Advanced Information Retrieval Research Articles

GPDminer: a tool for extracting named entities and analyzing relations in biological literature

PurposeThe expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers.MethodsGPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction.ResultsThe implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images.ConclusionGPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management.

Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

Search engines are now the main source for information retrieval due to the hugeexpansion of data on the internet over the last ten years. Providing users with the mostrelevant results for their queries poses a significant challenge for search engines.Semantic search engines, which go beyond traditional keyword-based searches, haveappeared as advanced information retrieval systems to address this problem. Thesesearch engines produce more precise and pertinent search results because theyunderstand the meanings of words and their relationships. They play a pivotal role inmanaging the vast amount of internet data, with a primary aim of enhancing searchprecision and user satisfaction. However, improving search precision remains as animportant goal for natural language processing researchers. The main objective of ourresearch is to improve the search engine results. We present a novel approach formeasuring the similarity between a user’s query and a list of documents within a searchengine. This approach provides a new fuzzy recommendation system using a syntacticand semantic similarity. Our results indicate that our method outperforms severalexisting approaches from the literature, achieving a high level of accuracy.

Report on the 1st Workshop on Query Performance Prediction and Its Evaluation in New Tasks (QPP++ 2023) at ECIR 2023

Query Performance Prediction (QPP) is currently primarily applied to ad-hoc retrieval tasks. The Information Retrieval (IR) field is reaching new heights thanks to recent advances in large language models and neural networks, as well as emerging new ways of searching, such as conversational search. Such advancements are quickly spreading to adjacent research areas, including QPP, necessitating a reconsideration of how we perform and evaluate QPP. This workshop sought to elicit discussion on three topics related to the future of QPP: exploiting advances in IR to improve QPP, instantiating QPP on new search paradigms, and evaluating QPP on new tasks. Date: 6 April 2023. Website: https://qpp.dei.unipd.it/.

Towards unsupervised keyphrase extraction via an autoregressive approach

Keyphrase extraction is a technique used to capture the core information of documents and is an upstream task for advanced information retrieval systems, particularly in the academic realm. Current unsupervised methods are primarily built on a score-and-rank framework with a consistent inability to acquire mutual information between extracted keyphrases, especially with graph-based models. Utilizing the autoregressive structure that is typically used in sequence-to-sequence text generation models, we propose a plug-and-play optimizer named C-Decay that can be integrated into any graph-based unsupervised keyphrase extraction model for a stable performance boost, and that mitigates the bias of certain semantically or lexically dominant tokens by optimizing the origin score distribution output by graph-based models directly. The architecture of C-Decay includes the keyphrase pool, the gain vector and the decay factor, where the keyphrase pool is designed to realize an autoregressive structure and the gain vector and the decay factor are the optimization operator. Herein, we examine three graph-based models integrated with C-Decay, and the experiment is conducted on four datasets KDD, Semeval, Nguyen, and Krapivin. Moreover, we prove that C-Decay can improve accuracy and F-Measure by an average of approximately 50% and 20%, respectively.

Differentiable SAR Renderer and Image-Based Target Reconstruction.

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in the optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt at inverse graphics for SAR imagery. A differentiable SAR renderer (DSR) is developed, which reformulates the mapping and projection algorithm of the SAR imaging mechanism in the differentiable form of probability maps. First-order gradients of the proposed DSR are then analytically derived, which can be back-propagated from rendered image/silhouette to the target geometry and scattering attributes. A 3D inverse target reconstruction algorithm from SAR images is devised. Several simulation and reconstruction experiments are conducted, including targets with and without background, using synthesized data or real measured inverse SAR (ISAR) data by ground radar. Results demonstrate the efficacy of the proposed DSR and its inverse approach.

Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances

Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances

Development of a management system for an R&D terminology dictionary

To provide advanced information retrieval and intelligent services using AI and big data processing technologies in R&D sectors, an R&D terminology dictionary that reflects classification information in national R&D fields is required. This study proposes the R&D terminology dictionary construction processes and develops a management system based on these processes. The proposed system supports efficient terminology extraction and cleansing by applying step-by-step processes for establishing R&D terms that reflect research field information using keywords and the Science and Technology Standard Classification of national R&D projects. After automatically extracting and cleansing keywords from the project information, the frequency of each term is calculated. Subsequently, a terminology dictionary is automatically constructed based on these frequencies, and term searching and merging via cleansing criteria functions are utilized for terms subject to manual cleansing. At NTIS(National Science &Technology Information Service), information concerning approximately 57,000 projects is collected yearly. Therefore, the step-by-step terminology dictionary construction and cleansing processes of the R&D terminology dictionary management system proposed in this study can be used to include new terms that occur each year. The proposed system enables users to select the terms related to the desired R&D field of R&D information via the search function of the developed terminology dictionary and perform an extended search using Korean-English terms or related words. As such, the system allows the users to accurately and efficiently find desired information.

Privacy-Aware Content-of-Interest Search and Recommendation in Internet of Things for Cross-Dressers

With the continuous development and gradual progress of Internet of Things (IoT) in human society, people are becoming increasingly diverse in terms of user preferences and things choices. In this situation, several new cultures or social phenomena have been emerging including the so-called Cross-dressing culture. As a special group of humans, cross-dressers are often very sensitive to their non-mainstream identities. Therefore, they are often confronted with more difficulties when using some modern information techniques such as Content-of-Interest (COI) search. Motivated by this fact, we introduce some advanced information retrieval and privacy protection techniques into the cross-dressing domain and further propose a privacy-aware COI search and recommendation solution for cross-dressers, named PCSR. First, PCSR uses fastText tool to transform the cross-dressers’ input keywords and the candidate webpages into corresponding vectors with less private content associated with cross-dressers. Afterwards, we use vector similarity calculation techniques to make privacy-preserving COI search and recommendation. At last, we validate the effectiveness of PCSR through a set of experiments. We believe that our proposed PCSR solution can benefit the cross-dressers significantly when performing COI search and recommendation in IoT while protecting sensitive information of cross-dressers.

Feature location enhancement based on source code augmentation with synonyms of terms

SummaryIn software maintenance the developers may add new feature to program, improve existing function, and remove bugs. In this case the developer should identify the location in the source code that corresponds to a specific functionality; this is known as feature location. This presents a new approach for enhancing the process of feature location using Information Retrieval (IR) and Natural Language Processing. The approach presented augments the source code with additional semantic information that was extracted and derived from the synonyms of source code terms. This approach works in a pipeline structure, starting by augmenting the source code corpus with synonyms of the original terms and ending by inferring the source code with a particular user query. More specifically, the WordNet platform is used for extracting the synonyms of terms. Moreover, the approach uses an advanced IR technique, namely the Latent Semantic Indexing, for searching and inferring the source code. The used approach was tested and evaluated on two open source systems, namely the Qt and Hippodraw. Four experiments were conducted on each system using 21 features and the results showed that enriching the source code with synonyms of terms clearly and significantly improved the process of feature location efficiently. The experimental results showed that the approach presented obtained higher levels of Recall and Precision. For that reason, it has been shown to improve the state‐of‐the‐art techniques for feature location process.

An Integrated Approach to 3D Web Visualization of Cultural Heritage Heterogeneous Datasets

The evolution of the high-quality 3D archaeological representations from niche products to integrated online media has not yet been completed. Digital archives of the field often lack multimodal data interoperability, user interaction and intelligibility. A web-based cultural heritage archive that compensates for these issues is presented in this paper. The multi-resolution 3D models constitute the core of the visualization on top of which supportive documentation data and multimedia content are spatial and logical connected. Our holistic approach focuses on the dynamic manipulation of the 3D scene through the development of advanced navigation mechanisms and information retrieval tools. Users parse the multi-modal content in a geo-referenced way through interactive annotation systems over cultural points of interest and automatic narrative tours. Multiple 3D and 2D viewpoints are enabled in real-time to support data inspection. The implementation exploits front-end programming languages, 3D graphic libraries and visualization frameworks to handle efficiently the asynchronous operations and preserve the initial assets’ accuracy. The choice of Greece’s Meteora, UNESCO world site, as a case study accounts for the platform’s applicability to complex geometries and large-scale historical environments.

Music Instrument Recognition from Spectrogram Images using Convolution Neural Network

The projected system presents a unique approach to instrument Recognition (MIR) supported Convolution Neural Networks (CNNs). Previous MIR strategies are supported planning and extracting spectrogram features from the audio signal so as to explain its characteristics and classify it. In distinction, CNNs learn the features directly from the input file. The model has evidenced successful in solving various advanced multimedia system information Retrieval (MIR) issues, like image classification or voice recognition. The projected system seeks to explore whether or not the success may be imported to MIR also. The results from this work shows 97% accuracy for all instrument classes.

Exploring How Evidence is Used in Care Through an Organizational Ethnography of Two Teaching Hospitals.

BackgroundNumerous published articles show that clinicians do not follow clinical practice guidelines (CPGs). However, a few studies explore what clinicians consider evidence and how they use different forms of evidence in their care decisions. Many of these existing studies occurred before the advent of smartphones and advanced Web-based information retrieval technologies. It is important to understand how these new technologies influence the ways clinicians use evidence in their clinical practice. Mindlines are a concept that explores how clinicians draw on different sources of information (including context, experience, medical training, and evidence) to develop collectively reinforced, internalized tacit guidelines.ObjectiveThe aim of this paper was to explore how evidence is integrated into mindline development and the everyday use of mindlines and evidence in care.MethodsWe draw on ethnographic data collected by shadowing internal medicine teams at 2 teaching hospitals. Fieldnotes were tagged by evidence category, teaching and care, and role of the person referencing evidence. Counts of these tags were integrated with fieldnote vignettes and memos. The findings were verified with an advisory council and through member checks.ResultsCPGs represent just one of several sources of evidence used when making care decisions. Some forms of evidence were predominately invoked from mindlines, whereas other forms were read to supplement mindlines. The majority of scientific evidence was accessed on the Web, often through smartphones. How evidence was used varied by role. As team members gained experience, they increasingly incorporated evidence into their mindlines. Evidence was often blended together to arrive at shared understandings and approaches to patient care that included ways to filter evidence.ConclusionsThis paper outlines one way through which the ethos of evidence-based medicine has been incorporated into the daily work of care. Here, multiple Web-based forms of evidence were mixed with other information. This is different from the way that is often articulated by health administrators and policy makers whereby clinical practice guideline adherence is equated with practicing evidence-based medicine.

Increased cerebral nuclear factor kappa B in a complex regional pain syndrome rat model: possible relationship between peripheral injury and the brain.

PurposeComplex regional pain syndrome (CRPS) is a rare but refractory pain disorder. Recent advanced information retrieval studies using text-mining and network analysis have suggested nuclear factor kappa B (NFκB) as a possible central mediator of CRPS. The brain is also known to play important roles in CRPS. The aim of this study was to evaluate changes in cerebral NFκB in rats with CRPS.Materials and methodsThe chronic post-ischemia perfusion (CPIP) model was used as the CRPS animal model. O-rings were applied to the left hind paws of the rats. The rats were categorized into three groups according to the results of behavioral tests: the CPIP-positive (A) group, the CPIP-negative (B) group, and the control (C) group. Three weeks after the CPIP procedure, the right cerebrums of the animals were harvested to measure NFκB levels using an ELISA.ResultsAnimals in group A had significantly decreased mechanical pain thresholds (P<0.01) and significantly increased cerebral NFκB when compared to those in groups B and C (P=0.024).ConclusionThis finding indicates that peripheral injury increases cerebral NFκB levels and implies that minor peripheral injury can lead to the activation of pain-related cerebral processes in CRPS.

A SEMANTIC GRAPH DATABASE FOR BIM-GIS INTEGRATED INFORMATION MODEL FOR AN INTELLIGENT URBAN MOBILITY WEB APPLICATION

Abstract. Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology.

Breaking the rules. Ir and Business

The emergence of advanced information retrieval technologies has caused a drastic change in business models, in the nature of information intermediaries and in its automated processing. The characteristics of these changes and their economic consequences are analyzed.

CHIS: A big data infrastructure to manage digital cultural items

In this paper, we describe CHIS (Cultural Heritage Information System), a big data infrastructure that can be used to query, browse, analyze and process digital contents related to cultural heritage from a set of heterogeneous and distributed repositories. CHIS is characterized by the following technical features: capability to gather information from distributed and heterogeneous data sources (e.g., Sensor Networks, Social Media Networks, Digital Libraries and Archives, Multimedia Collections, Web Data Services, etc.); advanced data management techniques and technologies; ability to provide useful and personalized data to users based on their preferences and context; advanced information retrieval facilities, data analytics and other utilities/services, according to the SOA paradigm. By means of a set of ad-hoc APIs, and value-added data processing and analytics services, our system can support several applications: mobile multimedia guides for cultural environments, web portals to promote the cultural heritage of a given organization, multimedia recommender and storytelling systems and so on. We discuss the main ideas that characterize the system, showing its use for several applications.

Automating Clinical Score Calculation within the Electronic Health Record. A Feasibility Assessment.

Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records. We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings. We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable. Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments - partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study.

An Overview of Bioinformatics Tools and Resources in Allergy.

The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens.

A Preliminary Study on SAR Advanced Information Retrieval and Scene Reconstruction

Rapid advances in synthetic aperture radar (SAR) technologies have brought challenges in image interpretation toward the development of new Earth observation applications. Both novel scattering–imaging models and intelligent inversion techniques are required for advanced information retrieval and interpretation of high-resolution multidimension and multimode SAR data. As an example, scene reconstruction attempts to transfer the SAR image to human-understandable representation of man-made targets and natural environment. In this letter, a framework for scene reconstruction is outlined. It includes three key elements: a dictionary of parametric scatterer model, a method for scatterer recognition and parameter estimation, and a method for target reconstruction. A preliminary case is presented, where a simulated 3-D SAR image of a simple target is successfully reconstructed to a solid geometry. It uses a novel surface extension method to connect isolated scatterers to form a complete target geometry.

The Notion of Relevance in Information Science: Everybody knows what relevance is. But, what is it really?

Abstract Everybody knows what relevance is. It is a ya'know notion, concept, idea–no need to explain whatsoever. Searching for relevant information using information technology (IT) became a ubiquitous activity in contemporary information society. Relevant information means information that pertains to the matter or problem at hand—it is directly connected with effective communication. The purpose of this book is to trace the evolution and with it the history of thinking and research on relevance in information science and related fields from the human point of view. The objective is to synthesize what we have learned about relevance in several decades of investigation about the notion in information science. This book deals with how people deal with relevance—it does not cover how systems deal with relevance; it does not deal with algorithms. Spurred by advances in information retrieval (IR) and information systems of various kinds in handling of relevance, a number of basic questions are raised: But w...