Search

The number of published findings in biomedicine increases continually. At the same time, specifics of the domain's terminology complicates the task of relevant publications retrieval. In the current research, we investigate influence of terms' variability and ambiguity on a paper's likelihood of being retrieved. We obtained statistics that demonstrate significance of the issue and its challenges, followed by presenting the sci.AI platform, which allows precise terms labeling as a resolution. [ABSTRACT FROM AUTHOR]

Classification of research articles into different subject areas is an extremely important task in bibliometric analysis and information retrieval. There are primarily two kinds of subject classification approaches used in different academic databases: journal-based (aka source-level) and article-based (aka publication-level). The two popular academic databases- Web of Science and Scopus- use journal-based subject classification scheme for articles, which assigns articles into a subject based on the subject category assigned to the journal in which they are published. On the other hand, the recently introduced Dimensions database is the first large academic database that uses article-based subject classification scheme that assigns the article to a subject category based on its contents. Though the subject classification schemes of Web of Science have been compared in several studies, no research studies have been done on comparison of the article-based and journal-based subject classification systems in different academic databases. This paper aims to compare the accuracy of subject classification system of the three popular academic databases: Web of Science, Scopus and Dimensions through a large-scale user-based study. Results show that the commonly held belief of superiority of article-based subject classification over the journal-based subject classification scheme does not hold at least at the moment, as Web of Science appears to have the most accurate subject classification. [ABSTRACT FROM AUTHOR]

The papers in this issue are a selection of the papers presented at the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES2012) held on 10, 11 and 12 September 2012, in San Sebastian, Spain. The main bias for the selection of the papers has been the proposition of foundational works or reviews that focus on some specific issues of intelligent systems and knowledge engineering. The variety of the papers collected is great going from some abstract mathematical topics up to more close to the earth applications of knowledge engineering such as information retrieval. [ABSTRACT FROM AUTHOR]

The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure's construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large. [ABSTRACT FROM AUTHOR]

Evaluating the performance of Content-Based Image Retrieval (CBIR) systems is a challenging and intricate task, even for experts in the field. The literature presents a vast array of CBIR systems, each applied to various image databases. Traditionally, automatic metrics employed for CBIR evaluation have been borrowed from the Text Retrieval (TR) domain, primarily precision and recall metrics. However, this paper introduces a novel quantitative metric specifically designed to address the unique characteristics of CBIR. The proposed metric revolves around the concept of grouping relevant images and utilizes the entropy of the retrieved relevant images. Grouping together relevant images holds great value from a user perspective, as it enables more coherent and meaningful results. Consequently, the metric effectively captures and incorporates the grouping of the most relevant outcomes, making it highly advantageous for CBIR evaluation. Additionally, the proposed CBIR metric excels in differentiating between results that might appear similar when assessed using other metrics. It exhibits a superior ability to discern subtle distinctions among retrieval outcomes. This enhanced discriminatory power is a significant advantage of the proposed metric. Furthermore, the proposed performance metric is designed to be straightforward to comprehend and implement. Its simplicity and ease of use contribute to its practicality for researchers and practitioners in the field of CBIR. To validate the effectiveness of our metric, we conducted a comprehensive comparative study involving prominent and well-established CBIR evaluation metrics. The results of this study demonstrate that our proposed metric exhibits robust discrimination power, outperforming existing metrics in accurately evaluating CBIR system performance. [ABSTRACT FROM AUTHOR]

In this paper, we reconstructed the medical history of frontotemporal dementia (FTD) by reviewing the literature and analyzing papers with the highest impact through citation index. Several research studies and groups involved in FTD have been reviewed. An increasing amount of knowledge has been made available in the last 20 years through a large number of publications, leading to a better definition of the genetic and clinical bases of the disease. A total of 1,436 references (articles and reviews), published in 395 journals, were retrieved through the Scopus database. The two highest publication peaks (i.e., largest number of publications) were found in 2000 and 2008. The most cited papers considering both total citation number and the number of citations within the first two years after publication refer to: (i) the genetic bases of FTD, (ii) the clinical criteria that progressively refined the different FTD phenotypes, and (iii) FTD epidemiology. Advanced neuroimaging techniques, genotype-phenotype heterogeneity, and animal models gave us a broader understanding of various aspects of the disorder. These findings confirm the great interest in FTD research. The analysis of the literature might help in guiding future goals in the field. [ABSTRACT FROM AUTHOR]

There are two mainstream strategies for image-text matching at present. The one, termed as joint embedding learning, aims to model the semantic information of both image and sentence in a shared feature subspace, which facilitates the measurement of semantic similarity but only focuses on global alignment relationship. To explore the local semantic relationship more fully, the other one, termed as metric learning, aims to learn a complex similarity function to directly output score of each image-text pair. However, it significantly suffers from more computation burden at retrieval stage. In this paper, we propose a hierarchically joint embedding model to incorporate the local semantic relationship into a joint embedding learning framework. The proposed method learns the shared local and global embedding spaces simultaneously, and models the joint local embedding space with respect to specific local similarity labels which are easy to access from the lexical information of corpus. Unlike the methods based on metric learning, we can prepare the fixed representations of both images and sentences by concatenating the normalized local and global representations, which makes it feasible to perform the efficient retrieval. And experiments show that the proposed model can achieve competitive performance when compared to the existing joint embedding learning models on two publicly available datasets Flickr30k and MS-COCO. [ABSTRACT FROM AUTHOR]

Retrieving mathematical expressions from scientific documents is a challenging task as mathematical expressions or formulae are quite different from the traditional text. Mathematical expressions are highly symbolic and complex. Moreover, the structure of a mathematical formula conveys a semantic meaning which cannot be overlooked. This paper proposes a scientific document retrieval system based on mathematical formula query. The paper explores the concept of Structure Encoded String (SES), which has been employed for mathematical expressions to capture the relations among the formula structures. A pattern based trie indexing scheme has been proposed for faster retrieval. The Jaro-Winkler Similarity has been adopted for matching and ranking. Experiments are conducted, results are reported using standard evaluation measures and compared with similar existing systems. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of retrieval accuracy and time-consuming of traditional document information retrieval methods, this paper designs an intelligent retrieval method of library document information based on hidden topic mining. Firstly, LDA model is used to mine the hidden topics of library document information, and then, based on the mining results, similarity degree of document information is calculated in inference network model. Finally, the Bayesian model is constructed in the sample space to retrieve the library literature information under the maximum retrieval space coverage. Experimental results show that, compared with traditional retrieval methods, the proposed method improves the retrieval accuracy significantly, with the highest retrieval accuracy reaching 99%, and the retrieval time is significantly reduced, indicating that the proposed method effectively improves the retrieval accuracy and timeliness. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the current Web has become the main platform for people to publish and retrieve information. How to quickly and accurately find the information required by users in a large amount of network information resources has become an urgent need of the people. Web crawlers are research fields that appear to meet this demand. Based on this, the paper designs and implements a distributed web crawler system based on the existing research work, and its goal is to provide high quality data support for the network public opinion system. The web crawler system designed and implemented in this paper solves the problems of low efficiency, poor scalability and low automation of single-machine crawlers, which improves the speed of webpage collection and data extraction precision and expands the scale of webpage collection. At the end of the article, the system related interface screenshots and test results are displayed. It can be seen from the test results that the crawler system can effectively collect dynamic web pages, and the result of automatic extraction of web pages has high precision, and also realizes the entire crawling system. [ABSTRACT FROM AUTHOR]

Traditional approaches to content-based recommendation and collaborative filtering do not suffer from cold-start problem, which is a challenge to recommend items for an unknown user. In this paper we present a Personalized Document Retrieval System which takes into account a social network information about the users. The overall idea of the system is to cluster users into groups of similar interests based on theirs usage data and to determine a representative profile for each of the groups. When a new user joins the system, he or she is classified into one of existing group based on his or her user data and the representative profile of the group becomes a starting profile for the new user. This paper focuses on a method for updating ontology-based user profile using Bayesian network approach. We analyze some properties of proposed updating method and describe an idea of experimental evaluations. [ABSTRACT FROM AUTHOR]

Cross-lingual event retrieval is an information retrieval task aimed at cross-lingual event retrieval among multiple languages to find text or documents related to a specific event. Specific to Chinese-Vietnamese cross-language event retrieval, it involves using Chinese as a query to retrieve Vietnamese documents related to the query event. The critical issue is how to efficiently align query and document representations with limited resources. Existing cross-language pre-training models are trained on large-scale multilingual corpora, but their training goals do not include explicit language alignment tasks. Due to the uneven distribution of training corpora between different languages, these models have The problem of language bias. Therefore, this linguistic bias is also inherited in cross-lingual retrieval based on these models. To solve this problem, this paper proposes a Chinese-Vietnamese cross-lingual event retrieval method based on knowledge distillation. This approach enables the model to learn good query-document matching features from monolingual retrieval by transferring knowledge from high-resource to low-resource languages. By enhancing the alignment between queries and documents in different languages in a shared semantic space, the method improves the performance of Chinese-Vietnamese cross-lingual event retrieval. [ABSTRACT FROM AUTHOR]

The goal of this project is to augment clinician communication by connecting it to evidence-based research, providing explicit knowledge to corroborate the experiential knowledge shared between health care practitioners. The source of tacit knowledge sharing is the Pediatric Pain Mailing List (PPML), a forum for practicing clinicians to contact peers on the subject of pain in children. The messages, dating back to 1993, are processed for pertinent information and gathered together into threads. They are then parsed and connected to a set of MeSH keywords, which is used to search Pubmed and return a set of papers that correspond to the subject being discussed. The results are presented in an online forum, providing clinicians with an arena in which they can browse the archives of the PPML and connect those conversations to pertinent medical literature. [ABSTRACT FROM AUTHOR]

This research presents an exploratory and descriptive study on the use of knowledge organization processes and systems in the context of repositories, published in journals indexed in databases between 2015 and 2020. The authors of these papers do not necessarily publish in specific events and journals in the Knowledge Organization area, but rather in the Library and Information Science arena. The study has been carried out in four steps: 1. Search, retrieval and selection of articles; 2. Development of a data codebook; 3. Identification and codification of topics; and, 4. Analysis of the data extracted. A final sample of 33 articles was defined. The methodology applied to determine the theme of journal articles is presented in detail, including the use of the Classification System for Knowledge Organization Literature (CSKOL). The illustrative data of disciplinarity and interdisciplinarity present in the articles object of this study are shown and discussed, regarding the predominance of certain ranges of CSKOL and regarding the diversity of the representative themes of the contents. It is possible to conclude that the use of CSKOL proves to be a suitable lens for analysing and understanding the literature on the field of knowledge organization in institutional repositories. It is shown that, in these 33 articles the themes of knowledge organization are combined with interdisciplinary themes from other areas of knowledge. In our opinion, this enriches and improves theoretical support for research development. [ABSTRACT FROM AUTHOR]

News feeds generate colossal amount of data consisting of important information hidden in the intricacies. State of the art methods are still at infancy in providing a very generic and publicly available solution to skim through the important information in the news from various sources and an ability to search using specific keywords in different languages. This paper focuses on designing a tool to extract semantic details from news articles published through various internet sources in various languages. The semantic information is stored within DBMS for ease of organizing and retrieving the data. Further, a querying facility to search through entire articles based on the keyword or date-based search is also proposed to view the crisp content. The news articles in English, and two Indian languages - Hindi and Malayalam are considered for experimentation. The proposed strategy consists of two main components namely, Generative model creation and Query engine. Generative model aims to extract important entities and keywords along with their relevance to the article and other similar articles using Latent Dirichlet Allocation(LDA) and Named Entity Recognition(NER). Query engine is to facilitate on the fly retrieval of semantic content from the database, based on user keyword. The search engine, along with database indexing, reduces the access time to the database thereby retrieving the information in less time. Experimental results show that the proposed method is effective in terms of quality of information and time consumed for information retrieval. [ABSTRACT FROM AUTHOR]

In recent years, the dramatic increase in academic research publications has gained significant research attention. Research has been carried out exploring novel ways of providing information services using this research content. However, the task of extracting meaningful information from research documents remains a challenge. This paper presents our research work on developing intelligent information systems that exploit online article databases. We present in this paper, a linked data application which uses a new semantic publishing model for providing value added information services for the research community. The paper presents a conceptual framework for modelling contexts associated with sentences in research articles and discusses the Sentence Context Ontology, which is used to convert the information extracted from research documents into machine-understandable data. The paper reports supervised learning experiments carried out using conditional probabilistic models for achieving automatic context identification. The paper also describes a Semantic Web Application that provides various citation context based information services. [ABSTRACT FROM AUTHOR]

BACKGROUND: For campus workplace secure text mining, robotic assistance with feature optimization is essential. The space model of the vector is usually used to represent texts. Besides, there are still two drawbacks to this basic approach: the curse and lack of semantic knowledge. OBJECTIVES: This paper proposes a new Meta-Heuristic Feature Optimization (MHFO) method for data security in the campus workplace with robotic assistance. Firstly, the terms of the space vector model have been mapped to the concepts of data protection ontology, which statistically calculate conceptual frequency weights by term various weights. Furthermore, according to the designs of data protection ontology, the weight of theoretical identification is allocated. The dimensionality of functional areas is reduced significantly by combining standard frequency weights and weights based on data protection ontology. In addition, semantic knowledge is integrated into this process. RESULTS: The results show that the development of the characteristics of this process significantly improves campus workplace secure text mining. CONCLUSION: The experimental results show that the development of the features of the concept hierarchy structure process significantly enhances data security of campus workplace text mining with robotic assistance. [ABSTRACT FROM AUTHOR]

Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases (Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method. [ABSTRACT FROM AUTHOR]

Attempts to express information from various documents in graph form are rapidly increasing. The speed and volume in which these documents are being generated call for an automated process, based on machine learning techniques, for cost-effective and timely analysis. Past studies responded to such needs by building knowledge graphs or technology trees from the bibliographic information of documents, or by relying on text mining techniques in order to extract keywords and/or phrases. While these approaches provide an intuitive glance into the technological hotspots or the key features of the select field, there still is room for improvement, especially in terms of recognizing the same entities appearing in different forms so as to interconnect closely related technological concepts properly. In this paper, we propose to build a patent knowledge network using the United States Patent and Trademark Office (USPTO) patent filings for the semiconductor device sector by fine-tuning Huggingface's named entity recognition (NER) model with our novel edge weight updating neural network. For the named entity normalization, we employ edge weight updating neural network with positive and negative candidates that are chosen by substring matching techniques. Experiment results show that our proposed approach performs very competitively against the conventional keyword extraction models frequently employed in patent analysis, especially for the named entity normalization (NEN) and document retrieval tasks. By grouping entities with named entity normalization model, the resulting knowledge graph achieves higher scores in retrieval tasks. We also show that our model is robust to the out-of-vocabulary problem by employing the fine-tuned BERT NER model. [ABSTRACT FROM AUTHOR]

Entity linking is an important task for information retrieval and knowledge graph construction. Most existing methods use a bi-encoder structure to encode mentions and entities in the same space, and learn contextual features for entity linking. However, this type of system still faces some problems: (1) the entity embedding part of the model only learns from the local context of the target entity, which is too unique for entity linking model to learn the context commonality of information; (2) the entity disambiguation part only uses similarity calculation once to determine the target entity, resulting in insufficient interaction between the mentions and candidate entities, and ineffective recall of real entities. We propose a new entity linking model based on graph neural network. Different from other bi-encoder retrieval systems, this paper introduces a fine-grained semantic enhancement information into the entity embedding part of the bi-encoder to reduce the specificity of the model. Then, the cross-attention encoder is used to re-rank the target mention and each candidate entity after the entity retrieval model. Experimental results show that although the model is not optimal in inference speed, it outperforms all baseline methods on the AIDA-CoNLL dataset, and has good generalization effects on four datasets in different fields such as MSNBC and ACE2004. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence (AI) has spotlighted ChatGPT as a key technology in the realm of information retrieval (IR). Unlike its predecessors, it offers notable advantages that have captured the interest of both industry and academia. While some consider ChatGPT to be a revolutionary innovation, others believe its success stems from smart product and market strategy integration. The advent of ChatGPT and GPT-4 has ushered in a new era of Generative AI, producing content that diverges from training examples, and surpassing the capabilities of OpenAI's previous GPT-3 model. In contrast to the established supervised learning approach in IR tasks, ChatGPT challenges traditional paradigms, introducing fresh challenges and opportunities in text quality assurance, model bias, and efficiency. This paper aims to explore the influence of ChatGPT on IR tasks, providing insights into its potential future trajectory. [ABSTRACT FROM AUTHOR]

Dense passage retrieval is a popular method in information retrieval recently, especially in open domain question answering. It aims to retrieve related articles from massive passages to answer the question. Retriever can increase retrieval speed with less loss of accuracy compared to other methods. However, the pretrained language models used in recent research are often ineffective in semantic embedding, which will reduce accuracy. In addition, we find that contrastive learning will diverge the representation space, and Siamese models with independent parameters on both sides will decrease generalization performance. Therefore, we propose span prompt dense passage retrieval (SPDPR) based on span mask prompt tuning and parameter sharing in Chinese open-domain dense retrieval. This model can generate more efficient representation embeddings and effectively counteract the separation tendency between positive samples. We evaluate the effectiveness of SPDPR in DYKzh, as well as two Chinese datasets. SPDPR surpasses all SOTAs implemented in DYKzh and achieves a competitive result in other datasets. [ABSTRACT FROM AUTHOR]

In this information age, there is exponential growth in visual content and video captioning can address many real-life applications. Automatic generation of video captions can be beneficial to comprehend a video in a short time, assist in faster information retrieval, video analysis, indexing, report generation, etc. Captioning of industrial videos is of importance to get a visual and textual summary of the work ongoing in the industry. The generated captioned summary of the video can assist in remote monitoring of industries and these captions can be utilized for video question-answering, video segment extraction, productivity analysis, etc. Due to the presence of diverse events processing of industrial videos are more challenging compared to other domains. In this paper, we address the real-life application of generating the descriptions for the videos of a labor-intensive industry. We propose a keyframe-based approach for the generation of video captions. The framework produces a video summary by extraction of keyframes, thereby reducing the video captioning task to image captioning. These keyframes are passed to the image captioning model for description generation. Utilizing these individual frame captions, multi-caption descriptions of a video are generated with a unique start and end time of each caption. For image captioning, a merge encoder-decoder model with a stacked decoder for caption generation is used. We have performed experimentation on a dataset specifically created for the small-scale industry. We have also shown that data augmentation on the small dataset can greatly benefit the generation of remarkably good video descriptions. Results of extensive experimentation performed by utilizing different image encoders, language encoders, and decoders in the merge encoder-decoder model are reported. Apart from presenting the results on domain-specific data, results on domain-independent datasets are also presented to show the applicability of the technique in general. Performance comparison with existing datasets - OVSD and Flickr8k and Flickr30k are reported to demonstrate the scalability of our method. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) is already used as the primary visualization and user interaction tool in several scientific and business areas. At the same time new AR technologies and frameworks considerably facilitate both the development of innovative applications and also their wide adoption in different domains of everyday life. In the area of healthcare AR solutions make use of mobile or wearable devices and glasses to support, among others, education and healthcare professionals training. The aim of this paper is to present a prototype mHealth app for education, which uses AR and computer vision technologies for pharmaceutical substances recognition on drug packaging. The conceptual design of the system includes three main components which are responsible for a) Text recognition, b) Drug identification and c) AR operations for interactivity. The prototype application is available in Android or iOS platforms and has been evaluated in real-world scenarios. Camera and screen of the mobile phones fulfill the text recognition and AR operations, which eliminates the need for special equipment, while PubChem and 3D Model databases provide assets required for the drug identification and AR visualizations. The results highlight the value of AR for educational purposes, especially when combined with advanced image recognition technologies to build interactive AR encyclopedias. [ABSTRACT FROM AUTHOR]

In this paper we present a first analysis towards better understanding of the query constraining aspects of knowledge, as expressed in the most used public medical bibliographic database MEDLINE. Our results indicate, possibly not surprising, that new terms occur, but also that traditional terms are replaced by more specific ones or even go out of use as they become common knowledge. Hence, as knowledge evolve over time, search methods may benefit from becoming more sensitive to knowledge expression, to enable finding new, as well as older, relevant database contents. [ABSTRACT FROM AUTHOR]

This paper proposes two kinds of image features. One feature is the spatial-histogram feature. It combines the color histogram feature and the information about the dimensional position of pixels in an image to record the distribution of the pixels' colors that are present in different spatial positions within an image. The other image feature proposed in this paper is the color-complexity feature, which can be used to describe the change of pixel colors in the image. From the experimental results, ANMRR value is provided and we observe that the image retrieval system based on these two kinds of image features can provide a fairly good accuracy rate in image retrieval. Moreover, it has the capacity to tolerate errors; that is, for images that are damaged by rotation, shift, or color variant attacks, their similar image pairs can still be retrieved from the image database. Thus, the accuracy and flexibility of the image retrieval system are drastically improved. [ABSTRACT FROM AUTHOR]

Finding good relevant documents for query optimization is a well-known difficulty in the field of document retrieval. This paper develops a novel approach, named Exponential Aquila Optimizer (EAO)-based Deep Fuzzy Clustering for retrieving the documents. The proposed technique effectively finds the relevant documents and tries to understand the relationship among the documents and queries in terms of the significance of documents for query optimization. Here, the Deep Fuzzy Clustering is employed for performing cluster-based inverted indexing where the Training procedure of Deep Fuzzy Clustering is done using the developed optimization algorithm, named EAO. Meanwhile, the developed EAO is newly designed by the incorporation of EWMA and AO. In addition, complex query matching is done using the Tversky index for the user-based queries, such as multigram queries and semantic queries. On the other hand, the RV coefficient is accomplished for performing query optimization for relevant document retrieval. The proposed technique achieves better performance in terms of the performance metrics, like precision, recall, and F-measure with the maximum precision of 1, maximum recall of 0.956, and maximum F-measure of 0.977, respectively. [ABSTRACT FROM AUTHOR]

Access control is an important mechanism to protect sensitive information and relational system resources. The traditional access control model (TACM), such as DAC, MAC, RBAC, etc., is no longer suitable for open network due to the lack of dynamic permission management. The increasing network nodes make the information storage and resource access becoming distributed. The traditional access control model has the characteristics of low adaptive ability and single deployment and application mode due to the centralized management mode. Therefore, this access control environment inevitably puts access control pressure on access control authorization. In order to overcome the shortcomings of traditional access control model, a new access control model named DMPAC (Distributed management of permission for access control model) is proposed in the paper. The authorization mechanism of the model has a distributed and dynamic management access permission, and all nodes covered by the model have the opportunity to participate in the execution of access and control. The model DMPAC provides the benefits of traditional access control models in terms of secure access and dynamic management. We also describe the framework and execution process of the model and the application of DMPAC in access control. At last, we will present some experimental results to show that while maintaining the effectiveness of distributed access control through the management of access permissions, DMPAC can achieve the performance of traditional access control models. [ABSTRACT FROM AUTHOR]

Search systems based on both professional meta-data (e.g., title, description, etc.) and social signals (e.g., like, comment, rating, etc.) from social networks is the trending topic in information retrieval (IR) field. This paper presents 2SRM (Social Signals Relevance Model), an approach of IR which takes into account social signals (users' actions) as an additional information to enhance a search. We hypothesize that these signals can play a role to estimate a priori social importance (relevance) of the resource (document). In this paper, we first study the impact of each such signal on retrieval performance. Next, some social properties such as popularity, reputation and freshness are quantified using several signals. The 2SRM combines the social relevance, estimated from these social signals and properties, with the conventional textual relevance. Finally, we investigate the effect of the social signals on the retrieval effectiveness using state-of-the-art learning approaches. In order to identify the most effective signals, we adopt feature selection algorithms and the correlation between the signals. We evaluated the effectiveness of our approach on both IMDb (Internet Movie Databese) and SBS (Social Book Search) datasets containing movies and books resources and their social characteristics collected from several social networks. Our experimental results are statistically significant, and reveal that incorporating social signals in retrieval model is a promising approach for improving the retrieval performance. [ABSTRACT FROM AUTHOR]

Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals (images, videos, news, etc.), into the Web search results. Major search engines perform what is known as Aggregated Search. Aggregated search is relatively new and its advantages need to be evaluated. With the increasing size of the data set on the network, different results retrieved from different dimensions are sometimes entirely different for a given search problem. In this paper, a new aggregated search algorithm was proposed. Firstly, the retrieval data in the vertical domain was textualized, and Doc2Vec makes the vector representation of the data. Then the results are aggregated and output by dimension reduction and density clustering. The experimental results show that the model achieves good accuracy in 20 given queries and significantly improves the aggregated search results. Discussion about the results also allowed us to identify some useful thoughts concerning the evaluation of AS approaches. [ABSTRACT FROM AUTHOR]

In order to solve the shortcomings of traditional methods in storage capacity, fault tolerance and time consuming, a secure data retrieval method based on block chain technology was proposed. Initialize the blockchain system, encrypt the cyberspace data through the public key, and upload the encrypted data by connecting the public key of the data aggregator. Complete the blockchain data consensus and verify the encrypted data through the aggregator workload calculation and cyberspace data verification. The blockchain data storage structure is constructed by combining the on-chain index table and the off-chain database. Decrypt the data retrieval instruction with the key, and then transfer the required data into the block chain data storage structure to achieve secure data retrieval in network space. Experimental results show that the proposed method has high storage capacity and fault tolerance, and the maximum running time is only 1.43 s. [ABSTRACT FROM AUTHOR]

BACKGROUND: The health sector has long been affected by programs, actions, plans to digitize data and care processes with a view to better protecting individual health, as well as public health, resulting in a slow and uneven development of different and often incompatible national services. OBJECTIVE: This paper aims to explore the grounds behind the urgency of turning the digital priority into concrete actions, as acknowledged by political leaders in the Rome Declaration, by explaining the capacity of digital tools to enhance healthcare management and the current obstacles. METHODS: It considers the progressive extension of the EU institutions' scope of action during the pandemic, the related supporting financial strategies launched and some examples of digital contact tracing systems. RESULTS: It emerged that the pandemic highlighted the inadequacy of purely national policies and the advantages of leveraging the digital health data processing for governance, surveillance and response to cross-border and global threats. CONCLUSIONS: Considering what emerged during the pandemic and the solemn commitment of the world's major political leaders, the solution to the still existing technical and organizational interoperability issues will no longer be postponed. [ABSTRACT FROM AUTHOR]

Thirukkural, a Tamil classic literature, which was written in 300 BCE is a didactic literature. Though Thirukkural comprises 1330 couplets which are organized into three sections and 133 chapters, in order to retrieve meaningful Thirukkural for a given query in search systems, a better organization of the Thirukkural is needed. This paper lays such a foundation by classifying the Thirukkural into ten new categories called superclasses that is helpful for building a better Information Retrieval (IR) system. The classifier is trained using Multinomial Naïve Bayes algorithm. Each superclass is further classified into two subcategories based on the didactic information. The proposed classification framework is evaluated using precision, recall and F-score metrics and achieved an overall F-score of 82.33% and a comparison analysis has been done with the Support Vector Machine, Logistic Regression and Random Forest algorithms. An IR system is built on top of the proposed system and the performance comparison has been done with the Google search and a locally built keyword search. The proposed classification framework has achieved a mean average precision score of 89%, whereas the Google search and keyword search have yielded 59% and 68% respectively. [ABSTRACT FROM AUTHOR]

Information overload has become a very important aspect of information retrieval domain. Even if a user knows where to look for interesting information, he can have a problem with precisely formulating his information needs. A solution of the problem is personalization and recommendation system - they observe user activities, analyze them to discover important preferences. Based on these information the system can improve the effectiveness of the results. In this paper we present a method for determining user profile in a document retrieval system. We propose ontology-based profile. Such a structure allows to process semantic relations between users' queries. We focus on methods for adapting profile because only up-to-date profile can help the user to obtained results that correspond with his information needs. We present a set of postulates for adaptation methods. Performed experimental evaluations of developed methods are promising. [ABSTRACT FROM AUTHOR]