Topic modelling is a beneficial technique used to discover latent topics in text collections. But to correctly understand the text content and generate a meaningful topic list, semantics are important. By ignoring semantics, that is, not attempting to grasp the meaning of the words, most of the existing topic modelling approaches can generate some meaningless topic words. Even existing semantic-based approaches usually interpret the meanings of words without considering the context and related words. In this article, we introduce a semantic-based topic model called semantic-LDA that captures the semantics of words in a text collection using concepts from an external ontology. A new method is introduced to identify and quantify the concept–word relationships based on matching words from the input text collection with concepts from an ontology without using pre-calculated values from the ontology that quantify the relationships between the words and concepts. These pre-calculated values may not reflect the actual relationships between words and concepts for the input collection, because they are derived from datasets used to build the ontology rather than from the input collection itself. Instead, quantifying the relationship based on the word distribution in the input collection is more realistic and beneficial in the semantic capture process. Furthermore, an ambiguity handling mechanism is introduced to interpret the unmatched words, that is, words for which there are no matching concepts in the ontology. Thus, this article makes a significant contribution by introducing a semantic-based topic model that calculates the word–concept relationships directly from the input text collection. The proposed semantic-based topic model and an enhanced version with the disambiguation mechanism were evaluated against a set of state-of-the-art systems, and our approaches outperformed the baseline systems in both topic quality and information filtering evaluations.

BACKGROUND:The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. OBJECTIVE:The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? METHODS:Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. RESULTS:The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. CONCLUSIONS:The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Whilst digital health data provides great benefits for improved and effective patient care and organisational outcomes, the quality of digital health data can sometimes be a significant issue. Healthcare providers are known to spend a significant amount of time on assessing and cleaning data. To address this situation, this paper presents six Digital Health Data Imperfection Patterns that provide insight into data quality issues of digital health data, their root causes, their impact, and how these can be detected. Using the CRISP-DM methodology, we demonstrate the utility and pervasiveness of the patterns at the emergency department of Australia's major tertiary digital hospital. The pattern collection can be used by health providers to identify and prevent key digital health data quality issues contributing to reliable insights for clinical decision making and patient care delivery. The patterns also provide a solid foundation for future research in digital health through its identification of key data quality issues, root causes, detection techniques, and terminology.

Process mining provides a range of methods and techniques to analyse business processes through information stored in so-called event logs. The richer these event logs and the higher quality they are, the more insights we can obtain. Till now, information in the form of unstructured text, e.g. notes, comments, reviews, and posts, is not fully and systematically exploited for the purposes of log enrichment. In this paper, we introduce Text2EL, a two-phase event log enrichment approach based on unstructured text. In Phase 1, events, case attributes, and event attributes are extracted from unstructured text associated with organisational processes. In Phase 2, the extracted events and attributes are semantically and contextually validated before enriching the event log. Our approach applies techniques from natural language processing, sentence embeddings, and contextual and expression validation. We evaluated the completeness, concordance, and correctness of an enriched event log through experiments with a real-life healthcare data set. The experiments showed the feasibility and applicability of our approach.

Most of the existing semantic-based topic models and topic generation approaches use external knowledgebases or ontology to interpret the meanings of the words. However, general ontologies do not cover many ambiguous or specific domain-related words in a text collection. Hence those ambiguous or domain-specific words are neglected in capturing the meanings in topic generation. In this paper, we introduce an approach to disambiguate the unmatched words in a text collection based on related and similar meaning words. Word embeddings are applied to discover similar or related words. We evaluated the topic generation approach with our ambiguity handling technique with a set of state-of-the-art systems which uses an external ontology. Our approach outperformed, and the generated topics were more meaningful. Our ambiguity handling approach interpreted all the important words and included them in the topic generation process.

Topic modeling discovers the hidden topics in a document collection. Most of the existing topic models focus only on word usage and generate the topics based on the word frequency and co-occurrence without considering the meaning of the text. In this paper, we propose a novel approach to generate a semantic pattern-based topic representation based on the meaning of the text to represent the topics in a document collection. The proposed approach considers both the semantics and co-occurrence of words to generate a set of frequent semantic patterns to represent each topic. The semantics are captured by matching the words in each topic with concepts in the Probase ontology. A set of frequent semantic patterns in each topic is generated based on the co-occurrence of the matched words to represent the topic. Hence, our approach differs from traditional topic models because of the meaningful frequent semantic patterns generated based on the ontology. The proposed topic representation was evaluated in terms of topic quality and information filtering performance against a set of state-of-the-art systems. Perplexity, coherence, and topic word distribution were examined in the topic quality evaluation. The generated frequent semantic patterns were used as features for the information filtering evaluation. Our topic representation outperformed in all the evaluations.

Card payment fraud is a serious problem, and a roadblock for an optimally functioning digital economy, with cards (Debits and Credit) being the most popular digital payment method across the globe. Despite the occurrence of fraud could be relatively rare, the impact of fraud could be significant, especially on the cardholder. In the research, there have been many attempts to develop methods of detecting potentially fraudulent transactions based on data mining techniques, predominantly exploiting the developments in the space of machine learning over the last decade. This survey proposes a taxonomy based on a review of existing research attempts and experiments, which mainly elaborates the approaches taken by researchers to incorporate the (i) business impact of fraud (and fraud detection) into their work , (ii) the feature engineering techniques that focus on cardholder behavioural profiling to separate fraudulent activities happening with the same card, and (iii) the adaptive efforts taken to address the changing nature of fraud. Further, there will be a comparative performance evaluation of classification algorithms used and efforts of addressing class imbalance problem. Forty-five peer-reviewed papers published in the domain of card fraud detection between 2009 and 2020 were intensively reviewed to develop this paper.

Topic modelling is the state of the art technique for understanding, organizing, and extracting information from text collections. Traditional topic modeling approaches apply probabilistic techniques to generate the list of topics from collections. Nevertheless, human understands, summarizes and discovers the topics based on the meaning of the content. Hence, the quality of the topic models can be improved by grasping the meaning from the content. In this paper, we propose an approach to identify sets of meaningful terms based on ontology, called Semantic-based Patterns, which represent the content of a collection of documents. A set of related semantic-based patterns can be used to represent a latent topic in the collection. The proposed Topic Representation using Semantic-based Patterns aims to generate semantically meaningful patterns based on ontology rather than term co-occurrence as what existing topic modelling methods do. The semantically meaningful patterns were evaluated by applying the information filtering to semantic-based topic representation. The semantic based patterns were used as features for information filtering and were evaluated by comparing against popular information filtering baseline systems. Topic quality was evaluated in terms of topic coherence and perplexity. The experimental results verified that the quality of the proposed patterns was better than features used in baseline systems for information filtering. Further, the quality of topic representation outperforms the generated topics of other topic modeling approaches.

Latent Dirichlet Allocation (LDA) has become the most stable and widely used topic model to derive topics from collections of documents where it depicts different levels of success based on diversified domains of inputs. Nevertheless, it is a vital requirement to evaluate the LDA against the quality of the input. The noise and uncertainty of the content create a negative influence on the topic model. The major contribution of this investigation is to critically evaluate the LDA based on the quality of input sources and human perception. The empirical study shows the relationship between the quality of the input and the accuracy of the output generated by LDA. Perplexity and coherence have been evaluated with three data-sets (RCV1, conference data set, tweets) which contain different level of complexities and uncertainty in their contents. Human perception in generating topics has been compared with the LDA in terms of human defined topics. Results of the analysis demonstrate a strong relationship between the quality of the input and generated topics. Thus, highly relevant topics were generated from formally written contents while noisy and messy contents lead to generate meaningless topics. A considerable gap is noticed between human defined topics and LDA generated topics. Finally, a concept-based topic modeling technique is proposed to improve the quality of topics by capturing the meaning of the content and eliminating the irrelevant and meaningless topics.

Text contents are overloaded with the digitization of the data and new contents are transmitted through many sources by generating a large volume of information, which spreads all over the world through different communication media. Therefore, text data is available everywhere and reading, understanding and analysing the text data has become a main activity in daily routine. With the increment of the volume and the variety of information, organizing and searching, the required information has become vital. Topic modelling is the state of the art for information organization, understanding and extracting the content. Most of the prevailing topic models use the probabilistic approaches and consider the frequency and the co-occurrence to discover the topics from collections of documents. The proposed research aims to address the existing problems of topic modeling by introducing a concept embedded topic model which generates the most relevant and meaningful topics by understanding the content. The research includes approaches to understand the semantic elements from the content, domain identification of concepts and provide most suitable topics without getting the number of topics from the user beforehand. Capturing the semantics of document collections and generating the most related set of topics according to the actual meaning will be the significance of this research.

Mathematics includes solving a variety of problems by applying theories and formulas. Thus mathematical problem solving requires performing arithmetical operations by using analytical and problem solving skills. Sentence based mathematical problems contains real world scenarios and requires to apply both mathematical and problem analyzing knowledge to solve problems. Human beings solve sentence based mathematical problems by applying different mathematical formulas and theorems to the comprehend questions. Understanding the sentence based questions requires an additional effort to grab the content and grasped content should be mapped with known concepts in terms of variables. Organizing the variables and formulas by understanding the relationships and properties would be important to formulate the answer. Thus the content can be easily modeled using an ontological approach and the problem solving can be accomplished by querying the ontology using a multi agent approach. Sentenced based mathematical problem solving approach demonstrates a system which can solve mathematical questions by acquiring the semantics of the question and applying learnt formulas. Information extraction from the question, ontology based knowledge representation, multi agent based ontology querying and answer generation with explanations can be defined as major functions. This approach can be used to introduce effective intelligent tutoring systems in any domain.

Learning is the act of acquiring new knowledge or updating the existing knowledge about different types of information.Human being starts the learning process with a minimal knowledge and then expands the knowledge by adding new information to the existing knowledge.Thus it is possible to model the learning process as ontology modelling process and the research has been conducted to develop an automated ontological solution for continuous learning.The solution demonstrates a system which can learn and expand the knowledge somewhat similar to a human being.The system consists of three major functions named as knowledge extraction, knowledge comparison and knowledge integration.This approach consists of two main research components.The ontology construction mechanism of the system was designed with over 60% of expected accuracy and knowledge integration can be evaluated over 75% accuracy.This system can be used as the main learning process of any application.

Topic modelling is a state-of-the-art technique to understand, categorize and summarise the text and is beneficial to discover the hidden themes in text collections. The existing topic modelling approaches pay less or no attention to capturing the semantics of words. Hence, meaningless topics are generated. This research addresses the main problem of existing topic modelling approaches by introducing two semantic-based topic generation approaches. The thesis has made main contributions to topic modelling and text mining domains by introducing semantic-based topic representation, semantic topic model and an ambiguity handling approach. The research outcomes are beneficial for many text mining applications.