Search results

Despite ongoing research into archival metadata standards, digital archives are unable to effectively represent records in their appropriate contexts. This study aims to propose a knowledge graph that depicts the diverse relationships between heterogeneous digital archive entities.

This study introduces and describes a method for applying knowledge graphs to digital archives in a step-by-step manner. It examines archival metadata standards, such as Records in Context Ontology (RiC-O), for characterising digital records; explains the process of data refinement, enrichment and reconciliation with examples; and demonstrates the use of knowledge graphs constructed using semantic queries.

This study introduced the 97imf.kr archive as a knowledge graph, enabling meaningful exploration of relationships within the archive’s records. This approach facilitated comprehensive record descriptions about different record entities. Applying archival ontologies with general-purpose vocabularies to digital records was advised to enhance metadata coherence and semantic search.

Most digital archives serviced in Korea are limited in the proper use of archival metadata standards. The contribution of this study is to propose a practical application of knowledge graph technology for linking and exploring digital records. This study details the process of collecting raw data on archives, data preprocessing and data enrichment, and demonstrates how to build a knowledge graph connected to external data. In particular, the knowledge graph of RiC-O vocabulary, Wikidata and Schema.org vocabulary and the semantic query using it can be applied to supplement keyword search in conventional digital archives.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

Ethiopia has enacted laws on transparency and disclosure of information in state-owned enterprises (SOEs). However, these laws are not strict enough, with the transparency and disclosure practices disappointing in the country. Thus, this study aims to investigate the legal framework governing transparency and disclosure in SOEs.

This study uses doctrinal, qualitative and comparative approaches. Domestic legal texts are appraised based on the organization for economic co-operation and development Guideline on Corporate Governance of State-owned Enterprises, the World Bank Toolkit on Corporate Governance of State-owned Enterprises and best national practices. This approach has been further corroborated by qualitative analysis of the basic principles of transparency and disclosure.

The finding reveals that the laws on transparency and disclosure do not comply with global practices and are inadequate to ensure transparency and discourse in SOEs. They fail to establish appropriate disclosure frameworks and practices at the SOE and state-ownership entity levels. They also indiscriminately subject enterprises to multiple auditing functions and conflicting responsibilities.

To the author’s knowledge, this study is the first legal literature on transparency and disclosure in Ethiopian SOEs. This study assists the state as owner in reforming the laws and uplifting SOEs from their current unpleasant condition. It can also become a reference for future research.

Institutional, economic, social and technological advancements enable openness to cope with wicked public management issues. Although open innovation (OI) is becoming a new normality for public sector entities, scholarly knowledge on this topic is not fully systematized. The article fills this gap, providing a thick and integrative account of OI to inspire public management decisions.

Following the SPAR-4-SLR protocol, a domain-based literature review has been accomplished. Consistently with the study purpose, a hybrid methodology has been designed. Bibliographic coupling permitted us to discover the research streams populating the scientific debate. The core arguments addressed within and across the streams were reported through an interpretive approach.

Starting from an intellectual core of 94 contributions, 5 research streams were spotted. OI in the public sector unfolds through an evolutionary path. Public sector entities conventionally acted as “senior partners” of privately-owned companies, providing funding (yellow cluster) and data (purple cluster) to nurture OI. An advanced perspective envisages OI as a public management model purposefully enacted by public sector entities to co-create value with relevant stakeholders (red cluster). Fitting architectures (green cluster) and mechanisms (blue cluster) should be arranged to release the potential of OI in the public sector.

The role of public sector entities in enacting OI should be revised embracing a value co-creation perspective. Tailored organizational interventions and management decisions are required to make OI a reliable and dependable public value generation model.

The article originally systematizes the scholarly knowledge about OI, presenting it as a new normality for public value generation.

Literature about transparency in public-sector organizations has been attracting the attention of scholars for the last two decades. This study reviews the existing literature with the intention of creating a description of the state of the art, categorized by geographical areas, levels of government, topics, and methodologies.

The authors have developed a structured literature review following a rigorous protocol. The initial search was launched on 25 April 2022 on Scopus and Web of Science, resulting in 3,217 articles. After removing duplicates and studies that did not meet all the inclusion criteria specified in the review protocol, the final sample includes 956 articles from 1991 to 2021.

The analyses show a considerable increase in studies since 2005, especially in the last two years, when 30% of the publications have been produced. Most of the studies analyze the national/central level of government. Many authors compare different countries, while other scholars focus on specific countries, overall, the USA and the UK. The local level of government has also been widely studied, especially in the Spanish and Chinese contexts. The most frequently used methodologies are quantitative and empirical techniques, and the most common topics are those associated with accountability.

This study uses a huge sample (956 articles over the period 1991–2021), which has never been used before, to examine the literature on transparency. The structured literature review facilitates the identification of gaps that can be filled by future studies. These include analyzing transparency in specific geographical areas like Africa, Asia, and Latin America, studying transparency at different levels of government, especially at the regional and federal levels, and providing comparative studies and case study collections.

The purpose of this paper is to examine whether the voluntary implementation of an internal audit department (IAD) by municipalities has any influence on external auditors' opinions.

This study population comprises the 308 Portuguese municipalities, from which the authors extracted a sample of 179. Financial and audit reports were collected from the period under analysis (2014–2017). The sample was then divided into two groups: municipalities that had voluntarily implemented an IAD and those that had not. Internal audit departments were characterized according to their robustness – whether they were more or less robust. First, a descriptive statistical analysis of the dataset was performed to analyze the representativeness of the sample and to extract insights. To address the research questions, ordinal random effect regression models were considered.

Contrary to the authors' expectations, the voluntary implementation of an IAD had no influence on the audit report type. However, when the authors refined the approach to include the robustness of the IAD, it became clear that this variable does influence the report issued by the external auditor.

This paper contributes to the current literature by determining the effects of the robustness of IADs on municipality audit reports. As far as the authors know, this paper is novel. Since auditing plays an important role in the transparency of public financial statements and in promoting equity, this study shows that a robust IAD is an advantage in the pursuit of these goals.

The purpose of this paper is to try to reach the main factors that could put national security at risk as a result of government cloud computing programs.

The paper adopts the analytical approach to first lay foundations of the relation between national security, cybersecurity and cloud computing, then it moves to analyze the main vulnerabilities that could affect national security in cases of government cloud computing usage.

The paper reached several findings such as the relation between cybersecurity and national security as well as a group of factors that may affect national security when governments shift to cloud computing mainly pertaining to storing data over the internet, the involvement of a third party, the lack of clear regulatory frameworks inside and between countries.

Governments are continuously working on developing their digital capacities to meet citizens’ demands. One of the most trending technologies adopted by governments is “cloud computing”, because of the tremendous advantages that the technology provides; such as huge cost-cutting, huge storage and computing capabilities. However, shifting to cloud computing raises a lot of security concerns.

The value of the paper resides in the novelty of the topic, which is a new contribution to the theoretical literature on relations between new technologies and national security. It is empirically important as well to help governments stay safe while enjoying the advantages of cloud computing.

In this article, we discuss an innovative audience research methodology developed for the AHRC-funded “Beyond the Multiplex: Audiences for Specialised Film in English Regions” project (BtM). The project combines a computational ontology with a mixed-methods approach drawn from both the social sciences and the humanities, enabling research to be conducted both at scale and in depth, producing complex relational analyses of audiences. BtM aims to understand how we might enable a wide range of audiences to participate in a more diverse film culture, and embrace the wealth of films beyond the mainstream in order to optimise the cultural value of engaging with less familiar films. BtM collects data through a three-wave survey of film audience members’ practices, semi-structured interviews and film-elicitation groups with audience members alongside interviews with policy and industry experts, and analyses of key policy and industry documents. Bringing each of these datasets together within our ontology enables us to map relationships between them across a variety of different concerns. For instance, how cultural engagement in general relates to engagement with specialised films; how different audiences access and/or share films across different platforms and venues; how their engagement with those films enables them to make meaning and generate value; and how all of this is shaped by national and regional policy, film industry practices, and the decisions of cultural intermediaries across the fields of film production, distribution and exhibition. Alongside our analyses, the ontology enables us to produce data visualisations and a suite of analytical tools for audience development studies that stakeholders can use, ensuring the research has impact beyond the academy. This paper sets out our methodology for developing the BtM ontology, so that others may adapt it and develop their own ontologies from mixed-methods empirical data in their studies of other knowledge domains.

The purpose of this paper is to review ontologies and data models currently in use for augmented reality (AR) applications, in the cultural heritage (CH) domain, specifically in an urban environment. The aim is to see the current trends in ontologies and data models used and investigate their applications in real world scenarios. Some special cases of applications or ontologies are also discussed, as being interesting enough to merit special consideration.

A search using Google Scholar, Scopus, ScienceDirect and IEEE Xplore was done in order to find articles that describe ontologies and data models in AR CH applications. The authors identified the articles that analyze the use of ontologies and/or data models, as well as articles that were deemed to be of special interest.

This review found that CIDOC-CRM is the most popular ontology closely followed by Historical Context Ontology (HiCO). Also, a combination of current ontologies seems to be the most complete way to fully describe a CH object or site. A layered ontology model is suggested, which can be expanded according to the specific project.

This study provides an overview of ontologies and data models for AR CH applications in urban environments. There are several ontologies currently in use in the CH domain, with none having been universally adopted, while new ontologies or extensions to existing ones are being created, in the attempt to fully describe a CH object or site. Also, this study suggests a combination of popular ontologies in a multi-layer model.