Integrated Information Retrieval Research Articles

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

In the aftermath of the COVID-19 pandemic, using web platforms as a communication medium and decision-making tool in online commerce has become widely acknowledged. User-generated comments, reflecting positive and negative sentiments towards specific items, serve as invaluable indicators, offering recommendations for product and organizational improvements. Consequently, the extraction of suggestions from mined opinions can enhance the efficacy of companies and organizations in this domain. Prevailing research in suggestion mining predominantly employs rule-based methodologies and statistical classifiers, relying on manually identified features. However, a recent trend has emerged wherein researchers explore solutions grounded in deep learning tools and techniques. This study aims to employ information retrieval techniques for the automated identification of suggestions. To this end, various methodologies, including distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks utilizing TF-IDF, Bag of Words (BOW), and Word2Vec vectors, along with keyword extraction, have been integrated. The proposed approach is assessed using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews. The obtained results demonstrate a notable enhancement in the F1 score, reaching 0.76 compared to prior research. The experiments further suggest that information retrieval-based approaches exhibit promising potential for this specific task.

A building regulation question answering system: A deep learning methodology

Regulations play an important role in assuring the quality of a building’s construction and minimizing its adverse environmental impacts. Engineers and the like need to retrieve regulatory information to ensure a building conforms to specified standards. Despite the availability of search engines and digital databases that can be used to store regulations, engineers, for example, are unable to retrieve information for domain-specific needs in a timely manner. As a consequence, users often have to deal with the burden of browsing and filtering information, which can be a time-consuming process. This research develops a robust end-to-end methodology to improve the efficiency and effectiveness of retrieving queries pertaining to building regulations. The developed methodology integrates information retrieval with a deep learning model of Natural Language Processing (NLP) to provide precise and rapid answers to user’s questions from a collection of building regulations. The methodology is evaluated and a prototype system to retrieve queries is developed. The paper’s contribution is therefore twofold as it develops a: (1) methodology that combines NLP and deep learning to be able to address queries raised about the building regulations; and (2) chatbot of question answering system, which we refer to as QAS4CQAR. Our proposed methodology has powerful feature representation and learning capability and therefore can potentially be adopted to building regulations in other jurisdictions.

Modern activity of the national police in counteracting economic crimes in the basic branches of the economy of Ukraine (agro-industrial complex)

It is emphasized that economic crimes affect all types of economic activity, are reflected in various spheres of society. Not only intentional encroachments on property have increased, but also other crimes related to economic activity: economic abuse, abuse of power by employees of institutions, enterprises, organizations, "bribery", fraud, forgery of documents. The main burden of combating economic crime falls on the National Police, and the priority role in the prevention of economic crime belongs to the criminal police, which includes the Department of Strategic Investigations. The effectiveness of prevention of economic crimes is ensured by high-quality information and analytical activities, the presence of scientifically sound prognostic estimates of high probability. The police should assess the following indicators: the actual level and trends of the criminal busi-ness, both parasitic on the legal economy and that which exists in illegal economic activities; the share of criminal communities in it; comparative intensity of fraud by areas and territories. The objects of preventive activities of the National Police should be: negative phenomena and processes that contribute to the commission of specific economic and tax crimes or facilitate their commission; persons planning or preparing to commit economic crimes; persons in respect of whom the National Police controls their behavior in cases provided by law; persons with persistent illegal behavior and who have previously been prosecuted for economic crimes; persons who are prosecuted for the commission of these crimes, as well as suspects or accused of committing economic crimes, if the criminal proceedings are terminated due to non-rehabilitative circumstances, as well as persons in respect of whom criminal proceedings were refused on the same grounds. The modern concept of combating economic and organized crime should be based on a strictly developed methodology for comprehensive analysis of certain features as a result of criminal analysis of a large amount of data obtained from various sources, including integrated information retrieval systems of the National Police of Ukraine.

Informational basis of law enforcement bodies activity

The modern system of law enforcement agencies, like many other state institutions, operates in an open and closed information space and dynamic information threats and opportunities, so it is important to maximize the use of tools and opportunities to work with information and to develop skills of analysing it. The aim of the article is to analyze the information component, and to search the directions for its development to ensure the effectiveness of law enforcement. In the article it is substantiated the need and features of integrated approach to information support of law enforcement activity from the point of combining different channels of receiving the information. There are characterized possibilities and directions of using the information retrieval system of the Ministry of Internal Affairs, which was created to unite information resources operating in the bodies and departments of Ukraine’s internal affairs bodies, into a single information and analytical complex using modern information technologies, computer and telecommunication equipment to support the operational activities of the bodies and department of internal affairs. The special features of working with open databases are outlined, in particular with the Unified state web portal of open data, which is formed on the basis of a combination of dataset search within different groups. It was identified the separate integrated information retrieval and analytical systems that allow obtaining information from many databases at one time. Their capabilities and benefits of use in the investigation process are outlined. The specifics of legal regulation, as well as technical and organizational support of polygraph studies are described. This aspect is important from the standpoint of human resourcing and implementation of professional functions. In the first case we are talking about the fact that one of the conditions of employment on the relevant positions of law enforcement agencies is a polygraph examination, in the second - polygraph examinations are an important additional information channel during the investigation activity. The importance of non-verbal information channels and aspects of its use in law enforcement activity are outlined.

Integrating neurophysiologic relevance feedback in intent modeling for information retrieval.

The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first‐of‐its‐kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology‐based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital

BackgroundTraditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King’s College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present.ResultsTo date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King’s College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall.ConclusionWe describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.

GENESIS OF INFORMATION AND ANALYTICAL PROVISION OF NATIONAL POLICE OF UKRAINE

The historical stages of formation and development of information analytics of law enforcement bodies in the territory of modern Ukraine from the beginning of the 18th century to the creation of the National Police of Ukraine in 2015 have been established and analyzed. Taking into account certain aspects of society's historical, political and technical progress, five stages of such development have been suggested to distinguish. The first stage ( pre-revolutionary ) from the 18th century to 1917 is a period characterized by the first systematization of knowledge about the fight against crime, namely the first systems of forensic registration for anthropological data, photographs, fingerprints, etc. The second stage ( early Soviet ) from 1917 to the early 1970's is a period that encompasses the formation of law enforcement bodies of the Soviet Union. A characteristic feature of this period is that after 1917, records of the royal police were destroyed, which greatly complicated the fight against crime, which at that time began to reach a large scale. Such a development led to the period of the formation of operational search and reference records. The third stage ( information activity automation ) covers the beginning of the 1970's and the beginning of the 1990's and is characterized by the rapid development of information and analytical work due to the development of electronic computing systems, as well as the creation of specialized information centers. During this period, modern foundations of information and analytical support of the bodies of internal affairs of Ukraine were launched. The fourth stage ( informational support of the Ministry of Internal Affairs of Ukraine ) covers the beginning of the 1990's to 2015. This period is characterized by the adoption of new legal acts that have influenced the development of informational support for the Ukrainian law enforcement bodies, and the intensive creation of autonomous, integrated information retrieval systems. The fifth stage ( informational support of the National Police of Ukraine ). This period is from 2015 until now. Separately this period is distinguished in connection with the creation of the National Police of the country and solving the problems of delimiting access to information resources between the Ministry of Internal Affairs of Ukraine and the National Police. Characteristic features of both the formation of information resources and the activities of the units that ensure their accumulation, storage and use are distinguished for each of the considered periods. Particular attention is paid to the issue of creation and improvement of information resources of the Ministry of Internal Affairs of Ukraine, the role of informational support processes in increasing the effectiveness of the National Police in the field of counteraction to crime. On the basis of the analysis of the current state of information provision of the National Police units, the basis for the development of new legal and organizational principles for its information activity has been established. This in turn will enable to meet not only theoretical but also practical needs for further reformation both within the Ministry of Internal Affairs and its constituent – the National Police of Ukraine.

RETRIEVAL—An Online Performance Evaluation Tool for Information Retrieval Methods

Performance evaluation is one of the main research topics in information retrieval. Evaluation metrics are used to quantify various performance aspects of a retrieval method. These metrics assist in identifying the optimum method for a specific retrieval challenge but also to allow its parameters fine-tuning in order to achieve a robust operation for a given set of requirements specification. In this work, we present RETRIEVAL, a Web-based integrated information retrieval performance evaluation platform. It offers a number of metrics that are popular within the scientific community, so as to compose an efficient framework for implementing performance evaluation. We discuss the functionality of RETRIEVAL by citing important aspects such as the data input approaches, the user-level performance metrics parameterization, the evaluation scenarios, the interactive plots, and the performance reports repository that offers both archiving and download functionalities.

Dynamic data source selection for humanistic culture integrated information retrieval

One of the core technologies of data source selection is to construct summary. When the data source content updates, it will inevitably lead to changes in the data source summary. The calculation work load to completely update the summary is huge, so a partial update strategy is used to improve the efficiency. In order to enhance the efficiency of tourism cultural information integration, we propose a new dynamic data source selection strategy. Firstly, we build a summary based on the relationship between people and characteristic words. Then, we design a local summary updating algorithm based on sampling technique. Finally, we propose a scoring method of data source base on the gain of cultural content of a single or synthetic character for integration purposes. We conduct a number of experiments based on field data collection and the results showed that compared with the existing update algorithms, our method has higher accuracy.

Dynamic data source selection for humanistic culture integrated information retrieval

One of the core technologies of data source selection is to construct summary. When the data source content updates, it will inevitably lead to changes in the data source summary. The calculation work load to completely update the summary is huge, so a partial update strategy is used to improve the efficiency. In order to enhance the efficiency of tourism cultural information integration, we propose a new dynamic data source selection strategy. Firstly, we build a summary based on the relationship between people and characteristic words. Then, we design a local summary updating algorithm based on sampling technique. Finally, we propose a scoring method of data source base on the gain of cultural content of a single or synthetic character for integration purposes. We conduct a number of experiments based on field data collection and the results showed that compared with the existing update algorithms, our method has higher accuracy.

생명 패러다임 정보행태모델

21세기에 들어서 인문학계와 과학계가 서로 융합되며 새로운 이론과 모델들이 창출되고 있다. 그 중에서도 여러 제반분야에서 관심을 받는 이론이 체화된(embodied) 인지이론이다. 본 연구는 이때까지의 정보학 이용자 행태에 대한 메타이론을 간략하게 살피고, 그 한계를 짚어 본다. 다음으로 이러한 체화된 인지이론과 더불어 정보행위의 주체인 인간이 속하는 생명체에 관한 엔트로피 개념을 도입해 새로운 시대의 흐름에 맞는 생명 패러다임 정보행태모델을 제시하고자 한다. 검색이라는 개념과 이용자의 행위에 대한 개별적인 접근의 한계를 극복하고 통합적 패러다임으로서의 새로운 정보검색이론의 담론을 기대한다. Humanities academia and scientific community have been fused together in 21st centurty and it creates new theories and models. Among them, the embodied cognition theory has received attention in several related fields. This study reviews the major metatheories in information user behavior with their limitations. Then, "Life paradigm information behavior model" is suggested as a new theory, which communsurates with new era's request, introducing the embodied cognition theory and the entropy concept. By overcoming the limitations of individual approach to the information retrieval and user behaviors, we expect the discourse of the new integrated information retrieval paradigm.

EMITS: An Experience Management System for IT Management Support

This research focuses on the identification of relevant experience required for solving IT (Information Technology) problems in small- to medium-sized enterprises. To achieve this, we integrated information retrieval techniques with clustering and optimization techniques to design and develop a custom-built Experience Management system for IT management support. We have built and evaluated our system on three different publicly available data sets: Princeton, Parallels and GoDaddy. Results support that it is possible to provide the right mix of automation and manual activity for IT experience management while achieving a high accuracy.

Integrating information retrieval with distant supervision for gene ontology annotation.

This article describes our participation of the Gene Ontology Curation task (GO task) in BioCreative IV where we participated in both subtasks: A) identification of GO evidence sentences (GOESs) for relevant genes in full-text articles and B) prediction of GO terms for relevant genes in full-text articles. For subtask A, we trained a logistic regression model to detect GOES based on annotations in the training data supplemented with more noisy negatives from an external resource. Then, a greedy approach was applied to associate genes with sentences. For subtask B, we designed two types of systems: (i) search-based systems, which predict GO terms based on existing annotations for GOESs that are of different textual granularities (i.e., full-text articles, abstracts, and sentences) using state-of-the-art information retrieval techniques (i.e., a novel application of the idea of distant supervision) and (ii) a similarity-based system, which assigns GO terms based on the distance between words in sentences and GO terms/synonyms. Our best performing system for subtask A achieves an F1 score of 0.27 based on exact match and 0.387 allowing relaxed overlap match. Our best performing system for subtask B, a search-based system, achieves an F1 score of 0.075 based on exact match and 0.301 considering hierarchical matches. Our search-based systems for subtask B significantly outperformed the similarity-based system.Database URL: https://github.com/noname2020/Bioc

EAD 기반 영구기록물 통합검색시스템 설계 및 구현

This study aims to design and implement an integrated information retrieval system for the archives in Korea. Particularly, this study is focused on the improvement of the usability of the system. To achieve the goal, this study analyzed the current retrieval systems of national archives and presidential archives in Korea. A new integrated retrieval system, called KAIRS, is designed based on the EAD elements, which is implemented through emulation technic between two retrieval systems. KAIRS showed higher of user satisfaction compared to the existing two retrieval systems in their usability test.

Integrating information retrieval, execution and link analysis algorithms to improve feature location in software

Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining or link analyses algorithms applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation on three Java systems indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 87%.

Mining group-based knowledge flows for sharing task knowledge

In an organization, knowledge is the most important resource in the creation of core competitive advantages. It is circulated and accumulated by knowledge flows (KFs) in the organization to support workers' task needs. Because workers accumulate knowledge of different domains, they may cooperate and participate in several task-based groups to satisfy their needs. In this paper, we propose algorithms that integrate information retrieval and data mining techniques to mine and construct group-based KFs (GKFs) for task-based groups. A GKF is expressed as a directed knowledge graph which represents the knowledge referencing behavior, or knowledge flow, of a group of workers with similar task needs. Task-related knowledge topics and their relationships (flows) can be identified from the knowledge graph so as to fulfill workers' task needs and promote knowledge sharing for collaboration of group members. Moreover, the frequent knowledge referencing path can be identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed methods, we implement a prototype of the GKF mining system. Our GKF mining methods can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential.

Scalable keyword search on large data streams

It is widely recognized that the integration of information retrieval (IR) and database (DB) techniques provides users with a broad range of high quality services. Along this direction, IR-styled m-keyword query processing over a relational database in an rdbms framework has been well studied. It finds all hidden interconnected tuple structures, for example connected trees that contain keywords and are interconnected by sequences of primary/foreign key relationships among tuples. A new challenging issue is how to monitor events that are implicitly interrelated over an open-ended relational data stream for a user-given m-keyword query. Such a relational data stream is a sequence of tuple insertion/deletion operations. The difficulty of the problem is related to the number of costly joins to be processed over time when tuples are inserted and/or deleted. Such cost is mainly affected by three parameters, namely, the number of keywords, the maximum size of interconnected tuple structures, and the complexity of the database schema when it is viewed as a schema graph. In this paper, we propose new approaches. First, we propose a novel algorithm to efficiently determine all the joins that need to be processed for answering an m-keyword query. Second, we propose a new demand-driven approach to process such a query over a high speed relational data stream. We show that we can achieve high efficiency by significantly reducing the number of intermediate results when processing joins over a relational data stream. The proposed new techniques allow us to achieve high scalability in terms of both query plan generation and query plan execution. We conducted extensive experimental studies using synthetic data and real data to simulate a relational data stream. Our approach significantly outperforms existing algorithms.

From Keyword Search to Exploration: Designing Future Search Interfaces for the Web

This monograph is directed at researchers and developers who are designing the next generation of web search user interfaces, by focusing on the techniques and visualizations that allow users to interact with and have control over their findings. Search is one of the keys to the Web's success. The elegant way in which search results are returned has been well researched and is usually remarkably effective. However, the body of work produced by decades of research into information retrieval continues to grow rapidly and so it has become hard to synthesize the current state-of-the-art to produce a search interface that is both highly functional, but not cluttered and distracting. Further, recent work has shown that there is substantial room for improving the support provided to users who are exhibiting more exploratory forms of search, including when users may need to learn, discover, and understand novel or complex topics. Overall, there is a recognized need for search systems to provide effective user experiences that do more than simply return results. With the aim of producing more effective search interfaces, human computer interaction researchers and web designers have been developing novel interactions and features that enable users to conveniently visualize, parse, manipulate, and organize their Web search results. For instance, while a simple set of results may produce specific information (e.g., the capital of Peru), other methods may let users see and explore the contexts of their requests for information (more about the country, city, and nearby attractions), or the properties that associate groups of information assets (grouping hotels, restaurants, and attractions by their type, district, or price). Other techniques support information-seeking processes that may last weeks or months or may even require collaboration between multiple searchers. The choice of relevant result visualization strategies in new search systems should reflect the searchers and the higher-level information needs that motivate their searches. These examples provide further motivation for supporting designers, who are challenged to synthesize and understand the breadth of advances in search, so that they can determine the benefits of varied strategies and apply them appropriately to build better systems. To support researchers and designers in synthesizing and understanding the advances in search, this monograph offers a structured means to think about web search result visualization, based on an inclusive model of search that integrates information retrieval, information seeking and a higher-level context of tasks and goals. We examine each of these levels of search in a survey of advances in browsers and related tools by defining search-related cognitive processes and analyzing innovative design approaches. We then discuss evaluations at each of these levels of search, presenting significant results and identifying both the traditional and novel means used to produce them. Based on this examination, we propose a taxonomy of search result visualization techniques that can be used to identify gaps for future research and as a reference for designers of next generation web search systems.

Language‐modeling kernel based approach for information retrieval

AbstractIn this presentation, we propose a novel integrated information retrieval approach that provides a unified solution for two challenging problems in the field of information retrieval. The first problem is how to build an optimal vector space corresponding to users' different information needs when applying the vector space model. The second one is how to smoothly incorporate the advantages of machine learning techniques into the language modeling approach. To solve these problems, we designed the language‐modeling kernel function, which has all the modeling powers provided by language modeling techniques. In addition, for each information need, this kernel function automatically determines an optimal vector space, for which a discriminative learning machine, such as the support vector machine, can be applied to find an optimal decision boundary between relevant and nonrelevant documents. Large‐scale experiments on standard test‐beds show that our approach makes significant improvements over other state‐of‐the‐art information retrieval methods.

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