Reviewerly/exHarmony huggingface dataset:
LDkit: Linked Data Object Graph Mapping Toolkit for Web Applications

The adoption of Semantic Web and Linked Data technologies in web application development has been hindered by the complexity of numerous standards, such as RDF and SPARQL, as well as the challenges associated with querying data from distributed sources and a variety of interfaces. Understandably, web developers often prefer traditional solutions based on relational or document databases due to the higher level of data predictability and superior developer experience. To address these issues, we present LDkit, a novel Object Graph Mapping (OGM) framework for TypeScript designed to provide a model-based abstraction for RDF. LDkit facilitates the direct utilization of Linked Data in web applications, effectively working as the data access layer. It accomplishes this by querying and retrieving data, and transforming it into TypeScript primitives according to user defined data schemas, while ensuring end-to-end data type safety. This paper describes the design and implementation of LDkit, highlighting its ability to simplify the integration of Semantic Web technologies into web applications, while adhering to both general web standards and Linked Data specific standards. Furthermore, we discuss how LDkit framework has been designed to integrate seamlessly with popular web application technologies that developers are already familiar with. This approach promotes ease of adoption, allowing developers to harness the power of Linked Data without disrupting their current workflows. Through the provision of an efficient and intuitive toolkit, LDkit aims to enhance the web ecosystem by promoting the widespread adoption of Linked Data and Semantic Web technologies.
Introduction and Contextual Background of Cybercrime as an Emerging Phenomenon in Africa

In many ways, information, and communication technologies (ICTs) have shrunk the world, but they have also exposed the world to influences that have never been as diverse and difficult (Seemma et al., International Journal of Advanced Research in Computer and Communication Engineering 7:125–130, 2018). Modern communication equipment, internetInternet access, and robust computer systems for dataData processing were all made possible by the development of digital technologyTechnology (Hunda et al., Law Journal, Gurn Nanak Dev University, Amritsar 13:1–10, 2014). The vulnerability of these systems and the potential for abuse or criminalCriminal activities have increased due to the rapid proliferation of large-scale computer networksComputer network with the ability to access multiple systems over conventional telecommunication lines (Oghenevwogaga, Research Journal of Mass Communication and Information Technology 3:10–24, 2017).
SPARQL_edit: Editing RDF Literals in Knowledge Graphs via View-Update Translations

In order to improve the adoption of knowledge graphs (KG) in everyday work, non-technical users must be empowered to not only view but to write data in a KG. Whereas most available software tools focus on displaying information, the presented solution helps business users to execute write operations for correcting wrong values or inserting missing values. SPARQL_edit is a Web application that enables users without any knowledge of SPARQL or RDF to view query results and instantly edit RDF literal values of the knowledge graph. The main concept can be summarized as ‘editable SPARQL result table’. If a user modifies the value of an RDF literal in the view, a generic view-update algorithm translates the change into a SPARQL/Update query that updates the correct RDF triple in the KG. Similar to the view update problem in databases, there are restrictions of the SPARQL language features that can be used for creating a view with unambiguous updates to the graph.
ASKRL: An Aligned-Spatial Knowledge Representation Learning Framework for Open-World Knowledge Graph

Knowledge representation learning (KRL) aims to project entities and relations in knowledge graphs (KGs) to densely distributed embedding space. As the knowledge base expands, we are often presented with zero-shot entities, often with textual descriptions. Although many closed-world KRL methods have been proposed, most of them focus on connections between entities in the existing KGs. Therefore, they cannot handle zero-shot entities well, resulting in the inability of bringing zero-shot entities to existing KGs. To address this issue, this paper proposes ASKRL, a straightforward yet efficient open-world knowledge representation learning framework. ASKRL learns representations of entities and relations in both structured and semantic spaces, and subsequently aligns the semantic space with the structured space. To begin with, ASKRL employs the off-the-shelf KRL models to derive entity and relation embeddings in the structured embedding space. Afterward, a Transformer-based encoder is applied to obtain contextualized representations of existing entities and relations in semantic space. To introduce structure knowledge of KG into the contextualized representations, ASKRL aligns semantic embedding space to structured embedding space from the perspective of common properties (i.e., angle and length). Additionally, it aligns the output distribution of the score function between the two spaces. To further learn representations of zero-shot entities effectively, a sophisticated three-stage optimization strategy is devised in the training phase. In the inference phase, representations of zero-shot entities can be directly derived from the Transformer-based encoder. ASKRL is plug-and-play, enabling off-the-shelf closed-world KRL models to handle the open-world KGs. Extensive experiments demonstrate that ASKRL significantly outperforms strong baselines in open-world datasets, and the results illuminate that ASKRL is simple and efficient in modeling zero-shot entities.
Internet and Drug Trafficking in Africa

Over the past ten years, the Internet's explosive growth has brought about unheard-of changes in criminal activity, creating new possibilities and infrastructure for crimes like trafficking. Africa has long been home to some of the world's most tenacious and resilient criminal organisations, with West African criminal networks now present in the majority of the world's main cities. The African peninsula has developed over the past ten years into a major route for drug shipments, a source of illegally obtained natural resources, a starting point for human trafficking, and a route for migrant smuggling. Drugs are meticulously disguised to hide their origins and avoid examination, and Africa has grown to be a significant transhipment route for drug trafficking. However, the majority of African nations lack the cutting-edge technology needed to identify dangerous products and substances like narcotics and weapons. In some instances, drug lords have colluded with senior government figures to thwart efforts to stop the practice after border control agents have confiscated contraband. Currently, crimes are performed without the perpetrators crossing international borders, and a significant quantity of illegal goods are sold online. Due to the significant part that the Internet plays in drug trafficking, over the past ten years, online recreational drug markets have grown significantly. Trafficking in illegal drugs harms people, communities, and even the receiving nations. These negative consequences include everything from health issues to crime, dropping out of school, and aggression, among other things. Among the notable offences connected to drug trafficking are homicide, abduction, armed robbery, and theft. This chapter explains how cyberspace has impacted drug trafficking on the African peninsula and the difficulties African nations face in policing the flow of illegal goods across their borders. The chapter suggests using cutting-edge technologies and enhancing border management law enforcement as a method to prevent the entry of drugs and mitigate the effects of the practice on states and their populations.
FeaBI: A Feature Selection-Based Framework for Interpreting KG Embeddings

Knowledge Graph (KG) embedding methods represent KG entities as vectors in an embedding space, and they have been successfully used for a variety of tasks, including link prediction and entity classification. While some of the recent embedding methods outperform traditional approaches on these tasks, their main drawback is the lack of interpretability. Several methods for explaining predictions made by KG embeddings have been proposed in the literature. However, none of them targeted the problem of constructing model explanations for embeddings, i.e., interpretable KG representations that behave similarly to embeddings on certain tasks. We address this problem and propose a novel method for generating interpretable vectors for entity embeddings. To achieve this, we employ embedded feature selection techniques to extract from the KG, on which the embedding model was trained, propositional features that are important for a given KG embedding model. Our approach sheds light on the information in the KG captured by embeddings and provides valuable insights that can be used to further enhance the embedding models. Additionally, we demonstrate the usefulness of our method for explaining embedding-based entity similarity.
AIDA-Bot 2.0: Enhancing Conversational Agents with Knowledge Graphs for Analysing the Research Landscape

The crucial task of analysing the complex dynamics of the research landscape and uncovering the latest insights from the scientific literature is of paramount importance to researchers, governments, and commercial organizations. Springer Nature, one of the leading academic publishers worldwide, plays a significant role in this domain and regularly integrates and processes a variety of data sources to inform strategic decisions. Since exploring the resulting data is a challenging task, in 2021 we developed AIDA-Bot, a chatbot that addresses inquiries about the research landscape by utilising a large-scale knowledge graph of scholarly data. This paper presents the novel AIDA-Bot 2.0, which can both 1) support a set of predetermined question types by automatically translating them to formal queries on the knowledge graph, and 2) answer open questions by summarising information from relevant articles. We evaluated the performance of AIDA-Bot 2.0 through a comparative assessment against alternative architectures and an extensive user study. The results indicate that the novel features provide more accurate information and an excellent user experience.
Visualizing Mappings Between Pairwise Ontologies - An Empirical Study of Matrix and Linked Indented List in Their User Support During Class Mapping Creation and Evaluation

Visual support designed to facilitate human interaction with semantic data has largely focused on visualizing entities such as classes and relationships within an ontology. Comparatively speaking, less attention has focused on visualizing mappings established between independent ontologies and determining the effectiveness of mapping visualizations. This paper presents a user study of the matrix and the linked indented list visualization in their visual support for users during creation and evaluation of class mappings between pairwise ontologies. A total of 81 participants took part in a task-based controlled experiment, with the aim of assessing the extent to which a given visualization supports recognition of visual cues, validation of existing mappings, and creation of new results. Based on empirical evidence collected from the participants in their speed to complete the tasks, their success in answering various questions, as well as their physiological sensory data such as eye gaze, we aim to quantify user performance and visual attention demanded in the use of the two aforementioned mapping visualizations. The experimental results indicate that the linked indented lists and the matrix visualization are comparable in terms of effectiveness and efficiency when assisting users in the given task scenarios with marginal differences. However, linked indented lists are likely to demand less effort from the users’ visual perceptual systems with statistically significant differences found in several gaze measures, including the physical distances needed to locate relevant visual information, number of fixations and time required to process visual cues, and the overall efforts in scanning the visual scene.
Knowledge Graph Enhanced Language Models for Sentiment Analysis

Pre-trained language models (LMs) have been widely used in sentiment analysis, and some recent works have focused on injecting sentiment knowledge from sentiment lexicons or structured commonsense knowledge from knowledge graphs (KGs) into pre-trained LMs, which have achieved remarkable success. However, these works often only obtain knowledge from a single source in either the sentiment lexicon or the KG, and only perform very shallow fusion of LM representations and external knowledge representations. Therefore, how to effectively extract multiple sources of external knowledge and fully integrate them with the LM representations is still an unresolved issue. In this paper, we propose a novel knowledge enhanced model for sentiment analysis (KSA), which simultaneously incorporates commonsense and sentiment knowledge as external knowledge, by constructing a heterogeneous Commonsense-Senti Knowledge Graph. Additionally, a separate global token and global node are added to the text sequence and constructed knowledge graph respectively, and a fusion unit is used to enable global information interaction between the different modalities, allowing them to perceive each other’s information and thereby improving the ability to perform sentiment analysis. Experiments on standard datasets show that our proposed KSA significantly outperforms the strong pre-trained baselines, and achieves new state-of-the-art results on most of the test datasets.
Integrating Knowledge Graph Embeddings and Pre-trained Language Models in Hypercomplex Spaces

Knowledge graphs comprise structural and textual information to represent knowledge. To predict new structural knowledge, current approaches learn representations using both types of information through knowledge graph embeddings and language models. These approaches commit to a single pre-trained language model. We hypothesize that heterogeneous language models may provide complementary information not exploited by current approaches. To investigate this hypothesis, we propose a unified framework that integrates multiple representations of structural knowledge and textual information. Our approach leverages hypercomplex algebra to model the interactions between (i) graph structural information and (ii) multiple text representations. Specifically, we utilize Dihedron models with 4*D dimensional hypercomplex numbers to integrate four different representations: structural knowledge graph embeddings, word-level representations (e.g., Word2vec and FastText), sentence-level representations (using a sentence transformer), and document-level representations (using FastText or Doc2vec). Our unified framework score the plausibility of labeled edges via Dihedron products, thus modeling pairwise interactions between the four representations. Extensive experimental evaluations on standard benchmark datasets confirm our hypothesis showing the superiority of our two new frameworks for link prediction tasks.
Working with JavaScript

If you’re completely new to JavaScript, or to programming altogether, there are some basic concepts that you’ll need to know. Many of these concepts are common to other programming languages, but every language has its own twists and quirks.
CapsKG: Enabling Continual Knowledge Integration in Language Models for Automatic Knowledge Graph Completion

Automated completion of knowledge graphs is a popular topic in the Semantic Web community that aims to automatically and continuously integrate new appearing knowledge into knowledge graphs using artificial intelligence. Recently, approaches that leverage implicit knowledge from language models for this task have shown promising results. However, by fine-tuning language models directly to the domain of knowledge graphs, models forget their original language representation and associated knowledge. An existing solution to address this issue is a trainable adapter, which is integrated into a frozen language model to extract the relevant knowledge without altering the model itself. However, this constrains the generalizability to the specific extraction task and by design requires new and independent adapters to be trained for new knowledge extraction tasks. This effectively prevents the model from benefiting from existing knowledge incorporated in previously trained adapters. In this paper, we propose to combine the benefits of adapters for knowledge graph completion with the idea of integrating capsules, introduced in the field of continual learning. This allows the continuous integration of knowledge into a joint model by sharing and reusing previously trained capsules. We find that our approach outperforms solutions using traditional adapters, while requiring notably fewer parameters for continuous knowledge integration. Moreover, we show that this architecture benefits significantly from knowledge sharing in low-resource situations, outperforming adapter-based models on the task of link prediction.
Manipulating HTML Elements

All web pages are written initially in HTML, but this code is only text to begin with. The browser then takes this HTML code, together with any associated CSS, and uses it to generate the visible content of the page.
Improving Contract Negotiations for Library Collections through Open Records Requests

Open records requests can be an important tool in obtaining valuable information to use in negotiations with content providers. This paper examines the opportunities libraries have in requesting public information through open records requests to better support their purchasing decisions. The case is made for investing time up front to better understand contract terms and pricing already secured by public entities, using that knowledge to improve their position in the negotiating process.
A Prediction of South African Public Twitter Opinion using a Hybrid Sentiment Analysis Approach

Sentiment analysis, a subfield of Natural Language Processing, has garnered a great deal of attention within the research community. To date, numerous sentiment analysis approaches have been adopted and developed by researchers to suit a variety of application scenarios. This consistent adaptation has allowed for the optimal extraction of the authors emotional intent within text. A contributing factor to the growth in application scenarios is the mass adoption of social media platforms and the bondless topics of discussion they hold. For government, organizations and other miscellaneous parties, these opinions hold vital insight into public mindset, welfare, and intent. Successful utilization of these insights could lead to better methods of addressing said public, and in turn, could improve the overall state of public well-being. In this study, a framework using a hybrid sentiment analysis approach was developed. Various amalgamations were created – consisting of a simplified version of the Valence Aware Dictionary and sEntiment Reasoner (VADER) lexicon and multiple instances of classical machine learning algorithms. In this study, a total of 67,585 public opinion-oriented Tweets created in 2020 applicable to the South African (ZA) domain were analyzed. The developed hybrid sentiment analysis approaches were compared against one another using well known performance metrics. The results concluded that the hybrid approach of the simplified VADER lexicon and the Medium Gaussian Support Vector Machine (MGSVM) algorithm outperformed the other seven hybrid algorithms. The Twitter dataset utilized serves to demonstrate model capability, specifically within the ZA context.
A Novel Digital Recognition Method Based on Improved SVD-DHNN

Discrete Hopfield Neural Network (DHNN) is widely used in character recognition because of its associative memory. It is a fully connected neural network. Its weight initialization is a random process. In order to give full play to the associative memory of DHNN and overcome the problems of pseudo-stable points and complex structure caused by random initialization, an improved SVD-DHNN model is proposed. Firstly, the weight of DHNN is optimized by the global search capability of PSO to help the model jump out of the pseudo stable point; secondly, the weight matrix of DHNN is readjusted by singular value decomposition (SVD). The contribution rate is used to trim the weights of DHNN, which can reduce the complexity of the network structure; finally, the validity and applicability of the new model are verified by means of digital recognition.
An Automatic Nuclei Segmentation on Microscopic Images using Deep Residual U-Net

Nuclei Segmentation is the preliminary step towards the task of medical image analysis. Nowadays, there exists several deep learning-based techniques based on Convolutional Neural Networks (CNNs) for the task of nuclei segmentation. In this study, we present a neural network for semantic segmentation. This network harnesses the strengths in both residual learning and U-Net methodologies, thereby amplifying cell segmentation performance. This hybrid approach also facilitates the creation of network with diminished parameter requirement. The network incorporates residual units contributes to a smoother training process and mitigate the issue of vanishing gradients. Our model is tested on a microscopy image dataset which is publicly available from the 2018 Data Science Bowl grand challenge and assessed against U-Net and several other state-of-the-art deep learning approaches designed for nuclei segmentation. Our proposed approach showcases a notable improvement in average Intersection over Union (IoU) gain compared to prevailing state-of-the-art techniques, by exhibiting a significant margin of 1.1% and 5.8% higher gains over the original U-Net. Our model also excels across various key indicators, including accuracy, precision, recall and dice-coefficient. The outcomes underscore the potential of our proposed approach as a promising nuclei segmentation method for microscopy image analysis.