Related papers: OAK: Ontology-Based Knowledge Map Model for Digita…
The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies.…
We present a novel approach to knowledge graph visualization based on ontology design patterns. This approach relies on OPLa (Ontology Pattern Language) annotations and on a catalogue of visual frames, which are associated with foundational…
A challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academic and industry. To address this challenge, semantic analysis of textual data is focused in this paper. We…
The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food…
Developing and organizing new knowledge is a core activity for scholars. Recently, ontologies have been introduced as an approach for organizing knowledge. However, most ontologies do not readily support the development and organization of…
As generative models become powerful, concerns around transparency, accountability, and copyright violations have intensified. Understanding how specific training data contributes to a model's output is critical. We introduce a framework…
With the expansion of scientific research, the number of scientific research is increasing. A new urgent problem is raised that how to keep these researches in a proper way. Therefore, knowledge mapping methods come into being, providing a…
Large Language Models (LLMs) have been extensively adopted in Knowledge Graph Completion (KGC), showcasing significant research advancements. However, as black-box models driven by deep neural architectures, current LLM-based KGC methods…
We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data.…
A key challenge for Industry 4.0 applications is to develop control systems for automated manufacturing services that are capable of addressing both data integration and semantic interoperability issues, as well as monitoring and decision…
Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within…
The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KGs) offer a…
Organizing a large-scale knowledge graph into a typed property graph requires structural decisions -- which entities become nodes, which properties become edges, and what schema governs these choices. Existing approaches embed these…
Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network. Most of the existing knowledge distillation methods direct the student to follow…
The methodological foundations of the construction of information technology, formalized models and tools for the implementation of the research-related design of smart systems based on the use of the concepts of transdisciplinarity and…
Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLMs) for capturing contextual semantics, this work revisits…
This paper proposes a knowledge-based legal document assembly method that uses a machine-readable representation of knowledge of legal professionals. This knowledgebase has two components - the formal knowledge of legal norms represented as…
For a long time threat modeling was treated as a manual, complicated process. However modern agile development methodologies and cloud computing technologies require adding automatic threat modeling approaches. This work considers two…
In recent years, knowledge graph embeddings have achieved great success. Many methods have been proposed and achieved state-of-the-art results in various tasks. However, most of the current methods present one or more of the following…
The data mining process consists of a series of steps ranging from data cleaning, data selection and transformation, to pattern evaluation and visualization. One of the central problems in data mining is to make the mined patterns or…