Existing Information Retrieval Research Articles

Smart Search Engine

Existing information retrieval systems, such search engines, must be improved due to the issue of having too much information on the web or in broad sectors. More consideration is needed. Integrated search tools that can manage searches and filters effectively and show relevant results The main sitting technology is bloated with ever-increasing volumes of data, much like the Internet. You'll have finished your keyword research and all of this information one day. Its capacity to deliver timely information that people are seeking. Search engines work to overcome the restrictions imposed by the conventional method of keyword research as a result. This study seeks to outline the primary issues that keyword search engines currently face in order to keep up with the rapid expansion of the web and to meet all user expectations in a timely manner. In the next section, it examines a number of models that are not dependent on keywords that have been created, created, or used by researchers and search engines, and rates those technologies in accordance with the aspects that each search engine prioritizes in order to deliver results.

Decentralized News-Retrieval Architecture Using Blockchain Technology

Trust is a critical element when it comes to news articles, and an important problem is how to ensure trust in the published information on news websites. First, this paper describes the inner workings of a proposed news-retrieval and aggregation architecture employed by a blockchain-based solution for fighting disinformation; this includes a comparison between existing information retrieval solutions. The decentralized nature of the solution is achieved by separating the crawling (i.e., extracting the web page links) from the scraping (i.e., extracting the article information) and having third-party actors extract the data. A majority-rule mechanism is used to determine the correctness of the information, and the blockchain network is used for traceability. Second, the steps needed to deploy the distributed components in a cloud environment seamlessly are discussed in detail, with a special focus on the open-source OpenStack cloud solution. Lastly, novel methods for achieving a truly decentralized architecture based on community input and blockchain technology are presented, thus ensuring maximum trust and transparency in the system. The results obtained by testing the proposed news-retrieval system are presented, and the optimizations that can be made are discussed based on the crawling and scraping test results.

A formula embedding approach for semantic similarity and relatedness between formulas

SummaryThe mathematical formula is one of the most vital components in a scientific document, which can explicitly describe various complex concepts and ideas. In addition to numerical calculations, they are also used to clarify definitions and disambiguate explanations transcribed in natural language. Nevertheless, the formulas have a noteworthy impact in the scientific documents, the existing information retrieval systems have limited access to scientific documents based on formulas‐based queries. To accomplish this, in this research, we have studied and implemented the formula embedding approach, which encodes the formula into the embedded vector. For encoding the formula, we have used pretrained sentence bidirectional encoder representations from transformers model. The proposed embedding model takes the latex formula as input and generates an upshot as a fixed dimensional embedding representation. In addition to this, the Siamese network is used to reform the semantic meaning of the formulas. Furthermore, the embedding of the formulas and the queried formula are compared, and cosine similarity is estimated. The performance of the suggested methodology is verified using a math stack exchange corpus of ARQMath 2020, and obtained results have shown a remarkable contribution in the task of formula retrieval.

Dataset Search and Augmentation

Data has become an indispensable part of our life. However, current mainstream commercial search engines do not support specialized functions for dataset search. A dataset usually consists of both metadata and data content. Existing information retrieval models designed for Web search cannot efficiently extract semantic information inside structured datasets, even when they contain textual content. Developing new algorithms for next-generation search engines to efficiently find datasets can benefit data practitioners in their data discovery experience. In this dissertation, we consider how to effectively perform dataset search and augmentation. We start by providing an end-to-end description of a dataset search engine following the lifecycle of datasets. Our review includes web dataset acquisition techniques, dataset profiling and augmentation methods, and dataset search tasks and corresponding methods. In order to extract datasets from research articles, we present an information extraction framework to determine triples of interest which can be used for academic dataset search. We propose a feature-based method to augment tabular datasets with additional schema labels to help users and systems to better understand the datasets. We develop three methods for tabular dataset search: the first utilizes generated schema labels to enhance the search results; the second adopts pretrained language models to learn matching features; the third models the complex relations in the datasets as one or more graphs and uses graph neural networks to learn representations of queries and tables. To support dataset search in which a query is also a dataset, we propose universal dataset encoders which regard a dataset as a point set so that the encoded dataset representations can be used to search for similar datasets. Extensive experiments across multiple tasks demonstrate the superiority of our proposed methods over the state of the art. Awarded by: Lehigh University, Bethlehem, USA on 10 May 2022. Supervised by: Brian D. Davison. Available at: https://github.com/Zhiyu-Chen/Dissertation/blob/main/Dissertation_Dataset_Search.pdf.

An Approach for Improving Similarity Measure Using Fuzzy Logic

An information retrieval system stores and indexes documents such that when users submit a query, the system gets relevant documents and assigns a score to each one. The higher the score, the more important the document is. IR systems typically yield vast result sets, and users must spend a significant amount of time sifting through them to identify the elements that are genuinely important. Different suggestions for applying evolutionary computing to the topic of information retrieval will be reviewed from the specialist literature. To do so, researchers looked at a variety of IR issues that were addressed using evolutionary algorithms. Some of the current ways will be detailed in detail; for example, when dealing with specialized domain knowledge, this challenge can be solved by embedding a knowledge base into existing information retrieval systems that illustrates the relationships between index words. The fuzzy set theory may be used to change the knowledge in the bases to cope with the ambiguity that is typical of human knowledge. In this work, a novel way for implementing a similarity measure utilizing fuzzy logic for IR is provided. A suggested similarity metric is based on many IR system attributes that boost IR system performance. This method's strength is that it can extract the majority of a document's characteristics. Fuzzy rules, which translated domain knowledge into fuzzy sets, were also designed to make this most effective. Our suggested similarity metric is validated using the CACM and CRAN benchmark datasets.

Фотофонд Государственного архива Свердловской области: проблемы комплектования, систематизации, поиска и использования фотодокументов

The article analyzes the history of creation of the photo fond in the State Archive of Sverdlovsk Region (GASO), assesses its scientific reference apparatus, and evaluates the efficiency of search for photographic documents by their subject. There is a growing interest towards local history and history of urban environment, which increases demand for photographic documents stored in the archive. However, the use of these documents is limited due to low efficiency and arduousness of search. The author suggests several ways to increase the efficiency of search using modern computer technology. To achieve this goal, the legal frameworks on photographic documents processing in state archives have been analyzed. The unique sources used in the research are detailed interviews with the GASO staff, as well as annual reports on the work of the archive during 1961–72. During this period, much effort was put to creation of the scientific reference apparatus: description, systematization, and cataloging of photographic documents were conducted. The photo fond (fond 1) counts 62265 storage items. Photographs and albums are also stored in other GASO fonds. The acquisition sources were departmental archives of institutions and organizations, the GASO itself, as well as individuals. Photo documents were widely used in the GASO’s educational and popularization work and were available on request of organizations and citizens. The documents have research potential, and yet (according to archival staff and to research) the existing information retrieval systems do not provide complete and effective search due to their limitations. Relevance and fullness ratio of the information retrieval system have been calculated for publicly available electronic inventories of photographic documents. It has been established that search efficiency of highly specialized queries has precision score of 0,003, while noise ratio is 0,997.

Using social media for flight path safety assessment

Purpose Artificial intelligence (AI)-based systems can save the lives of many people by assessing the safety of flight paths. Unfortunately, the world witnessed a horrible event in January 2020 with the case of flight 752 of Ukrainian International Airlines from Tehran to Kiev and it has prompted us to ask how AI can prevent such events by warning to flight path planners. This paper aims to propose a framework for assessing the safety of flight paths from a shooting of an airplane by air defense systems installed on the path. Unlike the existing studies, this study takes a new look at pre-flight risk assessment by using textual information in social and news networks. To this end, the authors use existing information retrieval techniques to identify high flight risk areas by examining the news articles, comments, posts, tweets, etc., in social media and then estimate the probability of targeting a passenger aircraft by the air defense systems probably installed on high-risk areas with the help of a statistical model. This estimation can then be used by fight planners to avoid such events. Design/methodology/approach To design a framework for estimating the probability of a fatal shooting of an airplane by air defense systems installed on its flight path, the authors have used the idea of information retrieval in conjunction with statistical methods. The authors have extracted some significant variables in the shooting of flights and proposed an AI-based framework to estimate the probability of a fatal shooting of an airplane during its flight and sketched a case study for using machine learning approaches to assist with flight path planning. As a case study, the authors covered flight 752 to explain the usefulness of the proposed framework in this context. Findings Unlike the existing methods, this study investigates flight path safety assessment from the social media and crowdsourcing perspective. In this study, the authors proposed an AI-based framework to avoid aviation hazards by estimating the probability of a shooting of an airplane by air defense systems installed on its flight path. Moreover, this study was designed to show how estimating the safety of flight paths by using AI-based methods can help flight planners to avoid such events and gain further insights into the use of AI-based systems in pre-flight risk assessment. Originality/value The idea behind the proposed method is original and as the authors’ best knowledge, there is no similar framework using social media for flight path safety assessment.

Unsupervised clinical relevancy ranking of structured medical records to retrieve condition-specific information in the emergency department

BackgroundDecision making in the Emergency Department (ED) requires timely identification of clinical information relevant to the complaints. Existing information retrieval solutions for the electronic health record (EHR) focus on patient cohort identification and lack clinical relevancy ranking. We aimed to compare knowledge-based (KB) and unsupervised statistical methods for ranking EHR information by relevancy to a chief complaint of chest or back pain among ED patients. MethodsWe used Pointwise-mutual information (PMI) with corpus level significance adjustment (cPMId), which modifies PMI to reward co-occurrence patterns with a higher absolute count. cPMId for each pair of medication/problem and chief complaint was estimated from a corpus of 100,000 un-annotated ED encounters. Five specialist physicians ranked the relevancy of medications and problems to each chief complaint on a 0–4 Likert scale to form the KB ranking. Reverse chronological order was used as a baseline. We directly compared the three methods on 1010 medications and 2913 problems from 99 patients with chest or back pain, where each item was manually labeled as relevant or not to the chief complaint, using mean average-precision. ResultscPMId out-performed KB ranking on problems (86.8% vs. 81.3%, p < 0.01) but under-performed it on medications (93.1% vs. 96.8%, p < 0.01). Both methods significantly outperformed the baseline for both medications and problems (71.8% and 72.1%, respectively, p < 0.01 for both comparisons). The two complaints represented virtually completely different information needs (average Jaccard index of 0.008). ConclusionA fully unsupervised statistical method can provide a reasonably accurate, low-effort and scalable means for situation-specific ranking of clinical information within the EHR.

Concept Based Intelligent Information Retrieval Within Digital Library

A digital library is a type of information retrieval (IR) system. The existing information retrieval methodologies generally have problems on keyword-searching. We proposed a model to solve the problem by using concept-based approach (ontology) and metadata case base. This model consists of identifying domain concepts in user’s query and applying expansion to them. The system aims at contributing to an improved relevance of results retrieved from digital libraries by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to import the concept of ontology, making use of its advantage of abundant semantics and standard concept. Domain specific ontology can be used to improve information retrieval from traditional level based on keyword to the lay based on knowledge (or concept) and change the process of retrieval from traditional keyword matching to semantics matching. One approach is query expansion techniques using domain ontology and the other would be introducing a case based similarity measure for metadata information retrieval using Case Based Reasoning (CBR) approach. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.

An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem

The explosion of information constrains the judgement of search terms associated with Knowledge-Based Web Ecosystem (KBWE), making the retrieval of relevant information and its knowledge management challenging. The existing information retrieval (IR) tools and their fusion in a framework need attention, in which search results can effectively be managed. In this article, we demonstrate the effective use of information retrieval services by a variety of users and agents in various KBWE scenarios. An innovative Integrated Search Framework (ISF) is proposed, which utilises crawling strategies, web search technologies and traditional database search methods. Besides, ISF offers comprehensive, dynamic, personalized, and organization-oriented information retrieval services, ranging from the Internet, extranet, intranet, to personal desktop. In this empirical research, experiments are carried out demonstrating the improvements in the search process, as discerned in the conceptual ISF. The experimental results show improved precision compared with other popular search engines.

A method to automatically push keywords for biological information searching in bio-inspired design

In bio-inspired design, identifying keywords is an important step of biological information searching. Based on existing information retrieval approaches, the amount of relevant biological information in a search result largely depends on the identified keywords. Due to the limitation of biological knowledge, design engineers are difficult to identify the appropriate keywords that can find the biological information related to engineering requirements. To address this issue, we present an algorithm that can calculate the Composite Correlation Intension of functionally combined words, which integrates semantic similarity computation, data normalization, and corpus technology. Based on the algorithm, a method that automatically pushes keywords for biological information searching in bio-inspired design is also proposed. The method decomposes engineering requirements and structures functionally combined words, calculates the Composite Correlation Intension values of all functionally combined words, ranks the functionally combined words by the Composite Correlation Intension values, takes the functionally combined words with larger Composite Correlation Intension values as keywords, and pushes them. Through these keywords, design engineers retrieve the relevant biological information and discover the required design knowledge. In order to facilitate the use of proposed method, an auxiliary tool was developed in Python environment. Finally, the possibility of proposed method was demonstrated by a preliminary validation and an application case. The results show that the proposed method would be a promising alternative to identify keywords for biological information searching in bio-inspired design.

The Development of a State-Aware Equipment Maintenance Application Using Sensor Data Ranking Techniques.

Billions of electric equipment are connected to Internet of Things (IoT)-based sensor networks, where they continuously generate a large volume of status information of assets. So, there is a need for state-aware information retrieval technology that can automatically recognize the status of each electric asset and provide the user with timely information suitable for the asset management of electric equipment. In this paper, we investigate state-aware information modeling that specializes in the asset management of electric equipment. With this state-aware information model, we invent a new asset state-aware ranking technique for effective information retrieval for electric power and energy systems. We also derive an information retrieval scenario for IoT in power and energy systems and develop a mobile application prototype. A comparative performance evaluation proves that the proposed technique outperforms the existing information retrieval technique.

A supervised term ranking model for diversity enhanced biomedical information retrieval

BackgroundThe number of biomedical research articles have increased exponentially with the advancement of biomedicine in recent years. These articles have thus brought a great difficulty in obtaining the needed information of researchers. Information retrieval technologies seek to tackle the problem. However, information needs cannot be completely satisfied by directly introducing the existing information retrieval techniques. Therefore, biomedical information retrieval not only focuses on the relevance of search results, but also aims to promote the completeness of the results, which is referred as the diversity-oriented retrieval.ResultsWe address the diversity-oriented biomedical retrieval task using a supervised term ranking model. The model is learned through a supervised query expansion process for term refinement. Based on the model, the most relevant and diversified terms are selected to enrich the original query. The expanded query is then fed into a second retrieval to improve the relevance and diversity of search results. To this end, we propose three diversity-oriented optimization strategies in our model, including the diversified term labeling strategy, the biomedical resource-based term features and a diversity-oriented group sampling learning method. Experimental results on TREC Genomics collections demonstrate the effectiveness of the proposed model in improving the relevance and the diversity of search results.ConclusionsThe proposed three strategies jointly contribute to the improvement of biomedical retrieval performance. Our model yields more relevant and diversified results than the state-of-the-art baseline models. Moreover, our method provides a general framework for improving biomedical retrieval performance, and can be used as the basis for future work.

DTexSL: A dynamic disaster textual storyline generating framework

Effectively capturing the status information and improving situational awareness is the most important task in disaster information management. Due to the rapid increase of online information, this task becomes very challenging. Existing information retrieval and text summarization methods can solve information overload problem to some extent, however, they suffer from some limitations: lacking theme structure, ignoring spatial information, and unable to update information on the real time events. In this paper, we propose a dynamic disaster storyline generation framework, which generates a global storyline describing the evolution of the disaster events in the high-level layer and provides condensed information about specific regions affected by the disaster in the local-level layer. The proposed framework considers both uniqueness and relevance for representative document selection, uses Maximal Marginal Relevance to generate summaries from each local document set, and utilizes dynamic Steiner tree to implement the information update. Comprehensive experiments on typhoons data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.

Machine translation-based bug localization technique for bridging lexical gap

ContextThe challenge of locating bugs in mostly large-scale software systems has led to the development of bug localization techniques. However, the lexical mismatch between bug reports and source codes degrades the performances of existing information retrieval or machine learning-based approaches. ObjectiveTo bridge the lexical gap and improve the effectiveness of localizing buggy files by leveraging the extracted semantic information from bug reports and source code. MethodWe present BugTranslator, a novel deep learning-based machine translation technique composed of an attention-based recurrent neural network (RNN) Encoder-Decoder with long short-term memory cells. One RNN encodes bug reports into several context vectors that are decoded by another RNN into code tokens of buggy files. The technique studies and adopts the relevance between the extracted semantic information from bug reports and source files. ResultsThe experimental results show that BugTranslator outperforms a current state-of-the-art word embedding technique on three open-source projects with higher MAP and MRR. The results show that BugTranslator can rank actual buggy files at the second or third places on average. ConclusionBugTranslator distinguishes bug reports and source code into different symbolic classes and then extracts deep semantic similarity and relevance between bug reports and the corresponding buggy files to bridge the lexical gap at its source, thereby further improving the performance of bug localization.

Intelligent ontology based semantic information retrieval using feature selection and classification

Semantic information retrieval provides more relevant information to the user query by performing semantic analysis. In such a scenario, knowledge representation using ontology can provide effective semantic retrieval facility which is more efficient than representation using semantic networks and frames. The existing information retrieval systems have been developed to handle very large volume of data and information stored in text format. On the other hand, the information available in the current web based applications such as Facebook and twitter grow very fast and hence the existing information retrieval systems consume large amount of time for relevant information retrieval. Moreover, most of the existing search engines use syntactic approach for information retrieval and use page ranking algorithms to measure the relevancy score. However, such approach is not able to provide more accurate results in terms of relevancy. Therefore, a new semantic information retrieval system is proposed in this paper which uses feature selection and classification for enhancing the relevancy score which is performed in this work by proposing a new intelligent fuzzy rough set based feature selection algorithm and an intelligent ontology and Latent Dirichlet Allocation based semantic information retrieval algorithm. The main advantages of the proposed algorithms are the increase in relevancy, ability to handle big data and fast retrieval.

Indian Tourism Information Retrieval System: An Onto-Semantic Approach

The huge amount of information in the tourism industry gives a base to this development of powerful and efficient information retrieval system. The usage of such information retrieval framework utilizes the heterogeneous data available on the web from the Indian tourism domain. The existing information retrieval frameworks are syntactic in nature, which renders irrelevant outcomes along with relevant outcomes to the given query. The aim of this approach is to devise a semantic based Indian tourism information retrieval system. The proposed approach utilizes ontology derived for the Indian tourism domain for the efficient information retrieval. This approach is evaluated with existing keyword based search engine’s and the results shows that there is remarkable improvement in this approach.

An Integrated Model for Information Retrieval Based Change Impact Analysis

The paper presents an approach to combine multiple existing information retrieval (IR) techniques to support change impact analysis, which seeks to identify the possible outcomes of a change or determine the necessary modifications for affecting a desired change. The approach integrates a bag-of-words based IR technique, where each class or method is abstracted as a set of words, and a neural network based IR technique to derive conceptual couplings from the source code of a software system. We report rigorous empirical assessments of the changes of three open source systems: jEdit, muCommander, and JabRef. The impact sets obtained are evaluated at the method level of granularity, and the results show that our integrated approach provides statistically significant improvements in accuracy across several cut points relative to the accuracies provided by the individual methods employed independently. Improvements in F-score values of up to 7.3%, 10.9%, and 17.3% are obtained over a baseline technique for jEdit, muCommander, and JabRef, respectively.

Providing consumers with a representative subset from online reviews

PurposeThe purpose of this paper is to find a representative subset from large-scale online reviews for consumers. The subset is significantly small in size, but covers the majority amount of information in the original reviews and contains little redundant information.Design/methodology/approachA heuristic approach named RewSel is proposed to successively select representatives until the number of representatives meets the requirement. To reveal the advantages of the approach, extensive data experiments and a user study are conducted on real data.FindingsThe proposed approach has the advantage over the benchmarks in terms of coverage and redundancy. People show preference to the representative subsets provided by RewSel. The proposed approach also has good scalability, and is more adaptive to big data applications.Research limitations/implicationsThe paper contributes to the literature of review selection, by proposing a heuristic approach which achieves both high coverage and low redundancy. This study can be applied as the basis for conducting further analysis of large-scale online reviews.Practical implicationsThe proposed approach offers a novel way to select a representative subset of online reviews to facilitate consumer decision making. It can also enhance the existing information retrieval system to provide representative information to users rather than a large amount of results.Originality/valueThe proposed approach finds the representative subset by adopting the concept of relative entropy and sentiment analysis methods. Compared with state-of-the-art approaches, it offers a more effective and efficient way for users to handle a large amount of online information.

A Literature Review on Patent Information Retrieval Techniques

Objective: Patents are critical intellectual assets for any competitive business. They can prove to be a gold mine if retrieved, analyzed and utilized appropriately. Patentability search is an important step in the patent process and missing out any relevant patent may cause expensive legal consequences. As worldwide patent collection is growing rapidly, retrieval of this enormous knowledge source has become complex and exhaustive. This paper attempts to review the studies carried out in enhancing the relevance effectiveness of patent information retrieval. Method/Analysis: Literature review presents various research works that have been carried out to yield better results in patent retrieval task by refining existing information retrieval techniques or by using standard approaches at the various stages of the patent retrieval task. This work exclusively looks at literatures dealing with retrieval of patent text. Findings: Patent retrieval is not a completely solved research domain and general information retrieval approaches do not prove effective in this domain as patents are special documents posing various retrieval challenges. The review also highlights future research directions and will help researchers working in the domain of patent retrieval. Application/Improvement: Considering the various techniques and frameworks available and their limitations, there is a lot of scope in the field of patent retrieval techniques which makes room for further research to be taken up in this domain. Keywords: Information Retrieval, Patent Retrieval, Patent Search, Query Expansion, Relevance Feedback, Semantic Analysis