Integrating data and text mining processes for digital library applications

DEFINING TEXT MINING Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns. In the case of text mining, however, the data sources are document collections, and interesting patterns are found not among formalized database records but in the unstructured textual data in the documents in these collections. Certainly, text mining derives much of its inspiration and direction from seminal research on data mining. Therefore, it is not surprising to find that text mining and data mining systems evince many high-level architectural similarities. For instance, both types of systems rely on preprocessing routines, pattern-discovery algorithms, and presentation-layer elements such as visualization tools to enhance the browsing of answer sets. Further, text mining adopts many of the specific types of patterns in its core knowledge discovery operations that were first introduced and vetted in data mining research. Because data mining assumes that data have already been stored in a structured format, much of its preprocessing focus falls on two critical tasks: Scrubbing and normalizing data and creating extensive numbers of table joins. In contrast, for text mining systems, preprocessing operations center on the identification and extraction of representative features for natural language documents.

An information retrieval system for college search is a crucial tool for students to find the best college for their academic needs. The system uses text mining, web mining, data mining, machine learning, and semantic web techniques to provide relevant and accurate results to users. The system collects data from various sources such as college websites, student reviews, and academic databases to create a comprehensive database of colleges and their relevant information. Users can search for colleges based on their preferences, such as location, academic program, admission requirements, tuition fees, and other factors. The system also provides personalized recommendations based on the user's search history and preferences. The proposed system aims to improve the college search process for students by providing a user-friendly interface and accurate information to make informed decisions about their education.

Data mining is a process of discovering useful information from a database and analysis of extracted information. Text mining uses many techniques of data mining. It primarily deals with unstructured data. Web mining is an extension of text mining since it deals with unstructured data. Data mining relates to find data from “static databases” which contains “structured” data where as, web mining plays with data that are “dynamic” and “unstructured”. In this papers our goal is to mine biomedical data from hypertext documents (e.g., mining data from web contents) using text mining techniques with the help of “biomedical ontology”. Web data repositories are the hypertext documents. Texts in the Hypertext documents are unstructured and they contain Hypertext Markup Language (HTML) tags, scripting languages, images, audios, videos, URLs etc. We collect a number of documents using Google crawler and preprocess the hypertext documents and extract the text data. Next, we identify whether a word is a biomedical entity or not by using a biomedical database the “Unified Medical Language System (UMLS) metathesaurus”. The mapping of biomedical entity from the metathesaurus will be done based on keyword query. Then we apply the result to re-rank the web documents to find most relevant documents. We conclude that the more occurrence of a biomedical entity in a page, the more relevant the page is, and thus, we can re-rank the documents to find the most relevant documents by using text mining technique.

The rise of the ``big data'' era has created a pressing demand for educating many data scientists and engineers quickly at low cost. It is essential they learn by working on assignments that involve real world data sets to develop the skills needed to be successful in the workplace. However, enabling instructors to flexibly deliver all kinds of data science assignments using real world data sets to large numbers of learners (both on-campus and off-campus) at low cost is a significant open challenge. To address this emerging challenge generally, we develop and deploy a novel Cloud-based Lab for Data Science (CLaDS) to enable many learners around the world to work on real-world data science problems without having to move or otherwise distribute prohibitively large data sets. Leveraging version control and continuous integration, CLaDS provides a general infrastructure to enable any instructor to conveniently deliver any hands-on data science assignment that uses large real world data sets to as many learners as our cloud-computing infrastructure allows at very low cost. In this paper, we present the design and implementation of CLaDS and discuss our experience with using CLaDS to deploy seven major text data assignments for students in both an on-campus course and an online course to work on for learning about text data retrieval and mining techniques; this shows that CLaDS is a very promising novel general infrastructure for efficiently delivering a wide range of hands-on data science assignments to a large number of learners at very low cost.

In order to find significant patterns and fresh ideas, free-form content is transformed into structured format using a process known as text mining or text data mining. It enables businesses to easily locate important information in texts like emails, social media posts, support requests, chatbots, and other sorts of text. Text mining enables businesses to anticipate possible threats from rivals, react quickly to production or delivery problems, and provide more individualised customer service. Businesses employ text mining for a range of functions, including production, IT, marketing, sales, and customer service. By carefully examining the phrases used in the source texts, topic modelling aims to pinpoint the recurrent themes in a corpus. These concepts are known as “topics”. As a result, textual data may be measured and used in quantitative analysis. In this sector, there are several subject modelling kinds that differ from one another based on a few unique traits and criteria. In our paper we have represented mainly 3 types of topic modelling techniques namely Latent Semantic Analysis (LSA), Hierarchical Dirichlet Process (HDP), and Latent Dirichlet Analysis (LDA) and calculated the coherence score of each method and compared them. And we have infused the concept of BERT with this topic modelling models and proposed a new model called HDP BERT and calculated the coherence Score and clusters the topics. At the end the n-grams features are applied to all 4 models and compared among each other in bases of uni, bi and trigram rate percentage.

Now-a-days information’s are stored electronically in databases. Extracting reliable, unknown and useful information from the abundant source is an eminent task. Data mining and Text mining are the process for extracting unknown and useful information. Text Mining is the process of extracting interesting and non-trivial patterns or knowledge from text documents. This paper presents the related activities and focuses on preprocessing steps in text mining. Keywords— DBMS, Data Mining, Text Mining, Preprocessing, Tokenization, Stemming, POS tagging.

The fields of taxonomy and biodiversity research have witnessed an exponential growth in published literature. This vast corpus of articles holds information on the diverse biological traits of organisms and their ecologies. However, access to and extraction of relevant data from this extensive resource remain challenging. Advances in text and data mining (TDM) and Natural Language Processing (NLP) techniques offer new opportunities for liberating such information from literature. Testing and using such approaches to annotate articles in machine-actionable formats is, therefore, necessary to enable the exploitation of existing knowledge in new biology, ecology and evolution research. Here, we explore the potential of these methods to annotate and extract organismal trait data for the most diverse animal group on Earth, the arthropods. The article processing workflow uses manually curated trait dictionaries with trained NLP models to perform labelling of entities and relationships of thousands of articles. A subset of manually annotated documents facilitated the formal evaluation of the performance of the workflow in terms of entity recognition and normalisation and relationship extraction, highlighting several important technical challenges. The results are made available to the scientific community through an interactive web tool and queryable resource, the ArTraDB Arthropod Trait Database. These methodological explorations provide a framework that could be extended beyond the arthropods, where TDM and NLP approaches applied to the taxonomy and biodiversity literature will greatly facilitate data synthesis studies and literature reviews, the identification of knowledge gaps and biases, as well as the data-informed investigation of ecological and evolutionary trends and patterns.

The digital library is an information providing service in which all the information resources are available in computer process-able form. The functions of acquisition, storage, preservation, retrieval, access and display are carried out through the use of digital technologies. In this way we can say that Digital Libraries are the systems which provide users with coherent access to a very large, organized repository of information and knowledge. Digital Libraries may be accessed remotely by the users. The retrieval of information from a digital library database is easy and time saving process. Present paper discusses about features of a digital library, role of a digital library in dissemination of information, architecture of digital library, federal search model and its features, digital library standards, steps in building digital library and problems of digital initiatives in India in detail.

of works held in research and academic libraries and other relevant institutions, for research and private study; * clarify the current EU exception permitting the use of works that were made to be permanently located in the public space (the 'panorama exception'), to take into account new dissemination channels.

Airport Performance - a multifarious review of literature

This paper presents a novel approach to model-driven development of Digital Library (DL) systems. The overall idea is to allow Digital Library systems designers (e.g. information architects, librarians, domain experts) to easily design such systems by using a visual language. We designed a Domain Specific Visual Language for such a purpose and developed a framework supporting it; this framework helps designers by automatically generating code for the defined Digital Library system, so that they do not have to get involved into technical issues concerning its deployment. In our approach, both Human-Computer Interaction and Computer Supported Collaborative Work techniques are exploited when generating interfaces and services for the specific Digital Library domain.

There is global attention on new data analytic methods. Data scraping (a typical first step for advanced data analytics), text and data mining (TDM, the extraction of knowledge from data) and machine learning (ML, often also simply referred to as Artificial Intelligence or AI) are seen as critical technologies. The legal issues involved in the regulation of data range from privacy and data protection (such as the GDPR) to proprietary approaches (such as copyright, database rights, or proposed new rights in data themselves). This paper focusses on one specific intervention, the introduction of two exceptions for text and data mining in the Directive on Copyright in the Digital Single Market (CDSM). Art. 3 is a mandatory exception for text and data mining (TDM) for the purposes of scientific research; Art. 4 permits text and data mining by anyone but with rightsholders able to “contract-out” (Art. 4), for example preventing TDM use of publicly available online content by “machine-readable means”. We trace the context of using the lever of copyright law to enable emerging technologies and support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of (permanent) copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction (which leaves the communication of research results in a grey zone). We also argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for accessing training data that offer the wrong incentives. We also identify some old and new areas of the EU acquis which will play a crucial role in the future relationship of EU copyright law with technological innovation.

The purpose of the article. Sustainability development issues, particularly Environmental, Social, and Governance (ESG) factors, are becoming increasingly relevant in corporate reporting, driven by rising environmental awareness and regulatory requirements. The aim of the study is the evaluation of ESG disclosures of selected Polish banks. The aim of the paper fills a gap in the Polish academic literature in economics and finance by analyzing the volume and size of non-financial ESG disclosures through computer text mining techniques. Methodology. This study applies text mining techniques to evaluate the ESG volume and size from 107 financial reports issued by Polish banks between 2006 and 2023. For this purpose, selected tools for computer-based analysis of textual data (text mining) are used. The primary methods include the emotional attitude (sentiment), analysis of the number of words regarding ESG, and analysis of the readability of ESG volume and size contained in company reports. Results of the research. The study reveals that ESG excerpts are more neutral or less optimistic compared to integrated reports, which tend to have a more positive tone. Additionally, sustainability disclosures are written in a complex language, and the volume of these reports has been increasing over time, likely due to new regulations and growing awareness of sustainability issues. The study focuses on Polish banks but suggests expanding future research to other sectors.

The availability of massive sets of unstructured data has opened up new opportunities for businesses to gain meaningful insights into what is currently happening in their organization and market place. Unfortunately, unstructured data is often challenging to work with, as it requires specialized toolsets, techniques, knowledge and skills to engage with the data. This study used a systematic review (SLR) process to explore the current and conversant state of affairs in the field of text mining (TM) techniques that exist for the processing of natural language in unstructured text datasets across a broad expanse of applications and domains. A comprehensive literature search yielded 1022 eligible articles from five prominent bibliographic databases, narrowed down to 89 articles for review. Information related to TM techniques, the TM process, applications, challenges and recommendations for the improvement of TM results were extracted and synthesized. Eighteen TM techniques were identified and used to complete a variety of tasks such as data pre-processing, information retrieval, information extraction, text classification, clustering, topic modeling, text summarization and sentiment analysis or opinion mining. No single TM technique may be suitable for all text representation and extraction requirements. Therefore, context and applicability are key factors for TM technique selection.KeywordsText miningText mining techniquesUnstructured dataKnowledge discovery

It is seen that distance education environments are more frequently used due to the progress of internet today. The data acquired from these environments are commonly irregular journal records and text-based communication messages. Digital mining applications are used in order to convert these irregular data into regular, and cleaning out the information stacks. The purpose of this research is to reveal the usage of the types of digital mining application, which are being applied within the distance education environments. For this purpose, a situation-based study was carried out which was related to the subjects of data mining, web mining and text mining, being used in the distance education environments. As a result of the research, a relationship was determined between the types of digital mining. Key words: Text mining, data mining, web mining.