Ontology-Based Document and Query Representation May Improve the Effectiveness of Information Retrieval

With the development of search engines (e.g. Google, Bing, Yahoo, etc.), people is ambitiously expecting higher quality and improvements of current technologies. Bringing human intelligence features to these tools, like the ability to find implicit information through semantics, is one of the must prominent research lines in Computer Science. Information semantics is a very wide concept, as wide as the human capability to interpret, in particular, the analysis of geographical semantics gives the possibility to associate information with a place. It is estimated that more than 70\% of all information in the world has some kind of geographic features \cite{Jones04}. In 2012, Ed Parsons, a GeoSpatial Technologist from Google, reported that between 30\% and 40\% of the user queries at Google search engine contain geographic references \cite{Parsons12}. This thesis addresses the field of geographic information extraction and retrieval in unstructured texts. This process includes the identification of spatial features in textual documents, the data indexing, the manipulation of the relevance of the identified geographic entities and the multi-criteria retrieval according to the thematic and geographic information. The main contributions of this work include a custom geographic knowledge base, built from the combination of GeoNames and WordNet; a Natural Language Processing and knowledge based heuristics for Toponym Recognition and Toponym Disambiguation; and a geographic relevance weighting model that supports non-spatial indexing and simple ranking combination approaches. The validity of each one of these components is supported by practical experiments that show their effectiveness in different scenarios and their alignment with state of the art solutions. In addition, it also constitutes a main contribution of this work GEIR, a general purpose GIR framework that includes the implementations of the above described components and brings the possibility of implementing new ones and test their performance within an end to end GIR system.

The subject of study. The paper focuses on the extraction of semantics from texts in Arabic. In particular, the applicability of the Bell test to word pairs is investigated as a measure of the semantic words relatedness in a context. The study applies the quantum formalism to the task of information retrieval in Arabic texts and presents the results of this work. The authors also examine the influence of the context width on the effectiveness of information retrieval. Method. The research is based on the vector representation of the context. It uses the well-known approach based on the HAL (Hyperspace Analogue to Language) matrix and Bell test. The HAL matrix allows taking into account both the frequency of the words occurrence in the context and the distance to the target word. Quantum theory operates with probability density matrices. Quantum theory allows describing probabilities in the vector space in a more natural way i.e., words can be represented as vectors. Main results. The results demonstrate that using the Bell’s test for texts in Arabic provides a better ranking of search results compared to the results of search services. Practical significance. The research results can be used in the development of the information retrieval systems, as well as for the further development of methods based on the distributive hypothesis.

Distributed information retrieval methods are growing rapidly because of the rising need to access and search distributed digital documents. However, the content-based information retrieval (CBIR) is concentrated to extract and retrieve the information from massive digital libraries, which require a huge amount of computing and storage resources. The grid computing provides the reliable infrastructure for effective and efficient retrieval on these large collections. In order to build an effective and efficient CBIR technique, varieties of architectures were developed based on grid technologies. The goal of such architecture is to solve interoperability and heterogeneous resource issues, and increase the efficiency and effectiveness of information retrieval (IR) techniques by harnessing the grid computing capabilities. This paper reviews and analyzes latest research carried out in the domain of large-scale dataset IR based on a grid. The evaluation is based on scalability, response time, scope, data type, search technique, middleware, and query type. The contribution is to illustrate the features, capabilities, and shortages of current solutions that can guide the researchers in this evolving area.

Question Answering (QA) is a specialized subfield within Information Retrieval (IR) that focuses on providing precise answers to user queries. Recent advances in Large Language Models (LLMs) have greatly improved the speed and accuracy of question-answering (QA) systems, leading to significantly enhanced capabilities and performance. This study introduces a multi-PDF querying system designed to support automated question answering for Vietnamese students, leveraging the capabilities of LLMs. To enhance the retrieval accuracy from natural language queries, we integrate four Vietnamese-language embedding models. These models are evaluated using a dataset comprising questions related to the academic regulations of the University of Da Nang - University of Science and Technology (DUT), reflecting real-life scenarios students commonly encounter. Experimental results reveal that the top-performing Vietnamese embedding model achieves a Mean Average Precision (mAP) of 0.840 and an F1-score of 0.959, demonstrating its high effectiveness in information retrieval. This study makes significant contributions to the integration of Vietnamese embedding models into automated question-answering systems, aiming to effectively support students in the university environment.

The intention of this paper was to determine usability of KOHA OPAC in information retrieval with regard to usability testing by undergraduate users at Technical University of Kenya Library (TU-KL) in Nairobi County. Usability testing can be realized by way of assigning tasks to a representative of the user population in order to determine whether users can be able to achieve the desired goal of retrieving information. To a great extent, success will depend on how users perform the tasks assigned to them. This study conducted usability test on actual users of KOHA OPAC at Technical University of Kenya Library. Qualitative data was collected using the think aloud method where study participants were required to verbalize their thought process as they performed search and retrieval tasks using KOHA OPAC. The number of errors made, time taken to complete search and retrieval activities were coded, recorded and analysed during usability testing. This helped the researcher to determine whether KOHA OPAC is easy to use and whether it saves time for the user. Undergraduate students for the study were selected using a stratified random sampling technique. The population of the study (n = 382) was determined using a simplified formula developed by Yamane. The study initiated a usability test which presented search scenarios where participants were invited to participate in searching and retrieving information resources using KOHA OPAC. The results of the usability test of KOHA OPAC at TU-KL showed that users were slow at accomplishing search tasks, they took long duration to access TU-KL website that hosts KOHA OPAC and there was inefficiency in accessing library materials. This study concluded that usability challenges may have an impact on users' overall satisfaction with KOHA OPAC and information retrieval effectiveness. In order to enhance user experience and boost the effectiveness of information retrieval, the study recommends improvement of KOHA OPAC usability by revamping its interface and enhancing its features.

QuantNet is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under quantlet. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in R”, LSA and D3 implementation.

Recall, the proportion of relevant documents retrieved, is an important measure of effectiveness in information retrieval, particularly in the legal, patent, and medical domains. Where document sets are too large for exhaustive relevance assessment, recall can be estimated by assessing a random sample of documents, but an indication of the reliability of this estimate is also required. In this article, we examine several methods for estimating two-tailed recall confidence intervals. We find that the normal approximation in current use provides poor coverage in many circumstances, even when adjusted to correct its inappropriate symmetry. Analytic and Bayesian methods based on the ratio of binomials are generally more accurate but are inaccurate on small populations. The method we recommend derives beta-binomial posteriors on retrieved and unretrieved yield, with fixed hyperparameters, and a Monte Carlo estimate of the posterior distribution of recall. We demonstrate that this method gives mean coverage at or near the nominal level, across several scenarios, while being balanced and stable. We offer advice on sampling design, including the allocation of assessments to the retrieved and unretrieved segments, and compare the proposed beta-binomial with the officially reported normal intervals for recent TREC Legal Track iterations.

Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. Learning to rank is useful for document retrieval, it uses feature vector to rank, but there is no feature about document topic. Our paper combines LDA and learning to rank, adds a topic feature into the feature vector of learning to rank algorithm. And the experiment results show that the topic feature has improved the rank result effectively.

Inferential representation of science documents

This study presents an overview of the research aimed at optimizing library information retrieval through the utilization of the Vector Space Model (VSM) method in a computer science context. Libraries, as publicly financed collections, provide extensive knowledge resources, eliminating the need for individual book purchases. However, the challenge lies in efficiently navigating the expanding library collections. To tackle this issue, the study employs information retrieval techniques, particularly the VSM method, which assesses term similarity by assigning weights to terms, enabling document and query representation as vectors. The relevance between documents and queries is measured through vector similarity. This approach, integrated with indexing, streamlines collection retrieval in libraries. Employing the Waterfall model for system development, the research outlines phases like analysis, design, coding, testing, and implementation. While effective, the model's rigidity in accommodating evolving requirements poses limitations. The VSM method's numerical representation of text documents facilitates precise similarity calculations, supported by TF-IDF values indicating term importance in documents relative to the corpus. The study further extends to system design using UML diagrams and a visitor interface, integrating VSM for efficient search functionality. Black-box testing confirms the robustness of the system components and interfaces. Overall, this research presents a systematic approach to enhance information retrieval in libraries, emphasizing the VSM's pivotal role in optimizing document searches within expansive collections.

Learning objects retrieval is important for a variety of information needs and applications such as collection management, summary and analysis. Especially, retrieval of learning objects from the big collection of textual database has become a very active field. Since the learning information is stored in textual format in most of the time, the retrieval of learning objects from textual database faces two major challenges such as, large data handling, and effectiveness. If these two challenges are solved, the performance and its applicability will be improved significantly. Accordingly, the first challenges of large data processing will be handled through the semantic annotations method. In the semantic annotation method, the text document was converted to semantic annotation data using concept-based modeling. With the help of existing work, the large data will be converted to annotation object so that the matching process with the input query will be reduced and the complexity of handling big data will be also reduced. The second challenge of effectiveness will be solved using new matching score to obtain better retrieval effectiveness. Here, query will be matched with the annotated object using matching score that will be devised newly and it will be applied for learning object retrieval. The experimentation will be carried out utilizing the learning objects given in IEEE digital library and the performance of the proposed technique is evaluated using precision, recall and F-measure and also, comparative analysis will be performed to prove the better performance of the proposed technique. The analysis from the experimentations showed that the proposed approach has obtained a maximum precision of 0.98 and average precision of 0.95.

Non-Learning Semantic Analysis for Context Discovery and Sentiment Estimation: Transportation Application by Himanshu Verma 〈Dr. Pushkin Kachroo〉, Examination Committee Chair Professor of Electrical and Computer Engineering University of Nevada, Las Vegas With enormous amount of linguistic data present on web, text analysis has become one of the major fields of interest today. This field includes sentiment analysis, information retrieval, text document classification, knowledge based modeling, content similarity measure, data clustering, words prediction/correction, decision making etc. Managing and processing such data has vital importance. The field being quite broad, our focus is mainly on transportation related social media(Twitter) data extraction, text categorization/classification which can be further sub-divided into concept discovery, word sense disambiguation and sentiment analysis to analyze performance of existing transportation system worldwide. Concept discovery is the method of extracting the actual concept/context in which the text is about. This also allows us to filter irrelevant data. Word sense disambiguation is to find the correct sense in which a word is being used in a sentence. It is the basic necessity for concept discovery. iii A lot of research has been done in this field with major improvements. However, when it comes to short texts, the field still seems in nascent stage. Moreover, most of the methods today require huge amount training corpus(database). Arranging such corpus is a cumbersome task and requires a lot of human effort. The other problem with the existing methods are that they require a set of defined concepts from which a concept is chosen and labeled to text. We will consider the case of finding a general context. In this work a novel approach has been proposed for word sense disambiguation which in turn allows us to find general context. For this purpose, I have used the existing knowledge based semantic dictionary called WordNet. This methodology helps in avoiding the use of huge corpus and works for general context recognition. Our focus is on short-texts(Tweets) but the concept is easily applicable to text documents as well. Sentiment measuring technique was applied on extracted data and the scores were mapped to google maps based on the location information present in the tweets. This clearly points out the locations where people are more frustrated with the existing transportation system and need immediate attention for improvements.

Research articles in biomedicine domain have increased exponentially, which makes it more and more difficult for biologists to manually capture all the information they need. Information retrieval technologies can help to obtain the users' needed information automatically. However, it is a great challenge to apply these technologies to biomedicine domain directly because of some domain specific characteristics, such as the abundance of terminologies. To enhance the effectiveness of the biomedical information retrieval, we propose a novel framework based on the state-of-the-art information retrieval methods, called learning to rank, which has been proved effective to rank documents based on their relevance degree. In the framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate our proposed framework is effective in improving the performance of biomedical information retrieval.

Mining people's semantic trajectory behaviours from geotagged photographs

Search Engine (SE) is the most used information retrieval tool in the present scenario. In spite of the huge involvement of users in search engines, their limited capabilities to understand the user context and emotions places high load on the user to maintain the search momentum. Thus the research is being done on the web search process including the users. The aim is to reduce the contextual and emotional mismatch between the SE's and users. Personalized user query processing, current knowledge updation on users, information retrieval effectiveness and user satisfaction are the major challenges to be addressed in this field. To meet the high level requirements of the users and to improve the intellectualization level of the search, various cognitive agent based SE models are proposed in the literature. The challenges of the cognitive agents in present search engine scenario are obtaining sufficient percepts from the environment, understanding it, gathering the knowledge and retrieving the information from the huge volume of data. Recent advancements in the field of semantic search engines, knowledge gathering techniques and information retrieval methods are discussed which can better the cognitive agent based search engines by addressing the challenges. Overall the goal is to enhance the search experience by fewer repetitions of queries and making it easier for the users to retrieve the highly relevant documents of their interest.