a subset of the citation search results that comprised 27% of the total documents retrieved by the method, and 7% of the documents retrieved by these reviews, but that contained 75% of the studies included in these reviews. Additional testing suggested that the method may be less appropriate for reviews that combine literature in ways that are not reflected in the literature itself. These results suggest that this ranking method could be useful in a range of information retrieval contexts. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) is used in every domain, especially in the healthcare system it plays a very important role in our life and made our life easier by providing various facilities in every area. On the Internet of Medical Things (IoMT), IoT devices collect real-time data from patients and send it to the cloud through the Internet. These gathered data are extorted to centralized processing and computational power. Centralization can lead to a single point of failure, data modification, snooping, security, and privacy evasion. Blockchain can resolve such issues by providing decentralized computational power and better storage capacity for IoT data. Therefore, integrating IoT and blockchain technologies can be an intelligent way to implement a decentralized IoT-based smart healthcare system. We find that integration of the blockchain and IoT is persuasive and can cause remarkable transformations across the healthcare system. We discussed blockchain and its applicability in the healthcare industry. The paper explores blockchain's dimensions, making the innovative healthcare system more secure and stable. We provide a comparative analysis of well-known recent research on IoT-based smart healthcare system security using blockchain based on different parameters such as architecture, data integrity, medical information sharing, distributed electronic health records, patient encryption key, access control, hardware implementation, etc. The paper focuses on the current security issues in IoT-based smart healthcare systems, architectures, taxonomy, and research gap and their possible solutions we point out various research challenges that should be considered before the deployment of a blockchain network in an IoT setting. This paper will be helpful to new researchers to get an overview of IoT-based healthcare system security. [ABSTRACT FROM AUTHOR]

The content retrieval systems aim to retrieve images similar to a query image from a large data set. A feature extractor and similarity measure play a key role in these systems. Hand-crafted feature descriptors like SURF, SIFT, and GIST find a suitable pattern for measuring the similarity between images. Recently deep learning in this field has been given much attention, which performs feature extraction and similarity learning simultaneously. Various research shows that the feature vector extracted from pre-trained networks contains richer information than class labels in classifying or retrieving information. This paper presents an effective method, Deep Muti-teacher Transfer Hash (DMTH), which uses knowledge from several complex models to teach a simple one. Due to the variety of available pre-trained models and the diversity among their extracted features, we utilize an attention mechanism to obtain richer features from them to teach a simple model via an appropriate knowledge distillation loss. We test our method on widely used datasets Cifar10 & Cifar100 and compare our method with other state-of-the-art methods. The experimental results show that DMTH can improve the image retrieval performance by learning better features obtained through an attention mechanism from multiple teachers without increasing evaluation time. Specifically, the proposed multi-teacher model surpasses the best individual teacher by 2% in terms of accuracy on Cifar10. Meanwhile, it boosts the performance of the student model by more than 4% using our knowledge transfer mechanism. [ABSTRACT FROM AUTHOR]

Document relational network has been effective in retrieving and evaluating papers. Despite their effectiveness, relational measures, including co-citation, are far from ideal and need improvements. The assumption underlying the co-citation relation is the content relevance and opinion relatedness of cited and citing papers. This may imply existence of some kind of co-opinionatedness between co-cited papers which may be effective in improving the measure. Therefore, the present study tries to test the existence of this phenomenon and its role in improving information retrieval. To do so, based on CITREC, a medical test collection was developed consisting of 30 queries (seed documents) and 4823 of their co-cited papers. Using NLP techniques, the co-citances of the queries and their co-cited papers were analyzed and their similarities were computed by 4 g similarity measure. Opinion scores were extracted from co-citances using SentiWordnet. Also, nDCG values were calculated and then compared in terms of the citation proximity index (CPI) and co-citedness measures before and after being normalized by the co-opinionatedness measure. The reliability of the test collection was measured by generalizability theory. The findings suggested that a majority of the co-citations exhibited a high level of co-opinionatedness in that they were mostly similar either in their opinion strengths or in their polarities. Although anti-polar co-citations were not trivial in their number, a significantly higher number of the co-citations were co-polar, with a majority being positive. The evaluation of the normalization of the CPI and co-citedness by the co-opinionatedness indicated a generally significant improvement in retrieval effectiveness. While anti-polar similarity reduced the effectiveness of the measure, the co-polar similarity proved to be effective in improving the co-citedness. Consequently, the co-opinionatedness can be presented as a new document relation and used as a normalization factor to improve retrieval performance and research evaluation. [ABSTRACT FROM AUTHOR]

The medical industry is constantly improving its own structure with the development of society. However, most of the current drug management systems cannot meet the needs of actual drug management. There are many problems such as incomplete system functions, confusion of drug management, unclear division of modules, loss and waste of human resources. At present, hospitals need a new and perfect hospital drug information management. Drug management is an indispensable part of the hospital management system. This paper completes the design of the target drug rapid retrieval system, which is realized multidimensional data analysis technology, and tests the multidimensional data analysis algorithm model used. The improved multidimensional data analysis algorithm greatly improves the accuracy. The design and improvement of the system can effectively improve the drug processing efficiency of existing pharmacies through the design and simulation experiments, enable the pharmacy department to better cooperate with other departments to work, make the cooperation between different departments more effective, and solve the work efficiency problems of the hospital. By introducing multidimensional data analysis technology into the field of drug information retrieval, this paper designs an effective and fast retrieval method. [ABSTRACT FROM AUTHOR]

A key task in the fact-checking workflow is to establish whether the claim under investigation has already been debunked or fact-checked before. This is essentially a retrieval task where a misinformation claim is used as a query to retrieve from a corpus of debunks. Prior debunk retrieval methods have typically been trained on annotated pairs of misinformation claims and debunks. The novelty of this paper is an Unsupervised Method for Training Debunked-Narrative Retrieval Models (UTDRM) in a zero-shot setting, eliminating the need for human-annotated pairs. This approach leverages fact-checking articles for the generation of synthetic claims and employs a neural retrieval model for training. Our experiments show that UTDRM tends to match or exceed the performance of state-of-the-art methods on seven datasets, which demonstrates its effectiveness and broad applicability. The paper also analyses the impact of various factors on UTDRM's performance, such as the quantity of fact-checking articles utilised, the number of synthetically generated claims employed, the proposed entity inoculation method, and the usage of large language models for retrieval. [ABSTRACT FROM AUTHOR]

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]

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention and control of crimes associated with COVID-19 are critical for controlling the pandemic. Therefore, to provide efficient and convenient intelligent legal knowledge services during the pandemic, we develop an intelligent system for legal information retrieval on the WeChat platform in this paper. The data source we used for training our system is "The typical cases of national procuratorial authorities handling crimes against the prevention and control of the new coronary pneumonia pandemic following the law", which is published online by the Supreme People's Procuratorate of the People's Republic of China. We base our system on convolutional neural network and use the semantic matching mechanism to capture inter-sentence relationship information and make a prediction. Moreover, we introduce an auxiliary learning process to help the network better distinguish the relation between two sentences. Finally, the system uses the trained model to identify the information entered by a user and responds to the user with a reference case similar to the query case and gives the reference legal gist applicable to the query case. [ABSTRACT FROM AUTHOR]

Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover "thematic topics" from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods. [ABSTRACT FROM AUTHOR]

The role of formulating a well-defined query in the retrieval of relevant search results is well-known to the users of an Information Retrieval (IR) system. Researchers have experimented with various approaches to formulating a search query, e.g., using topic fields in different combinations, reducing verbose queries, and query expansion to name a few. These approaches have been tested in various domain-specific IR applications. However, to the best of our knowledge, no survey or review article reviews the current state of query formulation in the domain of book search. This paper fills this gap in the literature by reviewing research publications on query formulation, published during 2007–2022. These publications have been selected through a rigorous search and selection strategy, where the findings have been summarized using a well-defined theoretical framework. It identifies the current trends and presents a cross-comparison for identifying the best-performing methods. The paper has implications for researchers working in IR in general and book search in specific. [ABSTRACT FROM AUTHOR]

To realize the great flexibility and cost savings for providing location-based service, data owners are incentivized to migrate their data to cloud servers. However, direct data outsourcing to untrusted servers may pose significant privacy risks. This paper proposes a practical privacy-preserving nearest neighbor searching method over encrypted spatial data. We simultaneously protect data and location privacy (access and pattern privacy) by encrypting data using asymmetric scalar-product-preserving encryption (ASPE) and performing computational private information retrieval (CPIR) on encrypted subspace datasets. To mitigate the performance degradation introduced by the combination of ASPE and CPIR, we propose a hierarchical index that enables users to safely obtain encrypted subspace datasets with configurable privacy, where different degrees of privacy can be traded off against query processing performance. Experiments demonstrate that our method outperforms the state-of-the-art method in efficiency while allowing for a flexible trade-off between performance and privacy. [ABSTRACT FROM AUTHOR]

This article discusses Internet Web Information Retrieval, which has reportedly encouraged both researchers and commercial business enterprises to utilize the network. The article suggests that use of the Internet for Web Information Retrieval has been supported by theoretical results, methods, experiments, and implementations. Manuscripts in the periodical are also discussed, which were selected from all submissions to be included in this special issue. Five papers were chosen, which deal with the functionality of Web searches, Web search users, topic distillation, link analysis, and performance.

Citation analysis-based systems are premised on assuming that all citations are equally important. The scientific community argues that a citation may hold divergent reasons and thus, should not be treated at par. In this regard, a plethora of existing studies classifies citations for varying reasons. Presently, the community has a propensity toward binary citation classification with the notion of contemplating only important reasons while employing quantitative analysis-based measures. We argue that outcomes yielded by the contemporary state-of-the-art models cannot be deemed ideal as the plethora of them has been evaluated on a data set with minimal number of instances due to which the outcomes cannot be generalized. The scope of results from such approaches is restricted to a single domain only which may exhibit entirely different behavior for the different data sets. Most of the studies are ruled by the content based features evaluated by harnessing traditional classification models like Support Vector Machine (SVM), and random forest (RF), while an inconsiderable number of studies employ metadata which holds the potential to serve as a quintessential indicator to tackle meaningful citations. In this study, we introduce Multilayer perceptron artificial neural network (MLP-ANN) binary citation classifier, which exploits the best combinations of features formed using both sources. We also introduce a new benchmark data set from the electrical engineering domain which is consolidated with two existing benchmark data sets for model evaluation. The outcomes reveal that the results produced by the proposed MLP model outperform the contemporary models achieving a precision of 0.92. [ABSTRACT FROM AUTHOR]

Scientists face challenges when finding datasets related to their research problems due to the limitations of current dataset search engines. Existing tools for searching research datasets rely on publication content or metadata, do not considering the data contained in the publication in the form of tables. Moreover, scientists require more elaborate inputs and functionalities to retrieve different parts of an article, such as data presented in tables, based on their search purposes. Therefore, this paper proposes a novel approach to retrieve relevant tabular datasets from publications. The input of our system is a research problem stated as an abstract from a scientific paper, and the output is a set of relevant tables from publications that are related to the research problem. This approach aims to provide a better solution for scientists to find useful datasets that support them in addressing their research problems. To validate this approach, experiments were conducted using word embedding from different language models to calculate the semantic similarity between abstracts and tables. The results showed that contextual models significantly outperformed non-contextual models, especially when pre-trained with scientific data. Furthermore, the importance of context was found to be crucial for improving the results. [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]

In the past decades, ontology-based query expansion has been studied to improve health and biomedical information retrieval by many researchers, but the results of previous works are inconsistent. Query expansion with domain ontologies could introduce noise that degrades the retrieval performance, therefore noise control is the key to its success. In this paper, we explore three noise control strategies for UMLS-based query expansion. The first strategy is the adoption of a word-phrase hybrid retrieval model, and the other two strategies explored are expansion term weighting and term filtering. All the three strategies are implemented based on the Indri search engine and evaluated on two standard datasets, OHSUMED and TREC Genomic Track 2006. The experimental results indicate that the word-phrase hybrid retrieval model is superior to the word-based model and the pure phrase-based model, and beneficial to not only baseline retrieval but also query expansion. Expansion term weighting is an effective strategy to suppress term noise and improve retrieval performance. And expansion term filtering can also give some positive effects in most cases but is not as effective as the other two strategies. By combining the three strategies together, the best retrieval performances can be achieved on both datasets. [ABSTRACT FROM AUTHOR]

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: (i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; (ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and (iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task. [ABSTRACT FROM AUTHOR]

Uncovering the public discourse on hydrogen energy is essential for understanding public behaviour and the evolving nature of conversations over time and across different regions. This paper presents a comprehensive analysis of a large multilingual dataset pertaining to hydrogen energy collected from Twitter spanning a decade (2013–2022) using selected keywords. The analysis aims to explore various aspects, including the temporal and spatial dimensions of the discourse, factors influencing Twitter engagement, user engagement patterns, and the interpretation of conversations through hashtags and ngrams. By delving into these aspects, this study offers valuable insights into the dynamics of public discourse surrounding hydrogen energy and the perceptions of social media users. [ABSTRACT FROM AUTHOR]

Due to the heterogeneity of program language and natural language query, it is difficult to identify the semantic relationship between them, which leads to the low efficiency of code recommendation. In order to solve the problems of the above code recommendation technology, a code recommendation method based on joint embedded attention network (JEAN) is proposed in this paper. The method uses GRU Network to embed code snippets and describe queries into vector representation, which solves the problem of heterogeneous code snippets and natural language queries. The Attention mechanism is then used to distribute totally different weights to different components of every mode of the code snippet. The reason for the Attention mechanism is that different components of every mode of the code snippet contribute differently to the semantic vector of the final code snippet, making it interpretable. Finally, two commonly used evaluation indexes of information retrieval, SuccessRate@k and MRR, are used for experimental comparison with other baseline models. The experimental results show that the code recommendation method based on joint embedded attention network proposed in this paper can effectively recommend appropriate code snippets according to the needs of developers, and its performance is better than other baseline methods. [ABSTRACT FROM AUTHOR]

Metaheuristic applications for information retrieval research are limited in spite of the importance of this problem domain. Ranking the retrieved documents based on their importance is a vital issue for the scientific and industrial communities. This paper proposes a novel variable neighborhood search (VNS) algorithm with adaptation based on an objective function for the learning to rank (LTR) problem. VNS is a global optimum metaheuristic algorithm that has been engaged to evolve the optimal solutions for heuristic problems based on exploring better neighbor solutions from the current one. The changes from the current to the next optimal solution are made during the perturbation stage to identify the global optimal solutions. The exploration procedure has been made through various mutation step sizes, whereas the exploitation process has been done by checking the quality of the evolved solutions using the fitness function. This research proposes a novel version of VNS based on four random probability distributions with gradient ascent. In addition to using the traditional random generator with gradient ascent for modifying the mutated genes of the neighborhood candidate solution in the following evolving iteration. This novel method in LTR is called gradient variable neighborhood (GVN). In the experiments, we utilized Microsoft Bing search (MSLR-WEB30K), Yahoo, and TREC Million Queries Competitions in 2008 and 2007 (LETOR 4) datasets. From the findings of the results, we can deduce that the GVN method outperformed recent studies on LTR methods. [ABSTRACT FROM AUTHOR]

On account of prevalence of mobile devices, a variety of mobile-based services have been conducted recently. In this situation, it is possible to serve the information that fits to users' requests according to the users' context using mobile devices. Therefore, it is necessary to research data mining techniques with mobile devices. Therefore, we researched information retrieval system in mobile environment. We applied semantic data mining method with context information ontology. These techniques of semantic information retrieval can provide the information according to users' context information such as location, weather, user preference etc. exactly and rapidly anytime and anyplace. In modern society, it is essential to develop mobile information retrieval system which is efficient to users. Also, the system can provide users' current location and the location information with Google map APIs in mobile devices. Therefore, in this paper, we researched semantic data mining technique with context information ontology using mobile techniques. And we compared and evaluated our proposed model and the existing model in three areas and showed our proposed model has higher performance. [ABSTRACT FROM AUTHOR]

Link prediction plays an important role in scientific collaboration networks, and can favourably affect the organization of international scientific projects. In this paper, a meta-path computed prediction (MPCP) algorithm for link prediction among scientists and publications is presented. The MPCP algorithm is based on a heterogeneous information network model composed of authors and keywords in articles retrieved from the Web of Science database. Two kinds of meta-paths are defined: Author to Author to Author (A-A-A) and Author to Direction to Author (A-D-A). By calculating A-A-A and A-D-A using the heterogeneous information network model, the predictive strength of the links can be computed. The overlap of the meta-paths is also taken into account. By restoring links and calculating the number of restored links with different standard values, similar results are achieved for (quantum communication and link prediction). The number of restored links decreases as a special threshold value increases. The experimental studies show that, for any threshold value up to 1, at least 50% of links are restored. The results presented in this paper verify that the algorithm is a feasible means of predicting collaboration among scientists. [ABSTRACT FROM AUTHOR]

It is intuitively clear that search for scientific publications often has many characteristics of exploratory search. The purpose of this paper is to formalize this intuitive understanding, explore which scientific search tasks can be classified as research search ones, what general approaches to the research search problem exist, and how they are implemented in specialized search engines for scientists. We overview the existing works that address the information-seeking behavior of scientists and a special variant of search called exploratory search. There are several types of search behavior typical for scientists; we show that most of them are exploratory ones. Exploratory search differs from information retrieval and requires special support from the search systems. We analyze seventeen search systems for academicians (from Google Scholar, Scopus, and Web of Science to ResearchGate) from the perspective of exploratory search support. We find that most of them do not go far beyond simple information retrieval, and there is room for further improvements, especially in collaborative search support. [ABSTRACT FROM AUTHOR]

Design inspiration comes from the continuous stimulation of external information and the continuous accumulation of knowledge. In order to obtain an ideal design inspiration from nature, researchers have proposed a large number of biological information retrieval and knowledge acquisition methods. But how to purposefully acquire valuable biological knowledge in order to effectively stimulate design inspiration and produce the novel and feasible designs idea is still an urgent problem to be solved. This paper proposes a method for acquiring valuable biological knowledge to efficiently stimulate inspiration and quickly conceive solutions in engineering design. First, keywords, such as the functional requirements and key components of design objects, are selected as the engineering terminologies. Next, biological keywords related to the engineering terminologies are searched from the biological dictionary and biology websites. Then in order to retrieve enough biological knowledge, these biological keywords are expanded manually and automatically respectively based on Thesaurus Webpage and WordNet database, and expanded keywords are filtered according to repeated words and different forms of the same words. Finally, in the biological knowledge base, biological keywords that had been filtered are used to obtain biological knowledge with Python web crawler programming. Through an example of application for ship equipment, the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

The paper presents an approach to mining heterogeneous information networks by decomposing them into homogeneous networks. The proposed HINMINE methodology is based on previous work that classifies nodes in a heterogeneous network in two steps. In the first step the heterogeneous network is decomposed into one or more homogeneous networks using different connecting nodes. We improve this step by using new methods inspired by weighting of bag-of-words vectors mostly used in information retrieval. The methods assign larger weights to nodes which are more informative and characteristic for a specific class of nodes. In the second step, the resulting homogeneous networks are used to classify data either by network propositionalization or label propagation. We propose an adaptation of the label propagation algorithm to handle imbalanced data and test several classification algorithms in propositionalization. The new methodology is tested on three data sets with different properties. For each data set, we perform a series of experiments and compare different heuristics used in the first step of the methodology. We also use different classifiers which can be used in the second step of the methodology when performing network propositionalization. Our results show that HINMINE, using different network decomposition methods, can significantly improve the performance of the resulting classifiers, and also that using a modified label propagation algorithm is beneficial when the data set is imbalanced. [ABSTRACT FROM AUTHOR]

This article discusses the September, 1, 2006 issue of "Information Retrieval," which contains six selected articles from the 27th European Conference on Information Retrieval Research. The conference, originally called the "Annual Colloquium on Information Retrieval Research, was created by the Information Retrieval Specialist Group of the British Computer Society. Papers in this issue include "Noun Phrases in Interactive Query Expansion and Document Ranking," by Olga Vechtomova, "Can constrained relevance feedback and display strategies help users retrieve items on mobile devices?" by Vishwa Vinay, Ingemar J. Cox, and "User study on ontology as a query construction tool," by Sari Suomela and Jaana Kekä¨ainen.

The abundance of literature related to nutritional support reflects its recently recognised role in preventing metabolic complications and gut dysfunction during critical illness. However, some published studies lack relevance to critically ill patients, as a result of the selection of subjects and outcome variables, or flaws in the study design, as well as in the type, composition, timing, route of administration and amount of nutritional support given. This review will highlight these confounding factors by describing two imaginary (but typical) clinical trials and by analysing some studies published. The point at issue is that basic quality requirements, such as the formulation of a prospective hypothesis and the delineation of the effects of the reference treatment, are often lacking in many studies published. Data analysis was often found to be biased by the absence of statistical power calculation and intention-to-treat analysis. Globally, studies designed to assess the effects of nutritional support on the outcome of critically ill patients, rarely fulfil basic quality requirements and should therefore be interpreted cautiously. We suggest simple strategies or study design that will allow important questions to be answered by future clinical trials. [ABSTRACT FROM AUTHOR]