Traditional Information Retrieval Systems Research Articles

Enhancing Multimodal Information Retrieval Through Integrating Data Mining and Deep Learning Techniques

Multimodal information retrieval, the task of re trieving relevant information from heterogeneous data sources such as text, images, and videos, has gained significant attention in recent years due to the proliferation of multimedia content on the internet. This paper proposes an approach to enhance multimodal information retrieval by integrating data mining and deep learning techniques. Traditional information retrieval systems often struggle to effectively handle multimodal data due to the inherent complexity and diversity of such data sources. In this study, we leverage data mining techniques to preprocess and structure multimodal data efficiently. Data mining methods enable us to extract valuable patterns, relationships, and features from different modalities, providing a solid foundation for sub- sequent retrieval tasks. To further enhance the performance of multimodal information retrieval, deep learning techniques are employed. Deep neural networks have demonstrated their effectiveness in various multimedia tasks, including image recognition, natural language processing, and video analysis. By integrating deep learning models into our retrieval framework, we aim to capture complex intermodal dependencies and semantically rich representations, enabling more accurate and context-aware retrieval.

A template-based approach for question answering over knowledge bases

In this paper, we address the problem of answering complex questions formulated by users in natural language. Since traditional information retrieval systems are not suitable for complex questions, these questions are usually run over knowledge bases, such as Wikidata or DBpedia. We propose a semi-automatic approach for transforming a natural language question into a SPARQL query that can be easily processed over a knowledge base. The approach applies classification techniques to associate a natural language question with a proper query template from a set of predefined templates. The nature of our approach is semi-automatic as the query templates are manually written by human assessors, who are the experts of the knowledge bases, whereas the classification and query processing steps are completely automatic. Our experiments on the large-scale CSQA dataset for question-answering corroborate the effectiveness of our approach.

Intelligent information retrieval based on Ontology

Traditional search engines can no longer meet the increasing amount of information in today's world, and often face problems such as blurred results and long search times for some queries. And the addition of ontology makes information retrieval more intelligent, so that information data between various fields can be shared, to achieve the purpose of getting the answers users want quickly and accurately. This paper firstly introduces the definition of ontology and analyzes the traditional information retrieval system, and finally summarizes some experimental results in recent years in ontology-based intelligent information retrieval and reviews them to get a more efficient and accurate information retrieval system to achieve the effect of improving the accuracy of search engines.

Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance

The complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums, review sites, news corpora, and blogs available on the internet, product and service reviews have become an essential source of information. In a data-driven world, they will improve services and operational insights to achieve real business benefits and help enterprises remain competitive. Despite the prevalence of textual data, few studies have demonstrated the effectiveness of real-time text mining and reporting tools in firms and organizations. To address this aspect of decision-making, we have developed and evaluated an unsupervised learning system to automatically extract and classify topics and their emotion score in text streams. Data were collected from commercial websites, open-access databases, and social networks to train the model. In the experiment, the polarity score was quantified at four different levels: word, sentence, paragraph, and the entire text using Latent Dirichlet Allocation (LDA). Using subjective data mining, we demonstrate how to extract, summarize, and track various aspects of information from the Web and help traditional information retrieval (IR) systems to capture more information. An opinion tracking system presented by our model extracts subjective information, classifies them, and tracks opinions by utilizing location, time, and reviewers’ positions. Using the online-offline data collection technique, we can update the library topic in real-time to provide users with a market opinion tracker. For marketing or economic research, this approach may be useful. In the experiment, the new model is applied to a case study to demonstrate how the business process improves.

LawRec: Automatic Recommendation of Legal Provisions Based on Legal Text Analysis.

Smart court technologies are making full use of modern science to promote the modernization of the trial system and trial capabilities, for example, artificial intelligence, Internet of things, and cloud computing. The smart court technologies can improve the efficiency of case handling and achieving convenience for the people. Article recommendation is an important part of intelligent trial. For ordinary people without legal background, the traditional information retrieval system that searches laws and regulations based on keywords is not applicable because they do not have the ability to extract professional legal vocabulary from complex case processes. This paper proposes a law recommendation framework, called LawRec, based on Bidirectional Encoder Representation from Transformers (BERT) and Skip-Recurrent Neural Network (Skip-RNN) models. It intends to integrate the knowledge of legal provisions with the case description and uses the BERT model to learn the case description text and legal knowledge, respectively. At last, laws and regulations for cases can be recommended. Experiment results show that the proposed LawRec can achieve better performance than state-of-the-art methods.

Proactive information retrieval

Users interact with digital systems with some underlying tasks in their minds. In our research scope, a task can be either single or multi-staged. A single-staged task is associated with a single information need, whereas a multi-staged one is associated with more than one information needs. An example of a single-staged task is suggesting related papers to an author while they are writing a section of the research papers. An example of a multi-staged task is planning for a vacation, where the different underlying information needs could be 'places to visit', 'booking accommodation' etc. In the process of accomplishing their task objectives, a user often needs to interact with an information retrieval (IR) system to address one or more information needs. For instance, for writing a research paper on a chosen topic, a user needs to look for existing research work related to the topic. Traditional IR systems do not take into account a user's task intent while showing search results to the user. In our work, we propose a methodology towards developing next generation IR systems (i.e. proactive IR systems) which seek to anticipate the task intent of a user from their interactions with digital systems in order to proactively suggest potentially relevant information sources to assist them to complete their tasks. Specifically speaking, in this PhD, we proposed an embedding approach that captures the task semantics from the interactions of a user with a digital system (e.g. laptop, desktop, smartphone etc.). The proposed embedding approach is then applied for the downstream tasks of providing proactive suggestions in both single and multi staged scenarios. For the single-staged task, we propose a simulation setup to simulate a user's reading and writing interactions in a desktop environment. For the multi-staged task, we focus on web search sessions where a user can have multiple information needs corresponding to a search task. We also proposed a reproducible evaluation framework to compare between different proactive suggestion models. Awarded by : Dublin City University, Ireland on 10 August 2021. Supervised by : Gareth Jones. Available at : https://procheta.github.io/sprocheta/Thesis.pdf.

Towards Immersive Generosity: The Need for a Novel Framework to Explore Large Audiovisual Archives through Embodied Experiences in Immersive Environments

Abstract This paper proposes an innovative framework to explore large audiovisual archives using Immersive Environments to place users inside a dataset and create an embodied experience. It starts by outlining the need for such a novel interface to meet the needs of archival scholars and the GLAM sector, and discusses issues in the current modes of access, mostly restrained to traditional information retrieval systems based on metadata. The paper presents the concept of “generous interfaces” as a preliminary approach to address these issues, and argues some of the key reasons why employing Immersive Visual Storytelling might benefit such frameworks. The theory of embodiment is leveraged to justify this claim, showing how a more embodied understanding of a collection can result in a stronger engagement for the public. By placing users as actors in the experience rather than mere spectators, the emergence of narrative is driven by their interactions, with benefits in terms of engagement with the public and understanding of the cultural component. The framework we propose is applied to two existing installations to analyze them in-depth and critique them, highlighting the key directions to pursue for further development.

Improving the Retrieval of Arabic Web Search Results Using Enhanced k-Means Clustering Algorithm.

Traditional information retrieval systems return a ranked list of results to a user’s query. This list is often long, and the user cannot explore all the results retrieved. It is also ineffective for a highly ambiguous language such as Arabic. The modern writing style of Arabic excludes the diacritical marking, without which Arabic words become ambiguous. For a search query, the user has to skim over the document to infer if the word has the same meaning they are after, which is a time-consuming task. It is hoped that clustering the retrieved documents will collate documents into clear and meaningful groups. In this paper, we use an enhanced k-means clustering algorithm, which yields a faster clustering time than the regular k-means. The algorithm uses the distance calculated from previous iterations to minimize the number of distance calculations. We propose a system to cluster Arabic search results using the enhanced k-means algorithm, labeling each cluster with the most frequent word in the cluster. This system will help Arabic web users identify each cluster’s topic and go directly to the required cluster. Experimentally, the enhanced k-means algorithm reduced the execution time by 60% for the stemmed dataset and 47% for the non-stemmed dataset when compared to the regular k-means, while slightly improving the purity.

Enhancing information retrieval performance by using social analysis

In recent decades, researchers have realized that social networks are important sources for adhering to the evolution of many aspects of Information Retrieval (IR). These social networks have produced vast amounts of important information that are not covered by traditional IR systems. This improvement, which has become one of the main applications of IR, offers several social features such as the conversational exchange and the share of opinions by users, and the association of users with the same interests. This work introduces a model of Social Information Research that takes into account social information on users and exploits them as a second source of information for a given query. In the proposed IR system, the quality of results improved by the analysis of user’s needs and by comparing other users’ social data.

Semantically-enhanced information retrieval using multiple knowledge sources

Classical or traditional Information Retrieval (IR) approaches rely on the word-based representations of query and documents in the collection. The specification of the user information need is completely based on words figuring in the original query in order to retrieve documents containing those words. Such approaches have been limited due to the absence of relevant keywords as well as the term variation in documents and user’s query. The purpose of this paper is to present a new method to Semantic Information Retrieval (SIR) to solve the limitations of existing approaches. Concretely, we propose a novel method SIRWWO (Semantic Information Retrieval using Wikipedia, WordNet, and domain Ontologies) for SIR by combining multiple knowledge sources Wikipedia, WordNet, and Description Logic (DL) ontologies. In order to illustrate the approach SIRWWO, we first present the notion of Labeled Dynamic Semantic Network (LDSN) by extending the notions of dynamic semantic network and extended semantic net based on WordNet (and DAML ontology library). According to the notion of LDSN, we obtain the notion of Weighted Dynamic Semantic Network (WDSN, intuitively, each edge in WDSN is assigned to a number in the [0, 1] interval) and give the WDSN construction method using Wikipedia, WordNet, and DL ontology. We then propose a novel metric to measure the semantic relatedness between concepts based on WDSN. Lastly, we investigate the approach SIRWWO by using semantic relatedness between users’ query keywords and digital documents. The experimental results show that our proposals obtain comparable and better performance results than other traditional IR system Lucene.

Novel approach in multilingual and mixed English-Arabic test collection

Mixing languages together in text and in talking is a major feature in non-English languages in developing countries. This mixed grammar is also emerging in SMS, Facebook communication, searching the web and any future attempts also may increase the footprint of such a mixed language knowledge base. Traditional information retrieval (IR) and cross-language information retrieval (CLIR) systems do not exploit this natural human tendency as the underlying assumption is that user query is always monolingual. Accordingly, the majority of the text collections are either monolingual or multilingual. This paper explores the trends of mixed-language querying and writing. It also shows how the corpus is validated statistically and how an Arabic lexicon can be extracted using co-occurrence statistics. Results showed that the distribution of frequencies of words in the corpus is very skewed the vocabulary growth is a good fit. The results of how to handle mixed queries are also summarised.

Novel approach in multilingual and mixed English-Arabic test collection

Mixing languages together in text and in talking is a major feature in non-English languages in developing countries. This mixed grammar is also emerging in SMS, Facebook communication, searching the web and any future attempts also may increase the footprint of such a mixed language knowledge base. Traditional information retrieval (IR) and cross-language information retrieval (CLIR) systems do not exploit this natural human tendency as the underlying assumption is that user query is always monolingual. Accordingly, the majority of the text collections are either monolingual or multilingual. This paper explores the trends of mixed-language querying and writing. It also shows how the corpus is validated statistically and how an Arabic lexicon can be extracted using co-occurrence statistics. Results showed that the distribution of frequencies of words in the corpus is very skewed the vocabulary growth is a good fit. The results of how to handle mixed queries are also summarised.

Analysis of Information Support of Knowledge Management in Ukraine

The article defines the place and components of IT solutions in the knowledge management system. The author substantiates that the use of modern IT solutions in the knowledge management system is an essential component of creation, search, transfer, exchange, development and effective use of knowledge. A large number of different technologies are used to integrate into a single complex of knowledge management system, including traditional automation systems and information retrieval systems, e-mail, corporate networks and Internet services, databases and data warehouses, electronic document management systems, specialized data processing programs, expert systems and databases knowledge In this case, a computer remains as a main tool of information management knowledge management in Ukraine. The scholar analysed the dynamics of the major indicators of computer devices’ use of by enterprises. The paper determined that not all enterprises in Ukraine use computers in their business activities. The proportion of enterprises, which used computers in 2017, amounted for 95.4%, which is by 0.2% more in comparison with 2015. The best situation in this aspect is in such types of economic activities as professional, scientific and technical activities; information and telecommunications; water supply; sewage, waste management. However, on average only every third employee in Ukraine used a computer to perform his/her professional duties. The author examined the main Internet-based jobs, where traditional activities are the most popular ones, namely: sending or receiving various e-mails, receiving information about goods and services. At the same time, videoconferencing is less popular, which is a negative factor in creating effective communication in the transmission, dissemination and exchange of knowledge. The paper explored the degree of using the possibilities of a corporate website for personnel training. The author determined that only every tenth enterprise in Ukraine uses its own website for the professional development of the employees. The scholar substantiated the features of social media, which promote the efficiency of their use as a tool for knowledge management. Exchanging views, opinions or knowledge within the enterprise and engaging customers in the development or innovation of goods and services remain the least popular objectives of social media work.

Information retrieval system based semantique and big data

In traditional word-based information retrieval systems, a document is considered a set of words representing graphs without semantics. In this paper, we focus on enriching the similarity measure by using synonymy and performance evaluation of semantic indexing approaches to a document corpus. We will also present comparisons showing that the use of synonymy with Leacock and Chodorow measures increases the semantic similarity that makes research more efficient.

Query Expansion Using Conceptual Knowledge in Computer Science

Traditional information retrieval systems retrieve documents based on keyword based matching, thereby incapable to provide results according to user information needs. These systems do not consider the semantic relationships among the user query keywords, thus cannot interpret the query context accurately. Query Expansion technique enhances the performance of information retrieval process by expanding user query with relevant meaningful concepts. Ontology represents semantics of a domain, and emerges as an important resource for query expansion. In this paper, we propose a query expansion framework based on conceptual knowledge derived from ontology in the domain of computer science. The system prototype results show good average precision percentage of 89.2% over keyword based search.

Talk to me: Exploring user interactions with the Amazon Alexa

Amazon Alexa is a voice-controlled application that is rapidly gaining popularity. We examined user interactions with this technology, and focused on the types of tasks requested of Alexa, the variables that affect user behaviors with Alexa, and Alexa’s alternatives. The data about Alexa usage were collected from 19 participants via the online questionnaire and diary methods over the course of several days. The results indicate that across all age groups, Alexa was primarily used for checking weather forecasts, playing music, and controlling other devices. Several participants reported using Apple Siri and Google Now applications in addition to Alexa for similar purposes except for controlling other devices. Alexa uses over the weekends were more frequent than on weekdays, but its overall usage tended to decrease over time. The users reported being satisfied with Alexa even when it did not produce sought information, suggesting that the interaction experience is more important to the users than the interaction output. More work is required to understand whether users treat Alexa and similar voice-controlled applications as primarily a traditional information retrieval system, a casual leisure system, a control interface for smart home devices, or, simply, a new toy.

A Semantic Multi-Agent system to Exchange Information between Hospitals

The amount of information in hospitals and the medical field is consistently increasing. Traditional information retrieval systems in hospitals are lacking, and the medical industry has been slow to adopt technology because it requires accurate and effective information. In order to achieve a solution, Statistics and Collaborative Knowledge Exchange (SCKE) is a multi-agent system that allows the retrieval and exchange of information between hospitals. The proposed system uses semantic search techniques to provide results that are more accurate. We examined the system accuracy, and the experiment results show a high accuracy rate, regardless of the number of queries.

Spatio-Temporal Contextualization of Queries for Microtexts in Social Media: Mathematical Modeling

In this paper, we present our ongoing project on query contextualization by integrating all possible IoT-based data sources. Most importantly, mobile users are regarded as the IoT sensors which can be the textual data sources with spatio-temporal contexts. Given a large amount of text streams, it has been difficult for the traditional information retrieval systems to conduct the searching tasks. The goal of this work is i) to understand and process microtexts in social media (e.g., Twitter and Facebook), and ii) to reformulate the queries for searching for relevant microtexts in these social media.

Using knowledge-based relatedness for information retrieval

Traditional information retrieval (IR) systems use keywords to index and retrieve documents. The limitations of keywords were recognized since the early days, specially when different but closely related words are used in the query and the relevant document. Query expansion techniques like pseudo-relevance feedback (PRF) and document clustering techniques rely on the target document set in order to bridge the gap between those words. This paper explores the use of knowledge-based semantic relatedness techniques to overcome the vocabulary mismatch between the query and documents, both on IR and Passage Retrieval for question answering. We performed query expansion and document expansion using WordNet, with positive effects over a language modeling baseline on three datasets, and over PRF on two of those datasets. Our analysis shows that our models and PRF are complementary; in that, PRF is better for easy queries, and our models are stronger for difficult queries and that our models generalize better to other collections, being more robust to parameter adjustments. In addition, we show that our method has a positive impact in an end-to-end question answering system for Basque and that it can be readily applied to other knowledge bases, as our good results using Wikipedia show, paving the way for the use of other knowledge structures such as medical ontologies and linked data repositories.

DataSift: An Expressive and Accurate Crowd-Powered Search Toolkit

Traditional information retrieval systems have limited functionality. For instance, they are not able to adequately support queries containing non-textual fragments such as images or videos, queries that are very long or ambiguous, or semantically-rich queries over non-textual corpora. In this paper, we present DataSift, an expressive and accurate crowd-powered search toolkit that can connect to any corpus. We provide a number of alternative configurations for DataSift using crowdsourced and automated components, and demonstrate gains of 2–3x on precision over traditional retrieval schemes using experiments on real corpora. We also present our results on determining suitable values for parameters in those configurations, along with a number of interesting insights learned along the way.