Relevant Search Results Research Articles

Context-Aware Search for Environmental Data Using Dense Retrieval

The search for environmental data typically involves lexical approaches, where query terms are matched with metadata records based on measures of term frequency. In contrast, dense retrieval approaches employ language models to comprehend the context and meaning of a query and provide relevant search results. However, for environmental data, this has not been researched and there are no corpora or evaluation datasets to fine-tune the models. This study demonstrates the adaptation of dense retrievers to the domain of climate-related scientific geodata. Four corpora containing text passages from various sources were used to train different dense retrievers. The domain-adapted dense retrievers are integrated into the search architecture of a standard metadata catalogue. To improve the search results further, we propose a spatial re-ranking stage after the initial retrieval phase to refine the results. The evaluation demonstrates superior performance compared to the baseline model commonly used in metadata catalogues (BM25). No clear trends in performance were discovered when comparing the results of the dense retrievers. Therefore, further investigation aspects are identified to finally enable a recommendation of the most suitable corpus composition.

INTEGRAÇÃO DA INTELIGÊNCIA ARTIFICIAL COM AS TÉCNICAS DE SEARCH ENGINE OPTIMIZATION

The internet has become an indispensable resource for accessing information. In this digital ecosystem, search engines play a crucial role in helping users find the most relevant content for their queries. This study aims to investigate how the application of Artificial Intelligence in content optimization can improve the relevance and accuracy of search results in Search Engine Optimization. Using an exploratory and descriptive methodology, the research explores the intersection between On-page and Off-page Search Engine Optimization techniques and AI, highlighting the application of Artificial Intelligence technologies such as natural language processing and machine learning. The results show that Artificial Intelligence has a significant impact on On-page Search Engine Optimization techniques, both in terms of the accuracy and relevance of search results. The research reveals that Artificial Intelligence can improve On-page Search Engine Optimization techniques, such as the optimization of Meta Tags and Title Tags, and On-page and Off-page Search Engine Optimization strategies. It is concluded that the integration of Artificial Intelligence into Search Engine Optimization strategies represents a significant evolution in digital marketing, offering more sophisticated and efficient methods for search engine optimization.

A comprehensive overview of the application of artificial intelligence in language learning

With the rapid advancement of artificial intelligence, AI technology has made significant progress in the field of language. Machine translation has become the dominant method, replacing manual translation due to its convenience and speed. This article will discuss three different aspects: translation, information retrieval, and language artificial intelligence. In the translation section, three distinct translation models will be analyzed, using Google Translate as a foundation. These models have transformed the translation industry and improved accuracy and efficiency. In the information retrieval section, the differences between semantic search involving AI and traditional keyword-based search techniques will be explored. Semantic search, driven by AI, provides more accurate and relevant search results by understanding the context and intent behind user queries. The impact of these advancements on search engine optimization (SEO) practices will also be discussed. Furthermore, the article will delve into the types of speech recognition and classify speech recognition technologies. Finally, the article will summarize the entire content and provide an outlook on future developments.

User versus institutional perspectives of metadata and searching: an investigation of online access to cultural heritage content during the COVID-19 pandemic

Findings from log analyses of user interactions with the digital content of two large national cultural heritage institutions (National Museums of Scotland and National Galleries of Scotland) during the COVID-19 lockdown highlighted limited engagement compared to pre-pandemic levels. Just 8% of users returned to these sites, whilst the average time spent, and number of pages accessed, were generally low. This prompted a user study to investigate the potential mismatch between the way content was indexed by the curators and searched for by users. A controlled experiment with ten participants, involving two tasks and a selected set of digital cultural heritage content, explored: (a) how does the metadata assigned by cultural heritage organisations meet or differ from the search needs of users? and (b) how can the search strategies of users inform the search pathways employed by cultural heritage organisations? Findings reveal that collection management standards like Spectrum encourage a variety of different characteristics to be considered when developing metadata, yet much of the content is left to the interpretations of curators. Rather, user- and context-specific guidelines could be beneficial in ensuring the aspects considered most important by consumers are indexed, thereby producing more relevant search results. A user-centred approach to designing cultural heritage websites would help to improve an individual’s experience when searching for information. However, a process is needed for institutions to form a concrete understanding of who their target users are before developing features and designs to suit their specific needs and interests.

Electronic health record impact on data quality: An integrated review

Few studies have assessed the impact of Electronic Health Record (EHR) on data quality and how it has impacted timeliness, accuracy, availability, integrity, consistency and uniqueness of health data. Assessing the current gaps between electronic health record utilisation in relation to enhancing data quality can help to identify and recommend key measures to improve healthcare delivery. This review examines the impact of electronic health record on data quality, and proposes recommendations desirable to improving healthcare delivery. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Integrative Review Frameworks (IRF) were adapted as a sign post for the study. These frameworks guided literature search, data analysis and presentation of findings. Boolean AND/OR operators were combined with search terms to increase relevant search results. Six (6) electronic databases that were used for literature search are Scopus, Web of Science, Science Direct, PubMed, ProQuest and Google Scholar. Searched articles were between 2012 to 2022, with the exception of two articles on the Technology Acceptance Model (TAM) published in 1986 and 1975. EHR implementation impact key health outcomes and contribute to timely and complete data submission. Implementation of Electronic Health Systems (EHS) embellish consistency, completeness, accuracy and integrity of clinical data significantly through embedded interoperability solutions. By reducing the scope of manual reviews, integrating a technology that automates data integrity and quality checks into EHR may potentially lessen the strain of maintaining data quality. Our findings depict that interoperability of clinical data sources will ensure consistent, timely and available health data when using EHR. The automated data integrity and consistency checks in the EHR improve data quality. The study contributes to advancing the theory of Technology Acceptance Model (TAM) in electronic health records and its impact on data quality in healthcare delivery.

Is your search query well-formed? A natural query understanding for patent prior art search

Recent advances in Deep Learning based prior art search has enabled the development of easy-to-use prior art search engines that accept natural language search queries and provide improved search performance. However, unlike conventional keyword-based techniques where the results are readily interpreted by the presence of queried keywords, Deep Learning based techniques act like a black box. As a result, it is difficult for users to articulate their information in order to obtain optimal results. In this paper, we share insights on query well-formedness from extensive experimentation with PQAI,11https://projectpq.ai/. an open source Deep Learning based prior art search engine. We study the effects of various query parameters such as grammar, specificity, and verbosity on the search results and show that ill-formed queries containing grammatical errors, non-essential content, and broad terminology adversely affect the relevance of search results. We also develop a number of Machine Learning models, viz. Grammatical Error Detection Model (GEDM), Query Specificity Model (QSM), and Query Verbosity Model (QVM), to identify and mitigate commonly encountered issues with ill-formed queries. The data, survey forms, and code relating to this work will be released to the community22https://github.com/Renuk9390/Query_formedness_PQAI.. Towards future breakthroughs, critical areas of query understanding in prior art search for advancing research are given in the end.

Research on methods and applications of question answering system in the context of ChatGPT

In the 21st century, there has been a growing importance placed on the "body" of artificial intelligence, particularly as it relates to language processing. Researchers have developed various machine learning models with a focus on language understanding, including Large Language Model (LLM), Bidirectional Encoder Representation from Transformers (BERT), and Natural Language Processing (NLP). These models have led to the development of numerous applications, such as ChatGPT-3.5, which has recently gained widespread attention. In addition to ChatGPT, other applications have also benefited from these language processing models, including Question Answering Systems (QAS). This paper will examine three QAS that have been enhanced by the context of ChatGPT, discuss the relevant applications, and analyze these different applications in order to predict future trends in this field. One notable QAS is OpenAI's GPT-3-powered AI that can answer questions about any topic. This application leverages the capabilities of GPT-3 to provide accurate and informative responses to a wide range of questions. Another QAS is IBM's Watson, which utilizes natural language processing and machine learning algorithms to understand and respond to user queries. Watson has been used in various industries, including healthcare, finance, and retail. A third QAS is Google's BERT-based system, which uses pre-trained language models to improve its responses to user queries. This system has been integrated into Google Search and other products, allowing users to receive more precise and relevant search results. Overall, the development of these QAS and other language processing applications marks an exciting period of progress in the field of artificial intelligence. As researchers continue to refine these models and explore new applications, we can expect to see even more advanced and sophisticated language processing systems emerge in the future.

Harmonized Research Information for Classifying and Linking Research Data

A well-described, harmonized in standard formats and linked metadata accelerates and refines the search process. Hence, researchers can ensure that their data is properly organized, easily discoverable, and accessible to others. Machine-readable metadata is especially essential for automatic discovery of research datasets and outputs. It facilitates the search and retrieval of data with increased accuracy, which saves the user time in finding crucial information within their field of research. This requires rich and categorized metadata that allows computers to automatically retrieve and sort relevant search results. Well documented metadata which is harmonized, standardized and serialized in linked data formats increases not only the accuracy of the search results within a field of research, but can also provide relevant and helpful suggestions within disciplinary and interdisciplinary fields of research.

Tourism Destination Article Search Features using TF-IDF and Cosine similarity

In the current digital era, the increasing public interest in searching for information about travel destinations necessitates an effective and accurate search system. However, search results for travel destination articles often yield irrelevant or inadequate outcomes. To address this issue, this paper proposes applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and Cosine Similarity in the search feature for travel destination articles. By employing these algorithms, the search system is anticipated to deliver more relevant and accurate results according to user needs.
 This research contributes to developing an effective search system for travel destination articles, assisting users in obtaining relevant and high-quality information about the destinations they are searching for. The methodology involves collecting data on travel destination articles, implementing the TF-IDF algorithm to evaluate term importance, and utilizing Cosine Similarity to measure the similarity between articles and user queries.
 The study results demonstrate that implementing the TF-IDF algorithm and Cosine Similarity in the search feature for travel destination articles enhances the accuracy and relevance of search results. Users can quickly discover articles that align with their queries, improving their search experience. In conclusion, this research highlights that applying the TF-IDF algorithm and Cosine Similarity in the search feature significantly improves the accuracy and relevance of search results for travel destination articles. This enhances the search experience for users seeking information about travel destinations.

Transient ischemic attack and minor stroke as "surgeons affairs": a narrative review.

The scope of this paper is to review the subtypes of transient ischemic attack (TIA) and minor stroke (mS) in which a surgical treatment is needed, discussing the importance and the timing of a multidisciplinary approach, in order to achieve an optimized management and prevent major strokes or other critical complications. The keywords "transient ischemic attack," "minor stroke," "surgical treatment," "vascular surgery," "heart surgery," "neurosurgery," and "multidisciplinary" were searched using MEDLINE, EMBASE, and Scopus. Relevant search results were discussed by the authors for references inclusion. Notwithstanding that best medical therapy is usually the first choice for the most part of cases, there are specific but recurrent etiologies that must be properly recognized because of a potential surgical approach, even in urgency. In fact, symptomatic carotid stenosis, or particular cases of hemodynamic cerebrovascular events, should be promptly referred to vascular surgeon, since increasing evidences highlighted a benefit from an early artery revascularization. In addition, beyond arrhythmic causes, cardioembolic events due to bacterial endocarditis and atrial myxoma should be quickly diagnosed, possibly in emergency department, because they are a presumptive urgency for heart surgery. In addition to the above-mentioned conditions, in patients suffering from vertebrobasilar TIA or mS, clinicians should keep in mind the Bow Hunter disease, because surgical artery decompression can represent the only suitable treatment in selected cases. TIA and mS require a multidisciplinary in order to discuss therapeutic options, comparing risks and benefits and determining the best timing for an optimized management.

Telehealth services for global emergencies: implications for COVID-19: a scoping review based on current evidence

IntroductionThe availability of low-cost computing and digital telecommunication in the 1980s made telehealth practicable. Telehealth has the capacity to improve healthcare access and outcomes for patients while reducing healthcare costs across a wide range of health conditions and situations.ObjectiveThis study compares the adoption, advantages, and challenges of telehealth services between high-income (HICs) and low-and-middle-income countries (LMICs) before and during the COVID-19 pandemic.MethodsPreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The key search terms were: “Telehealth”, “Telehealth in HICs”, “Telehealth in LMICs”, “Telehealth before COVID-19”, “Telehealth during COVID-19”. We searched exhaustively ProQuest, Scopus, Web of Science, Google Scholar, CINAHL, and EMBASE databases from 2012. Booleans OR/AND were combined with key search terms to increase relevant search results. The literature search and selection process followed the Sample, Phenomena of Interest, Design, Evaluation, and Research (SPIDER) question format.ResultsThe adoption of telehealth before COVID-19 was generally low in both HICs and LMICs. The impact of COVID-19 accelerated the adoption of telehealth at the facility level but not nationwide in both high-income countries and LMICs. The rapid adoption of telehealth at the facility level in both high-income and LMICs introduced several challenges that are unique to each country and need to be addressed.ConclusionThe lack of national policies and regulations is making the adoption of telehealth at the national level challenging in both high and low-middle-income countries. Governments and Stakeholders of healthcare must consider telehealth as a healthcare procedure that should be deployed in clinical working procedures. Primary quantitative and qualitative studies must be conducted to address challenges encountered during the pilot implementation of telehealth services in both high-income countries and LMICs before and during pandemics.

Librarian and Researcher Assessments of Search Result Relevance: How Well Do They Align?

Conducting comprehensive but efficient literature searches for complex evidence syntheses involves selecting databases that will retrieve the greatest number of relevant results on the question. Lack of a comprehensive single database on allied health educational topics challenges those seeking such literature. In this study, six participants contributed research questions on instructional methods and materials for allied health patients, caregivers, and future health professionals. Two health sciences librarians created search strategies for these questions and searched eleven databases. Both the librarians and the six participants evaluated the search results using a rubric based on PICO to assess extent of alignment between the librarians’ and requestors’ relevance judgments. Intervention, Outcome, and Assessment Method constituted the most frequent bases for assessments of relevance by both librarians and participants. The librarians were more restrictive in all of their assessments except in a preliminary search yielding twelve citations without abstracts. The study’s results could be used to identify effective techniques for reference interviewing, selecting databases, and weeding search results.

Emerging Gene Manipulation Strategies for the Treatment of Monogenic Eye Disease.

Genetic eye diseases, representing a wide spectrum of simple and complex conditions, are one of the leading causes of visual loss in children and working adults, and progress in the field has led to changes in disease investigation, diagnosis, and management. The past 15 years have seen the emergence of novel therapies for these previously untreatable conditions to the extent that we now have a licensed therapy for one form of genetic eye disease and many more in clinical trial. This is a systematic review of published and ongoing clinical trials of gene therapies for monogenic eye diseases. Databases of clinical trials and the published literature were searched for interventional studies of gene therapies for eye diseases. Standard methodological procedures were used to assess the relevance of search results. A total of 59 registered clinical trials are referenced, showing the significant level of interest in the potential for translation of these therapies from bench to bedside. The breadth of therapy design is encouraging, providing multiple possible therapeutic mechanisms. Some fundamental questions regarding gene therapy for genetic eye diseases remain, such as optimal dosing, the relative benefits of adeno-associated virus (AAV)-packaging and the potential for a significant inflammatory response to the therapy itself. As a result, despite the promise of the eye as a target, it has proven difficult to deliver clinically effective gene therapies to the eye. Despite setbacks, the licensing of Luxturna (voretigene neparvovec, Novartis) for the treatment of RPE65-mediated Leber congenital amaurosis (LCA) is a major advance in efforts to treat these rare, but devastating, causes of visual loss.

MODELS OF NATURAL LANGUAGE PROCESSING FOR IMPROVING SEMANTIC SEARCH RESULTS

Most people rely on search engines to get and share information from all sorts of resources. All totals returned by search engines are not always important because they are drawn from heterogeneous data sources. Moreover, it is not easy for a user to prove that the acquired results are relevant for the request. As a result, the semantic network plays a significant role in interpreting the relevance of search results. This paper proposes a new method for searching for appropriate documents using the semantic web, based on the concept of natural language processing (NLP). In the proposed system, NLP is used to analyze a user request in terms of parts of speech. The extracted definitions are compared with a domain dictionary to identify the appropriate domain of the user's interest. The retrieved user request papers are examined with natural language processing support to identify the respective domain. Keywords: semantic search engine, natural language processing, context analysis, information retrieval systems, graphematic analysis, ontology

Structured encryption for knowledge graphs

We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents—symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational graph (MG) and property graph (PG)), and then all encrypted nodes and edges in a KG constitute the entire index table and database. This paper proposes the first STE for KGs with CQA2-security to search on protected knowledge, where KGs include MGs and PGs. In general, the latter is more complex than the former, but it can represent more abundant knowledge. Experimental results show that the index construction time of our schemes is about 1.9s and the query time is about 190 ms. Our sensitivity analysis shows that the performance of our proposed schemes is greatly influenced by the number of edges and nodes, but less by the number of properties.

Artificial intelligence behind the scenes: PubMed's Best Match algorithm.

This article focuses on PubMed's Best Match sorting algorithm, presenting a simplified explanation of how it operates and highlighting how artificial intelligence affects search results in ways that are not seen by users. We further discuss user search behaviors and the ethical implications of algorithms, specifically for health care practitioners. PubMed recently began using artificial intelligence to improve the sorting of search results using a Best Match option. In 2020, PubMed deployed this algorithm as the default search method, necessitating serious discussion around the ethics of this and similar algorithms, as users do not always know when an algorithm uses artificial intelligence, what artificial intelligence is, and how it may impact their everyday tasks. These implications resonate strongly in health care, in which the speed and relevancy of search results is crucial but does not negate the importance of a lack of bias in how those search results are selected or presented to the user. As a health care provider will not often venture past the first few results in search of a clinical decision, will Best Match help them find the answers they need more quickly? Or will the algorithm bias their results, leading to the potential suppression of more recent or relevant results?

Ensuring Safety of Forensic Medical Examiners during Autopsy of Suspected or Detected Cases of Deadly Communicable Diseases

Background: Forensic medical examiners are known to be exposed to a combined effect of occupational risk factors potentially leading to adverse health outcomes. In the current epidemiologic situation largely influenced by the ongoing COVID-19 pandemic, postmortem examination of the bodies having signs of deadly communicable diseases, including the novel coronavirus disease, poses a high infection risk, thus making the development and implementation of appropriate preventive measures an urgent and important task. Objective: To summarize and systematize the results of scientific research and requirements of legal and regulatory documents in the field of ensuring workplace safety of forensic medical examiners. Materials and methods: We searched for literary sources published in Russian and English in 2000–2021 in the PubMed, eLibrary, and Cyberleninka databases using such keywords as “healthcare workers”, “safety”, “forensic medical examiner”, “prosector”, and “deadly infections”. We conducted an analytical review of legal documents regulating safety in the conduct of forensic medical examination. Based on the relevance of search results, we selected 30 articles and five legal and regulatory documents for this review and carried out their content analysis using descriptive and analytical methods. Results: As a solution to the problem of epidemiological surveillance, a series of modular buildings for postmortem examination of patients with suspected or confirmed deadly infectious diseases, including COVID-19, was developed and introduced. Conclusions: Our findings indicate that in the context of the current pandemic, it is critical to update the requirements for biological safety of forensic experts stipulated in sanitary rules and norms. Construction of modular special purpose buildings in the city of Kazan contributed to improvement of biological safety during autopsy of COVID-19 victims.

Search results diversification for effective fair ranking in academic search

Providing users with relevant search results has been the primary focus of information retrieval research. However, focusing on relevance alone can lead to undesirable side effects. For example, small differences between the relevance scores of documents that are ranked by relevance alone can result in large differences in the exposure that the authors of relevant documents receive, i.e., the likelihood that the documents will be seen by searchers. Therefore, developing fair ranking techniques to try to ensure that search results are not dominated, for example, by certain information sources is of growing interest, to mitigate against such biases. In this work, we argue that generating fair rankings can be cast as a search results diversification problem across a number of assumed fairness groups, where groups can represent the demographics or other characteristics of information sources. In the context of academic search, as in the TREC Fair Ranking Track, which aims to be fair to unknown groups of authors, we evaluate three well-known search results diversification approaches from the literature to generate rankings that are fair to multiple assumed fairness groups, e.g. early-career researchers vs. highly-experienced authors. Our experiments on the 2019 and 2020 TREC datasets show that explicit search results diversification is a viable approach for generating effective rankings that are fair to information sources. In particular, we show that building on xQuAD diversification as a fairness component can result in a significant (p<0.05) increase (up to 50% in our experiments) in the fairness of exposure that authors from unknown protected groups receive.

A Survey of Query Expansion Methods to Improve Relevant Search Engine Results

Due to large volumes of documents available for retrieval in a search database, an intelligent method is required to retrieve relevant search results. Query expansion is one of such methods widely used in retrieving pertinent results of various search domains. The increased amount of information stored in a search engine database requires the use of query expansion. A query expansion deals with expanding the query by adding additional information to the query for effective retrieving relevant results. Recently, many query expansion techniques have been proposed to addresses the vocabulary mismatch problem that may arise in the information retrieval system. However, these techniques still have low precision results. This paper presents a systematic review of query expansion research from 1999 to 2018. The paper reviewed and discussed 573 research papers on query expansion methods and their application areas. It focuses only on the query expansion in text retrieval of search engines. This review's primary goal is to provide a broad overview of query expansion research and view how research approaches changed. The research paper analyzed and presented the contributions of each query expansion study. It also identifies major application areas of query expansions and their future opportunities. The finding of this study indicates a trend towards using semantic-ontology and pseudo-relevant feedbacks methods. This work will be beneficial to query expansion researchers in extending future work on query expansion research.

Transformation Approach of Open Web Data to Linked Open Data

A large amount of data available over the Web and, in particular, the open data have, generally, heterogeneous formats and are not machine-readable. One promising solution to overcome the problems of heterogeneity and automatic interpretation is the Linked Data initiative, which aims to provide unified practices for publishing and contextually to link data on the Web, by using World Wide Web Consortium standards and the Semantic Web technologies. LinkedIn data promote the Web’s transformation from a web of documents to a web of data, ensuring that machines and software agents can interpret the semantics of data correctly and therefore infer new facts and return relevant web data search results.&#x0D; This paper presents an automatic generic transformation approach that manipulates several input formats of open web data to linked open data. This work aims to participate actively in the movement of publishing data compliant with linked data principles.