Search

The revolution of the Internet of Things (IoT) and the Web of Things (WoT) has brought new opportunities and challenges for the information retrieval (IR) field. The exponential number of interconnected physical objects and real-time data acquisition requires new approaches and architectures for IR systems. Research and prototypes can be crucial in designing and developing new systems and refining architectures for IR in the WoT. This paper proposes a unified and holistic approach for IR in the WoT, called IR.WoT. The proposed system contemplates the critical indexing, scoring, and presentation stages applied to some smart cities' use cases and scenarios. Overall, this paper describes the research, architecture, and vision for advancing the field of IR in the WoT and addresses some of the remaining challenges and opportunities in this exciting area. The article also describes the design considerations, cloud implementation, and experimentation based on a simulated collection of synthetic XML documents with technical efficiency measures. The experimentation results show promising outcomes, whereas further studies are required to improve IR.WoT effectiveness, considering the WoT dynamic characteristics and, more importantly, the heterogeneity and divergence of WoT modeling proposals in the IR domain. [ABSTRACT FROM AUTHOR]

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]

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470 K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions. [ABSTRACT FROM AUTHOR]

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques. [ABSTRACT FROM AUTHOR]

There has been a proliferation of new research discovery tools that aid scientists in finding relevant publications. To obtain a general overview of this development, this article generates a conceptual typology of all possible research discovery tools by drawing from the information-theoretical concepts of redundancy/variety. Bibliometric links between scholarly publications can thus exhibit 'redundancy' (i.e. expectable linkages between academic works) or 'variety' (i.e. original co-occurrence patterns). On the redundancy-reproducing end of the typology are machines that harness extant co-citations or keyword queries, such as academic search engines and paper recommender systems. The variety end of the spectrum harbours services that enable categorial browsing or that suggest publications randomly, such as journals' tables of contents or random paper bots. The typology has implications for understanding how the design of research discovery platforms may ultimately shape aggregate citational networks of science. [ABSTRACT FROM AUTHOR]

Purpose: This paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results. Design/methodology/approach: The research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined. Findings: The study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study. Research limitations/implications: The study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries. Practical implications: The findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface. Originality/value: The study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines. [ABSTRACT FROM AUTHOR]

This paper discusses the importance of search engine optimization (SEO) for digital repositories. We first describe the importance of SEO in the academic environment. Online systems, such as institutional digital repositories, are established and used to disseminate scientific information. Next, we present a case study of our own institution's DSpace repository, performing several SEO tests and identifying the potential SEO issues through a group of three independent audit tools. In this case study, we attempt to resolve most of the SEO problems that appeared within our research and propose solutions to them. After making the necessary adjustments, we were able to improve the quality of SEO variables by more than 59% compared to the non-optimized state (a fresh installation of DSpace). Finally, we apply the same software audit tools to a sample of global institutional repositories also based on DSpace. In the discussion, we compare the SEO sample results with the average score of the semi-optimized DSpace repository (from the case study) and make conclusions. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [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]

Institutional Repositories (IRs) are an effective way of managing and disseminating institutions’ in scholarly communication. More specifically, an IR enhances the visibility and discoverability of the content and validates the repository’s importance. Knowledge Organisation System (KOS) strengthens the digital content organisation, connects users with collections, and improves information retrieval functionalities. This paper investigates the present status of user interface features and incorporates KOS in the institutional repository of technical institutions, restricted to Centrally Funded Technical Institutions in India. A group of twenty-four web-accessible institutional repositories was identified, and their KOS and user interface features were evaluated. It was found that user interfaces of all IRs under study comply with essential search and navigation functionalities, such as simple and advanced search, browsing, faceted or filtering approaches, and integration with multiple KOS. Only a few of them include complex KOS, such as control vocabulary. All repositories show their search results in both normal-text and metadata views. Some have specific display features, such as highlighting the query or displaying a thumbnail. Google is one of the most popular search engines that indexes IR content for visibility and discoverability, and approximately 90 per cent of repositories are linked with NDLI. Global visibility and impact participation are moderate, and they require attention. [ABSTRACT FROM AUTHOR]

Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users' visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder's line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations. [ABSTRACT FROM AUTHOR]

The past work on Information Retrieval (IR) targeting web document collections shows that incorporating a measure that measures the quality of web documents, or rather the document prior (e.g., PageRank), into an IR system improves the retrieval effectiveness. In this study, we introduce new document priors and empirically investigate their effect by employing them as features in a learning to rank (LTR) deployment. The experiments are performed on the two standard Web IR test collections: the ClueWeb09 and the ClueWeb12 datasets, which include 500 and 733 million web documents, respectively, and the associated TREC & NTCIR query sets with a total number of 1,204 queries. A strong baseline is formed by using standard features introduced in the previous works, with respect to which the effect of newly introduced features in this paper is empirically compared. We test our features by LambdaMART, which is state-of-the-art LTR technique. The results reveal that the features introduced in this work led improvement in retrieval performance on the test collections in use. The introduced features are classified into 5 groups with respect to functional properties and each group is also analyzed in detail. • Measuring the quality of a web document is a challenging task. • A web page contains various elements that indicate quality. • Document priors can be used as query-independent features in LTR deployments. • Introduced query-independent features can measure the quality of web documents. • Contribution of the new feature groups to the retrieval performance is examined. [ABSTRACT FROM AUTHOR]

Information retrieval aims to retrieve the documents that answer users' queries. A typical search process consists of different phases for which a variety of components have been defined in the literature; each one having a set of hyper-parameters to tune. Different studies focused on how and how much the components and their hyper-parameters affect the system performance in terms of effectiveness, others on the query factor. The aim of these studies is to better understand information retrieval system effectiveness. This paper reviews the literature of this domain. It depicts how data analytics has been used in IR for a better understanding system effectiveness. This review concludes lack of full understanding of system effectiveness according to the context although it has been possible to adapted the query processing to some contexts successfully. This review also concludes that, even if it is possible to distinguish effective from non effective system on average on a query set, nor the system component analysis nor the query features analysis were successful in explaining when and why a particular system fails on a particular query. [ABSTRACT FROM AUTHOR]

With the development of information technologies, more and more information retrieval tools, containing a wide variety of information, can be found. Internet resources, universal and specialized search engines, virtual libraries are becoming the main information retrieval tools for students. The article introduces an exploratory (pilot) research aiming to find out the differences of information retrieval between Lithuanian and Portuguese students. Šiauliai State College offers a unique study programme Information Management, having a signed contract of a double diploma with Porto (Portugal) Polytechnic Institute Porto Accounting and Business School (ISCAP). 10 Portuguese and 10 Lithuanian students participated in the research. The online questionnaire was forwarded to the students of ISCAP Porto Accounting and Business School and to the second year students of the study programme of Information Management at Šiauliai State College. The research revealed that the respondents Lithuanian students look for scientific papers and books, whereas Portuguese students search for basic information and scientific papers. All the respondents of the survey identified Google Scholar as the most important search engine for scientific information retrieval. [ABSTRACT FROM AUTHOR]

For some years now, the faceted exploratory search has faced tremendous challenges to devise a simple, instantly understandable, and usable user interface to explore the vast amount of data and information available on the Internet. In our previous research, we have indicated certain burdens with some of the existing approaches. Therefore, in this research, we are trying to uncover the attributes that influence users to use the faceted feature of the exploratory search technique. In this paper, we presented several key elements of existing search engine systems, the best of their major features were explored, and guidelines that will help to improve the usability of faceted exploratory search have been suggested and offers a further understanding of the fundamental characteristics of the faceted search, which shows the motivations to use the search, also presented the main parts of a pluggable search system. [ABSTRACT FROM AUTHOR]

Medical images are growing dramatically both in quantity and application in the data era and the emerging Big Data problem. Searching and finding the proper medical image among such a huge number of medical images is not possible unless using a proper medical search engine. Indexing is the backend process in information retrieval systems in which documents are annotated with the index entry to be retrieved more accurately and efficiently. Most of the indexing techniques for medical images are content-based that are more complex and time consuming than the text-based ones. In this paper, a text-based medical image indexing technique is proposed that use medical images' attributes and fragments them with the hybrid fragmentation approach (that are used in distributed database design) and re-form each of such attribute fragments into a hierarchy, constructing a multidimensional index. Hybrid fragmentation approach uses both Horizontal and vertical fragmentation of medical images' attributes provided in header of medical image standard formats (such as the DICOM). Horizontal fragmentation uses values of image attributes (i.e., image content and properties dependent), whilst the vertical fragmentation uses pairwise affinity and correlation of the attributes in the application domain (i.e., application dependent). So, the proposed hybrid fragmentation approach based indexing of medical images aim to consider both the image properties and application statistics together to provide a better functionality. As the experimental performance evaluation results illustrate, the proposed multidimensional indexing can provide better precision of information retrieval rather that a single index or a set of multiple indexes, since that considers semantic relationship of the medical image's attributes via the hybrid (horizontal and vertical) fragmentation. Moreover, the hybrid fragmentations approach based indexing also outperforms the vertical fragmentation-based multidimensional medical image indexing technique in terms of precision, recall and response time. [ABSTRACT FROM AUTHOR]

This paper estimates users' search intents when using the context search engine (CSE) by analyzing submitted queries. Recently, due to the increase in the amount of information on the Web and the diversification of information needs, the gap between user's information needs and a basic search function provided by existing web search engines becomes larger. As a solution to this problem, the CSE that limits its tasks to answer questions about temporal trends has been proposed. It provides three primitive search functions, which users can use in accordance with their purposes. Furthermore, if the system can estimate users' search intents, it can provide more user-friendly services that contribute the improvement of search efficiency. Aiming at estimating users' search intents only from submitted queries, this paper analyzes the characteristics of queries in terms of typical search intents when using CSE, and defines classification rules. To show the potential use of the estimated search intents, this paper introduces a learning to rank into CSE. Experimental results show that MAP (mean average precision) is improved by learning rank models separately for different search intents. [ABSTRACT FROM AUTHOR]

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines. [ABSTRACT FROM AUTHOR]

There is limited guidance on how to web‐search in systematic reviews and concern relates to the reproducibility of searches using search engines such as Google. The aim of this paper is to address one potential source of variation in Google searches: does the geographical location of a researcher affect Google search returns? Using a virtual private network, we ran the same web‐search for the medical technology Dasatinib in 12 different countries. Two researchers independently extracted the search returns by country organised by page rank. We compared: C1. any difference in the items returned by Google searches between countries and C2. any difference in the page rank of items returned between countries. Searches were undertaken on Monday September 28th 2020. From 12 countries, 43 items were identified. For C1: 19 items were common to all 12 countries. Twenty‐four items were missed by searches in some countries. This means that there were differences in search returns between countries. For C2: a randomised trial reported by Raddich et al was the first search return for all countries. All other items, common to all countries, varied in their page‐rank. We find that geographic location would appear to influence Google search returns based on the findings of this case study. The findings suggest that recording the location of the researcher undertaking web‐searching may now be an important factor to report alongside detail on steps taken to minimise personalisation of web‐searches covered by recent guidance. This finding also has implications for stopping‐rules. [ABSTRACT FROM AUTHOR]

Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. Indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions; roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set, images from Tabriz Behbood Hospital and a subset of TCIA images were used. Experimental evaluation results show that Diamond, the proposed multidimensional indexing technique improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

Distracted driving due to smartphone use is one of the key reasons for road accidents. However, the 6G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information services involving the use of smart apps, and related information retrieval by drivers in modern Vehicle-to-People (V2P) Networks. The tension raised due to the conflicting attention requirements of driving and information retrieval can be resolved by designing information retrieval solutions that demand minimal user interaction. In this paper, we construct a Personalized Search Query Generator (PSQG) to reduce driver-mobile interaction during information retrieval in the 6 G era. This system has a query generator and a query recommendation component that update two sets of relationships dynamically: one is the query and the title, another is search and recommendation. The proposed system learns a user's intent based on historical query records and recommends personalized queries, thus reducing the driver-mobile interaction time. We deploy the system into a real search engine and conduct several online experiments. These experiments are conducted using a custom constructed dataset comprising ten million samples. We use the BLEU-score metric and perform A/B testing. The results demonstrate that our system can assist users in making precise queries efficiently. The proposed system can improve drivers’ safety if used in smartphones and other information retrieval systems in vehicles. [ABSTRACT FROM AUTHOR]

People are getting more and more used to consume a digital, online content. Many outlets switched to online publication or at least increased their online presence. Besides, online publication is not the only domain of publishing houses. Many different organisations and companies - including those from area of agriculture and rural development - provide online content in form of articles. Importance of semantic web is growing constantly. Together with metadata descriptions, it is necessary for all the current search engines, smart assistants and AI technologies. Public standards and open source software can significantly speed up development and reduce costs when it comes to the Internet and World Wide Web. The paper provides overview of an updated methodology for creation, storage and presentation of online information content in World Wide Web environment. The latest research was focused mainly on presentation and semantics. The whole research process is established as well as the final f ormulation of the methodology. [ABSTRACT FROM AUTHOR]

Quran is the most significant religious document in the Arabic language in Islamic law. Several Quranic search engines have been designed and widely used for the past two decades. However, these search engines have certain limitations. For example, in many cases, the search is unable to retrieve relevant verses because it is based on keywords or root search and does not rely on the semantic relation between words in the query. The main objective of the present paper is to design a semantic search engine based on ontology as an index. In our work, we focus on creating a new ontology for Quranic document based on a set of useful words extracted from the Quranic Earab book with grammatical functions that serve as concepts. This ontology will be used as an index in information retrieval. The main idea is to create links between the existing Quranic words in the same verse, which will be used with user's query to find the desired verses. We developed a graphical user interface with free and multiple inputs that convert users' Arabic queries into SPARQL queries and then retrieve relevant verses from the ontology. The obtained results show that our proposal provides heightened precision and recall compared to other search engines. [ABSTRACT FROM AUTHOR]

Massive digital documents on Internet leading to use e-learning, and it becomes an emerging field of research due to the massive growth of internet users. E-learning requires suitable document ranking method to avoid navigating to the next Search Engine Result Page (SERP) frequently. The existing document ranking methods are lacking to rank the documents independently based on the conceptual contents. This paper proposes a novel method for ranking the documents independently based on the different classification of term it contains. In this approach, the terms are classified into five categories such as (1) direct query term, (2) expanded terms, (3) semantically related term, (4) supporting terms and (5) stop words. The query has been expanded using domain ontology to acquire more semantic terms for better understanding of user query. The semantic weight has been applied independently over different categories of terms in a document for ranking. The document with the highest augmented value in each category of terms has been ranked first. Remaining documents are ranked in the same way and are arranged in the descending order. The WordNet tool is utilized as a knowledge base and Wu and Palmer semantic distance method have applied for measuring semantic distance between the query and document terms for ranking the terms. The experiments show that the performance of the proposed document ranking method for e-learning retrieved better document compared with existing document ranking methods. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the publication of datasets collected via Google Dataset Search, specialized in families of RNA viruses, whose terminology was obtained from the National Cancer Institute (NCI) thesaurus developed by the US Department of Health and Human Services. The objective is to determine the scope and reuse capacity of the available data, determine the number of datasets and their free access, the proportion in reusable download formats, the main providers, their publication chronology, and to verify their scientific provenance. On the other hand, we also define possible relationships between the publication of datasets and the main pandemics that have occurred during the last 10 years. The results obtained highlight that only 52% of the datasets are related to scientific research, while an even smaller fraction (15%) are reusable. There is also an upward trend in the publication of datasets, especially related to the impact of the main epidemics, as clearly confirmed for the Ebola virus, Zika, SARS-CoV, H1N1, H1N5, and especially the SARS-CoV-2 coronavirus. Finally, it is observed that the search engine has not yet implemented adequate methods for filtering and monitoring the datasets. These results reveal some of the difficulties facing open science in the dataset field. [ABSTRACT FROM AUTHOR]

Query expansion is a method for improving retrieval performance by supplementing an original query with additional terms. This process improves the quality of search engine results and helps users to find the required information. In the recent years, different methods have been proposed in this area. In addition to such a variety of different approaches in this area and necessity of the study of their characteristics, the lack of a comprehensive classification based on candidate expansion terms extraction methods and also suitable and complete criteria to evaluate them, make the precise study, comparison and evaluation of methods for query expansion and choosing appropriate method based on need difficult for researchers. Therefore, in this paper a new useful framework is presented. In the proposed framework, in addition to the identification of three basic approaches based on the candidate expansion terms extraction methods for query expansion and expressing their properties, appropriate criteria for qualitative evaluation of these methods will be described. Next, the proposed approaches will be evaluated qualitatively based on these criteria. Using the systematic and structured framework proposed in this paper leads a useful platform for researchers to be provided for the comparative study of existing methods in the field, investigating their features specially their drawbacks to improve them and choosing appropriate method based on their needs. [ABSTRACT FROM AUTHOR]

Learning to rank (LTR) is one of the problems attracting researchers in information retrieval (IR). The LTR problem refers to ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There is a number of LTR approaches based on machine learning and computational intelligence techniques. Most existing LTR methods have limitations, such as being too slow or not being very effective or requiring a huge computer memory to operate. This paper proposes a LTR method that combines a (1+1)-evolutionary strategy with machine learning. Three variants of the method are investigated: ES-Rank, IESR-Rank and IESVM-Rank. They differ on the chromosome initialisation mechanism for the evolutionary process. ES-Rank simply sets all genes in the initial chromosome to the same value. IESR-Rank uses linear regression, and IESVM-Rank uses support vector machine for the initialisation process. Experimental results from comparing the proposed method to fourteen other approaches from the literature show that IESR-Rank achieves the overall highest performance. Ten problem instances are used here, obtained from four datasets: MSLR-WEB10K, LETOR 3 and LETOR 4. Performance is measured at the top-10 query-document pairs retrieved, using five metrics: mean average precision (MAP), root-mean-square error (RMSE), precision (P@10), reciprocal rank (RR@10) and normalized discounted cumulative gain (NDCG@10). The contribution of this paper is proposing an effective and efficient LTR method combining a list-wise evolutionary technique with point-wise and pair-wise machine learning techniques. [ABSTRACT FROM AUTHOR]

Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate. [ABSTRACT FROM PUBLISHER]

Current metadata schema, internet search engines and databases of scientific literature have an Anglo-American language and cultural bias. While it is possible to catalogue or index non-English, non-Latin script publications and achieve some degree of success in the retrieval of such information, librarians and researchers are often disappointed by inconsistencies in the accuracy of search results for this type of publication and frustrated by the limitations created by the biases embedded in the technology and schema. This paper is the result of a two-year dialog between a Farsi-speaking medical librarian in Iran and an English-speaking metadata librarian in Canada. They present the argument that a metadata schema which incorporates graphical images such as Emoji along with keyword indexing or a controlled vocabulary and new search engines which can make use of the new schema for Boolean searching is a robust potential solution for the difficulties they discussed. While this new information seeking environment that requires both the edition of a graphical facet to metadata schema and new search engines presents the potential to break down significant intercultural barriers, the authors recognize that this undertaking is more than a simple technological or procedural change but would require significant agreement between and work from librarians, standards bodies and researchers. [ABSTRACT FROM AUTHOR]

Evaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides' philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus' flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato's theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato's dialectical method is an appropriate composite method for evaluating information retrieval systems. [ABSTRACT FROM AUTHOR]

Understanding the search behaviour of online users is among the long-tail practices of Interactive Information Retrieval that helps identify the user information needs. The Interactive Social Book Search (SBS), under the umbrella of Interactive Information Retrieval (IIR), aims to understand the user interactions with book collections and the associated professionally-curated and socially-constructed metadata on the baseline and multistage user interfaces (UIs). This paper reports on the book search behaviour of users by reviewing research publications related to the Interactive SBS published during the last two decades. It presents a holistic view of the overall progress of Interactive SBS by summarising and visualising the experimental structure, search systems, datasets, demographics of participants, and findings to identify the research trends and possible future directions. Based on the collected evidence, it attempts to answer how the search system, user interface (UI), and the nature of tasks affect the book search behaviour of users. The article is the first of its kind that attempts to understand the book search behaviour of users in the context of Social Book Search with implications for usability experts and others working in UI design, web search engines, book search engines, digital libraries, collaborative social cataloguing websites, and e-Commerce applications. [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]

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]

This study applies Kuhlthau's Information Search Process stage (ISP) model to understand bilingual users' Internet search experience. We conduct a quasi‐field experiment with 30 bilingual searchers and the results suggested that the ISP model was applicable in studying searchers' information retrieval behavior in search tasks. The ISP model was applicable in studying searchers' information retrieval behavior in simple tasks. However, searchers' emotional responses differed from those of the ISP model for a complex task. By testing searchers using different search strategies, the results suggested that search engines with multilanguage search functions provide an advantage for bilingual searchers in the Internet's multilingual environment. The findings showed that when searchers used a search engine as a tool for problem solving, they might experience different feelings in each ISP stage than in searching for information for a term paper using a library. The results echo other research findings that indicate that information seeking is a multifaceted phenomenon. [ABSTRACT FROM AUTHOR]

Learning algorithms based on probability organize the observed data in subsets corresponding to binary variables. In this paper, we address the problem of estimating one probability space given a set of observed data about $n$n variables or properties. One problem with estimating one single probability space is the exponential number of events. Approximation is one approach to addressing the problem of the exponential order of the number of events. Alternatively to approximation, we change paradigm – from classical, set-based probability spaces based on sets to quantum probability spaces based on vector subspaces. By changing paradigm, we leverage quantum probability and present an efficient algorithm to calculate a Quantum Probability Space (QPS) in only $O(n^4)$O(n4) steps. [ABSTRACT FROM AUTHOR]

Internet searches that occur in learning contexts are very different in nature from traditional “lookup” or “known item” searches: students usually perform searches to gather information about or master a certain topic, and the search engine is used as an aid in the exploration of a domain of knowledge. This paper presents SDE (Search Discover Explore), an exploratory search engine for educational resources that was built on top of the knowledge provided by Wikipedia: the set of its articles provides the search space (the set of topics that users can investigate), and the relationships between Wikipedia articles inform the suggestions that the search engine provides to students to go deeper in the exploration of a certain domain of knowledge. SDE indexes several hundreds of thousands of educational resources from high-quality Web sources, such as Project Gutenberg and Open Education Europe, among many others. This paper also reports the results of the evaluation of SDE by experts in Technology Enhanced Learning in several workshops that took place across Europe in the context of the European FP7 project iTEC. These results enable us to conclude that the exploratory search paradigm, making use of knowledge mined from Wikipedia, is a very promising approach for building information retrieval systems to be used in learning contexts. [ABSTRACT FROM AUTHOR]

The central argument of this paper is that the design, implementation and use of technologies that underpin general semantic search have implications for what we know and the way in which knowledge is understood. Semantic search is an assemblage of technologies that most Internet users would use regularly without necessarily realising. Users of search engines implementing semantic search can obtain answers to questions rather than just retrieve pages that include their search query. This paper critically examines the design of the Semantic Web, upon which semantic search is based. It demonstrates that implicit in the design of the Semantic Web are particular assumptions about the nature of classification and the nature of knowledge. The Semantic Web was intended for interoperability within specific domains. It is here argued that the extension to general semantic search, for use by the general public, has implications for what type of knowledge is visible and what counts as legitimate knowledge. The provision of a definitive answer to a query, via the reduction of discursive knowledge into machine-processable data, provides the illusion of objectivity and authority in a way that is increasingly impenetrable to critical scrutiny. [ABSTRACT FROM AUTHOR]

Web page ranking is one of the core components of search engines. Given a user query, ranking aims to provide a ranked list of Web pages that the user is likely to prefer the most. By and large, the ranking algorithms can be categorized into content-based approaches, link-based approaches, and hybrid approaches. Hybrid ranking algorithms, which exploit both the content and link information, are the most popular and extensively studied techniques. Among the hybrid algorithms, C-Rank combines content and link information in a very effective way using the concept of contribution. This algorithm is known to provide high performance in terms of both accurate and prompt responses to user queries. However, C-Rank suffers from very high costs to reflect the highly dynamic and extremely frequent changes in the World Wide Web, because it re-computes all of the C-Rank scores used for ranking from scratch to reflect the changes. As a result, C-Rank may be considered inappropriate to provide users with accurate and up-to-date search results. This paper aims to remedy this limitation of C-Rank. We propose incremental C-Rank , which is designed to update the C-Rank scores of only a carefully chosen portion of the Web pages rather than those of all of the Web pages without any accuracy loss. Our experimental results on a real-world dataset confirm both the effectiveness and efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

Query intents describe user information needs for searching on the web. How to capture the query intents is a crucial research topic in information retrieval. Search engine users always employ insufficient or unclear words as queries, thus making query intents ambiguous and uncertain to be interpreted by search engines. Query intent classification can deal with the problem by clarifying user queries and interpreting information needs for improving user satisfaction. Two main challenges have been addressed to classify query intents: one is how to effectively represent short and ambiguous queries; the other is how to generate a set of appropriate categories for matching diverse queries. In the paper, we propose a hybrid deep neural network model for query intent classification to meet the challenges. Our model adopts two state-of-the-art neural network models to comprehensively represent queries as feature vectors. We then employ query logs to automatically generate intermediate categories for fine-grained query intent clarification. Experimental results show that our method can outperform other baseline models, and effectively improve the performance in query intent classification. [ABSTRACT FROM AUTHOR]

Conventional approaches to relevance ranking typically optimize ranking models by each query separately. The traditional cluster hypothesis also does not consider the dependency between related queries. The goal of this paper is to leverage similar search intents to perform ranking consistency so that the search performance can be improved accordingly. Different from the previous supervised approach, which learns relevance by click‐through data, we propose a novel cocluster hypothesis to bridge the gap between relevance ranking and ranking consistency. A nearest‐neighbors test is also designed to measure the extent to which the cocluster hypothesis holds. Based on the hypothesis, we further propose a two‐stage unsupervised approach, in which two ranking heuristics and a cost function are developed to optimize the combination of consistency and uniqueness (or inconsistency). Extensive experiments have been conducted on a real and large‐scale search engine log. The experimental results not only verify the applicability of the proposed cocluster hypothesis but also show that our approach is effective in boosting the retrieval performance of the commercial search engine and reaches a comparable performance to the supervised approach. [ABSTRACT FROM AUTHOR]

Abstract While Information Retrieval (IR) systems have gained success in Web-style search engines in the past two decades, nonetheless, the DataBase (DB) paradigm remains prevalent in handling data in enterprise environments and digital libraries, and is gaining even more importance in the Semantic Web with the increasing need to handle partly structured (NoSQL) data. This paper describes SemIndex+ , a semantic-aware indexing and querying framework that allows semantic search, result selection, and result ranking of structured (relational DB-style), unstructured (IR-style), and partly structured (NoSQL) data. Various weighting functions and a parallelized search algorithm have been developed for that purpose and are presented here. We provide a general keyword query model allowing the user to choose the results' semantic coverage and expressiveness based on her needs. Different from alternative solutions involving query relaxation, query refinement, or query disambiguation, our approach incorporates semantics at the most basic data indexing level: providing more opportunities toward speedups and semantic coverage. An extensive experimental evaluation, comparing SemIndex+ with alternative methods, highlights our approach's flexibility and effectiveness, which in turn impact efficiency (requiring less or more time following the user specified index and query semantic coverages). Highlights • Search, select and rank unstructured, structured (relational) and partly structured (NoSQL) data. • Maps a textual data collection and a semantic knowledge base into a tightly-coupled semantic graph. • Involves users during semantic index creation, initial query formulation and query refinement. • Parallelized query processing, with a dedicated model for answer weighting and relevance scoring. • Comparative experiments with legacy methods highlight solution's flexibility & effectiveness. [ABSTRACT FROM AUTHOR]

Search users rely on result captions including titles, snippets, and URLs to decide whether they should read and click a particular result or not. Snippet usually serves as a query-dependent summary of its corresponding landing page and is therefore treated as one of the most important factors in search interaction process. Although there exist many efforts in improving snippet generation algorithms and incorporating more powerful interaction functions into snippets, little attention is paid to the effect of text highlighting in user behaviors. The highlighting of query terms in search snippets has been regarded as a matter of course and whether there exists a better way in snippet text highlighting remains uninvestigated. In this paper, we try to find out whether the default strategy of highlighting query terms employed by most commercial search engines is the best for search users. Through carefully designed experiments, we show that the retrieval efficiency can be affected by different term-highlighting strategies without changes in snippet contents. We also propose an automatic method which adopts CRF to learn to highlight terms based on word embedding, Wikipedia, and snippet content information. Experimental results show that the proposed method could predict highlighted terms selected by crowd workers with moderate performance. [ABSTRACT FROM AUTHOR]

Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm. [ABSTRACT FROM AUTHOR]

The need for information web-searching is needed by many users nowadays. They use the search engines to input their query or question and wait for the answer or best search results. As results to user query the search engines many times may be return irrelevant pages or not related to information need. This paper presents a proposed model to provide the user with efficient and effective result through search engine, based on modified chicken swarm algorithm and cosine similarity to eliminate and delete irrelevant pages(outliers) from the ranked list results, and to improve the results of the user's query . The proposed model is applied to Arabic dataset and use the ZAD corpus dataset for 27300 document. The experimental result shows that the proposed model improves the precision, recall, and accuracy. Thus the result produced by this method improves accuracy. [ABSTRACT FROM AUTHOR]

Recommender systems are changing the way that people find information, products, and even other people. This paper studies the problem of leveraging the context of the items presented to the user in a user/system interaction session to improve the recommender system's ranking prediction. We propose a novel model that incorporates the opportunity cost of giving up the other items in the session and computes session‐specific relevance values for items for context‐aware recommendation. The model can work on a variety of different problems settings with emphasis on implicit user feedback as it supports varying levels of ordinal relevance. Experimental evaluation demonstrates the advantages of our new model with respect to the ranking quality. [ABSTRACT FROM AUTHOR]

PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature—about two articles are added every minute on average—finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is statistical examination of nearly 7,000 web directories and an analysis of factors which affect their quality measured by PageRank. Design/methodology/approach – The authors analysed 6,821 directories registered at www.katalogiseo.info/. The following information about the directories was examined: the year of registration on the website, the directory’s PageRank value, the existence of an active IP address, backlink requests, a fee charged for submission to the directory, as well as directory moderation and subject. Statistical analyses were performed with the use of Microsoft Excel, version 2010, and R software, version 3.0.0. The PageRank values were collected with a software written in Python. Findings – The study has shown a gradual increase in popularity of directories as one of the basic tools in search engine optimisation. The analysis has indicated a relatively high percentage of spam web directories. The evidence of this is the number of directories with undetermined PageRank values. The study revealed that careful management of a directory and its subject have key impact on directory quality measured by PageRank. Originality/value – Relatively few publications focus on the problem of web directories which represent a very large group of websites created solely to manipulate web search engine rankings. This paper discusses the phenomenon of web directories, reveals the percentage of spam directories, and factors which affect their quality measured by PageRank. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to estimate Google search engine’s ranking function from a search engine optimization (SEO) perspective. Design/methodology/approach – The paper proposed an estimation function that defines the query match score of a search result as the weighted sum of scores from a limited set of factors. The search results for a query are re-ranked according to the query match scores. The effectiveness was measured by comparing the new ranks with the original ranks of search results. Findings – The proposed method achieved the best SEO effectiveness when using the top 20 search results for a query. The empirical results reveal that PageRank (PR) is the dominant factor in Google ranking function. The title follows as the second most important, and the snippet and the URL have roughly equal importance with variations among queries. Research limitations/implications – This study considered a limited set of ranking factors. The empirical results reveal that SEO effectiveness can be assessed by a simple estimation of ranking function even when the ranks of the new and original result sets are quite dissimilar. Practical implications – The findings indicate that web marketers should pay particular attention to a webpage’s PR, and then place the keyword in URL, the page title, and snippet. Originality/value – There have been ongoing concerns about how to formulate a simple strategy that can help a website get ranked higher in search engines. This study provides web marketers much needed empirical evidence about a simple way to foresee the ranking success of an SEO effort. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to develop a search engine dedicated to image retrieval in a bilingual (French and English) context. This paper presents the first phase of user testing that was carried out to validate and refine SINCERITY, the new search device. Design/methodology/approach – This first phase of the search engine testing involved a small group of image searchers (10 French-speaking and 10 English-speaking participants) who were asked to retrieve a sample of images (30) using the new tool. A questionnaire was also developed to compile the comments of the users. Findings – The results of this first phase of testing revealed that even though image indexing was sometimes problematic, the participants did not encounter major difficulties retrieving images with SINCERITY. Comments and suggestions received will be taken into consideration to improve the performance and aesthetics of the search engine. Originality/value – Once fully operational, SINCERITY will allow users to search images in an attractive and user-friendly manner. Eventually, other types of images (documentary and artistic) will be added to the image database linked to the image search engine, as well as other languages. [ABSTRACT FROM AUTHOR]