Constituent vs Dependency Parsing-Based RDF Model Generation from Dengue Patients’ Case Sheets

Electronic Health Record (EHR) systems in healthcare organisations are primarily maintained in isolation from each other that makes interoperability of unstructured(text) data stored in these EHR systems challenging in the healthcare domain. Similar information may be described using different terminologies by different applications that can be evaded by transforming the content into the Resource Description Framework (RDF) model that is interoperable amongst organisations. RDF requires a document’s contents to be translated into a repository of triplets (subject, predicate, object) known as RDF statements. Natural Language Processing (NLP) techniques can help get actionable insights from these text data and create triplets for RDF model generation. This paper discusses two NLP-based approaches to generate the RDF models from unstructured patients’ documents, namely dependency structure-based and constituent(phrase) structure-based parser. Models generated by both approaches are evaluated in two aspects: exhaustiveness of the represented knowledge and the model generation time. The precision measure is used to compute the models’ exhaustiveness in terms of the number of facts that are transformed into RDF representations.

Association of Corporate Governance with Intellectual Capital Performance: A Study of S&P 200 Companies

This paper aims to investigate the relationship between intellectual capital efficiency and the attributes of corporate governance in the top 116 companies from 2012 to 2018. The VAIC has been calculated for the sample chosen, which did not include financial companies. The relationship between corporate governance structure and intellectual capital performance was investigated using panel data regression analysis. Results have shown that board size is negatively associated with intellectual capital and its components. CEO duality, on the other hand, is not found to be associated with intellectual capital performance. This study also shows that intellectual capital performance and human capital efficiency are negatively correlated with board independence, Indian promoters, institutional ownership, and foreign ownership. The components of intellectual capital performance, on the other hand, have yielded mixed results. The findings could be useful to policymakers while deciding on the composition and structure of the board.

Ontology Versioning Driven by Instance Evolution in the τOWL Framework

Like other components of Semantic Web-based applications, ontologies are evolving over time to reflect changes in the real world. Several of these applications require keeping a full-fledged history of ontology changes so that both ontology instance versions and their corresponding ontology schema versions are maintained. Updates to an ontology instance could be non-conservative that is leading to a new ontology instance version no longer conforming to the current ontology schema version. If, for some reasons, a non-conservative update has to be executed, in spite of its consequence, it requires the production of a new ontology schema version to which the new ontology instance version is conformant so that the new ontology version produced by the update is globally consistent. In this paper, we first propose an approach that supports ontology schema changes which are triggered by non-conservative updates to ontology instances and, thus, gives rise to an ontology schema versioning driven by instance updates. Note that in an engineering perspective, such an approach can be used as an incremental ontology construction method driven by the modification of instance data, whose exact structure may not be completely known at the initial design time. After that, we apply our proposal to the already established [Formula: see text]OWL (Temporal OWL 2) framework, which allows defining and evolving temporal OWL 2 ontologies in an environment that supports temporal versioning of both ontology instances and ontology schemas, by extending it to also support the management of non-conservative updates to ontology instance versions. Last, we show the feasibility of our approach by dealing with its implementation within a new release of the [Formula: see text] OWL-Manager tool.

Efficient Distributed Matrix Factorization Alternating Least Squares (EDMFALS) for Recommendation Systems Using Spark

With the emergence of e-commerce and social networking systems, the use of recommendation systems gained popularity to predict the user ratings of an item. Since the large volume of data is generated from various sources at high speed, predicting the ratings accurately in real-time adds enormous benefit to the users while choosing the correct item. So a recommendation system must be capable enough to predict the rating accurately when the data are large. Apache Spark is a distributed framework well suited for processing large datasets and real-time data streams. In this paper, we propose an efficient matrix factorisation algorithm based on Spark MLlib alternating least squares (ALS) for collaborative filtering. The optimisations used for the proposed algorithm using Tungsten improved the performance of the algorithm significantly while doing the predictions. The experimental results prove that the proposed work is significantly faster for top-N recommendations and rating predictions compared with the existing works.

Examining How Employer Brand Perceptions Differ for Potential and Existing Employees in the Indian IT Sector: A Longitudinal Study

Employer branding as a tool is consistently gaining importance to attract and retain talent. Previous studies have observed employer branding process through potential employee’s perspective while others have taken current employees as their sample. It has been well acknowledged that variation exists in perception of potential and existing employees regarding the value propositions that an employer offers. This variation may result in employee disengagement or lower commitment. Considering the importance of the problem, this paper attempts to explore and analyse this phenomenon of variation in employer brand perceptions that exists between potential and existing employees. To achieve this objective, a longitudinal study consisting of 411 employees of top IT companies in India is conducted. Responses from the same sample are collected at two different points; first, when the respondents are final year students (potential employees/applicants) and the second instance is when they are absorbed into the company after induction and training. The results reveal that certain differences (based on instrumental-symbolic framework) are observed in the relative importance of employer brand attributes for the same individuals, i.e. when they are looking for a job and when they are working as an employee. Also, we conclude that though the differences cannot be eliminated totally, however, it can be minimised to a certain level by focusing on certain touchpoints.

Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals

In radar signal processing, detecting the moving targets in a cluttered background remains a challenging task due to the moving out and entry of targets, which is highly unpredictable. In addition, detection of targets and estimation of the parameters have become a major constraint due to the lack of required information. However, the appropriate location of the targets cannot be detected using the existing techniques. To overcome such issues, this paper presents a developed Deep Convolutional Neural Network-enabled Neuro-Fuzzy System (Deep CNN-enabled Neuro-Fuzzy system) for detecting the moving targets using the radar signals. Initially, the received signal is presented to the Short-Time Fourier Transform (STFT), matched filter, radar signatures-enabled Deep Recurrent Neural Network (Deep RNN), and introduced deep CNN to locate the targets. The target location output results are integrated using the newly introduced neuro-fuzzy system to detect the moving targets effectively. The proposed deep CNN-based neuro-fuzzy system obtained effective moving target detection results by varying the number of targets, iterations, and the pulse repetition level for the metrics, like detection time, missed target rate, and MSE with the minimal values of 1.221s, 0.022, and 1,952.15.

DISSparse: Efficient Mining of Discriminative Itemsets

We tackle the problem of discriminative itemset mining. Given a set of datasets, we want to find the itemsets that are frequent in the target dataset and have much higher frequencies compared with the same itemsets in other datasets. Such itemsets are very useful for dataset discrimination. We demonstrate that this problem has important applications and, at a same time, is very challenging. We present the DISSparse algorithm, a mining method that uses two determinative heuristics based on the sparsity characteristics of the discriminative itemsets as a small subset of the frequent itemsets. We prove that the DISSparse algorithm is sound and complete. We experimentally investigate the performance of the proposed DISSparse on a range of datasets, evaluating its efficiency and stability and demonstrating it is substantially faster than the baseline method.

Psycho-Cognitive Model of Knowledge Creation Theory

Humans perform activities collaboratively or individually, and these activities, more often than not, involve both physical and mental processes. However, irrespective of whether individual or collective functioning, knowledge creation is a personal experience. Nevertheless, the general tenet of this paper is that knowledge is created in a human’s mind and resides in the head. Hence, it posits that knowledge creation is cognitive (associated with the neurological structures of the brain) and psychological (involving consciousness)—a psycho-cognitive process. This study thus employs a “Cognaction” mechanism that is based on the assumptions captured below. The mechanism premised that the human cognitive chamber consists of 3C modes of comprehension (for interpreting stimuli transmitted to the brain by sensory organs), contextualisation (for mindful connecting of chunks to existing schemas), and conceptualisation (for evaluative reflection in a manner that leads to drawing inference and building themes or new concepts). It demonstrates that as diverse skill sets are applied to a task, they generate varieties of effects and outcomes. The outcomes though are distinctive and at the same time are cospecialised. Thus, the psycho-cognitive perspective demonstrates knowledge creation as a cocreation process and sees knowledge as a mix of cocreated, cognitive structures. In view of these, the study provides the missing explanation on how the knowledge archetypes emerged. And it provides the missing link between the belief that “knowledge is created in the head” and knowledge creation theory.

Effect of Business Intelligence on Competitive Advantage Case of Ankara IT Sector

The main purpose of this research is to examine the effect of business intelligence (BI) on competitive advantage in Ankara IT sector. For this purpose, we followed a qualitative research design phenomenology and conducted face-to-face semi-structured interviews with BI staff of 14 organizations defined according to criterion sampling method. We applied inductive qualitative content analysis to interview data. As a result, four main themes emerged: (1) BI adoption (2) competition, (3) organizations, and (4) recommendations. We found that BI has a positive effect on competitive advantage, but the nature of competition in sector is unfair and changeable according to the origin of the product. Leader foreign companies adopt differentiation strategy in gaining competitive advantage and there is a severe competition between the sector leaders. Companies that produce domestic BI solutions adopt low-cost strategy, but they are far away from the high-level competition among foreign companies. Government support and guidance are required for domestic BI solutions to be competitive.

Intellectual Structure of Knowledge Management: A Bibliometric Analysis of the Journal of Information and Knowledge Management

The Journal of Information and Knowledge Management (JIKM) published its first issue in 2002 and celebrated its 19th birthday in 2020. This study aims to assess JIKM performance over its lifetime between 2002 and 2020 by extracting data from the Scopus database and using a combined approach of bibliometric and content analysis. More specifically, we evaluate JIKM’s productivity and stature, discuss its performance compared to other journals, and identify key contributing (authors, institutions, and countries), citation pattern, and conceptual structure. The results highlight JIKM’s growing presence, which is reflected in the dual rise of publication activity and accumulated citation. JIKM becomes one of the preeminent journals in the area of knowledge management, with a broad range of scientific actors’ contributions (authors, institutions and countries) from all over the world. Furthermore, using a bibliographic coupling, keywords’ analysis, and co-authorship analysis, we analyse JIKM’s content and identify the most frequent themes discussed. The analysis reveals that JIKM has expanded its scope from knowledge management to a new array of emerging technologies’ topics such as artificial intelligence and data mining. Graphical visualization of similarities (VOSviewer and Rstudio) shows that the major themes published are clustered into four groups, mainly (i) sustainable knowledge, (ii) emerging technologies, (iii) information management, and (iv) organization culture and knowledge sharing.