Research on Knowledge Graph Creation

Knowledge graphs [1][2][3][4] represent structured knowledge using nodes and edges, where nodes signify entities and edges denote relationships between these entities.These graphs have become essential in various fields such as cultural heritage [5], life sciences [6], and encyclopedic knowledge bases, thanks to projects like Yago [7], DBpedia [8], and Wikidata [9].These knowledge graphs have enabled significant advancements in data integration and semantic understanding, leading to more informed scientific hypotheses and enhanced data exploration.Despite their importance, understanding the topology and dynamics of knowledge graphs remains a challenge due to their complex and often chaotic nature.Current models, like the preferential attachment mechanism, are limited to simpler networks and fail to capture the intricate interplay of diverse relationships in knowledge graphs.There is a pressing need for models that can accurately represent the structure and dynamics of knowledge graphs, allowing for better understanding, prediction, and utilisation of the knowledge contained within th... Read More

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Abstract A knowledge graph enables the structured representation of process knowledge. Traditional knowledge graphs typically represent process fact knowledge by depicting relations between entities. However, higher-order knowledge, such as causality, coupling, and rationale among process facts, should be addressed. The Process Hyper-relational knowledge graph (PHKG) was proposed to address these shortcomings. It comprises three layers: a concept layer representing process concept knowledge, an instance layer representing process fact knowledge, and a hyper-relationship layer representing higher-order knowledge linking process facts. Employing a semi-automatic construction method, a hyper-relation knowledge graph was created with 1, 602 entities, 2, 509 entity relationships, and 231 pairs of hyper-relationships. A process knowledge reasoning algorithm has also been developed to enable applications to reason about process knowledge.

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Knowledge graphs are extremely versatile semantic tools, but there are current bottlenecks with expanding them to a massive scale. This concern is a focus of the Graph-Massivizer project, where solutions for scalable massive graph processing are investigated. In this paper we'll describe how to build a massive knowledge graph from existing information or external sources in a repeatable and scalable manner. We go through the process step-by-step, and discuss how the Graph-Massivizer project supports the development of large knowledge graphs and the considerations necessary for replication.

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With the development of artificial intelligence, knowledge graphs are becoming increasingly important in information systems. The article reviews the definitions and representations of knowledge graphs by different authors. The purpose of the study is to form an idea of knowledge graphs for subsequent research. To determine the essence of the concept of the knowledge graph, its history and evolution are considered, as well as a review and comparative analysis of existing definitions over the past 10 years using induction and deduction methods. The main accents on which the researchers relied in their works are also highlighted. As a result, the author's idea of knowledge graphs is derived by the analysis and synthesis methods

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Automated Knowledge Graph Construction with Large Language Models Part 2 <strong> Harvesting the Power and Knowledge of Large Language Models </strong> Author Amanda Kau ( <strong> ORCID </strong> : <strong> 0009000449499284 </strong> ) Introduction Knowledge graphs (KGs) are a structured representation of data in a graphical format, in which entities are represented by nodes and are connected by edges representing relationships

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Knowledge graphs have emerged as a powerful approach for representing and organizing vast amounts of data in a structured and interconnected manner. This research paper explores the construction of knowledge graphs, focusing on some of the techniques and methodologies involved. We have mentioned two approaches available among others for construction of the Knowledge Graph (KG). Here we investigate KG Construction utilizing available tools such as Apache Jena, Stardog, and others, as well as hands-on experience with Neo4j and other libraries such as AmpliGraph and SpaCy, also NetworkX Python. Furthermore, it discusses the challenges and future directions in knowledge graph construction. The insights provided in this paper aim to contribute to the understanding and advancement of knowledge graph construction methodologies and their application in various domains.

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<strong> Harvesting the Power and Knowledge of Large Language Models </strong> <strong> Author </strong> Amanda Kau <strong> (ORCID: </strong> 0009000449499284 <strong> ) </strong> Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

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Recently, Dynamic knowledge graphs (DKGs) have been considered the foundation stone for several powerful knowledge-aware applications.DKG has a great advancement over static knowledge graph with the ability to capture the dynamicity of knowledge.The correctness and completeness of DKGs strongly affect the accuracy of the dependent application, in which many factors may have an impact, including data sources, graph construction model, and evaluation methods.Despite the increasing attention to DKGs, the literature of DKG construction is not comprehensively investigated, and the limitations are not fully revealed.In this paper, a comparative study is conducted for the emerging construction models of DKG.An extensive analysis is provided for each of the three main phases of DKG construction: entity extraction, relationship extraction and graph completion.For the different phases, we investigated the employed techniques, the used data sources, as well as the associated challenges, limitations, and evaluation metrics of each model.The learning approach is introduced as a novel categorizati... Read More

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Research is crucial in today's environment since it helps to ensure our safety and comfort. Years of study, investigation, and experimentation go into every piece of research, and even seasoned experts have challenges when first trying to pinpoint a research gap or identify a prospective strategy. t is highly essential to have knowledge-based system which eases the effort of researchers in identifying the research area and their work. Knowledge graphs are one of the efficient technologies for creating knowledge systems. It is a form of graph data used to store and share information about the physical world. Knowledge graphs are widely acknowledged to be an effective means of representing complex information. n this paper, we are discussing various solutions to the problem like (i) recommending experts in the corresponding domain, (ii) a systematic literature review, and finally (iii) the hotness prediction of topic with the help of knowledge graph and showing how knowledge graph represents the data very effectively in the context of big data.

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Knowledge graphs have been commonly used to represent relationships between entities and utilized in the industry to enhance service qualities. As knowledge graphs integrate data from a variety of sources, they can also be useful references for data analysts. However, there is a lack of effective tools to make the most of the rich information in knowledge graphs. Existing knowledge graph exploration systems are ineffective because they didn?t consider various users? needs and the characteristics of knowledge graphs. Exploratory approaches specifically designed for uncovering and summarizing insights in knowledge graphs have not been well studied yet. In this paper, we propose KGScope that supports interactive visual explorations and provides embedding-based guidance to derive insights from knowledge graphs. We demonstrate KGScope with usage scenarios and assess its efficacy in supporting knowledge graph exploration with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and aiding comprehensive exploration.

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Knowledge graphs represent relationships between entities. These graphs can take dynamic forms to trace changes along time through text models and further used by reasoning systems with the intent to answer queries. In this research we explore their applicability for extracting temporal patterns of knowledge in the form of communities. To this end, we propose a method for generating knowledge relationships over unconnected components of a knowledge graph, allowing for a targeted exploration of emerging contents in corpora. This analysis is applied to the corpora of the Conference on Knowledge Discovery and Data Mining (KDD) publications over the last decade. We find the key knowledge communities over time and rank the underlying concepts. Results show that the publication efforts increasingly focus on graph research and the creation of relationships instead of new concepts. The acquired results confirm the validity of the proposed knowledge discovery methodology for community-centered analysis of emerging changes in dynamic knowledge graphs.

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Knowledge Graphs have demonstrated a real advantage in knowledge representation, leveraging graphs NoSQL structures and schema-less technology, which offers superior comprehension, knowledge representation, interpretation, and reasoning. The problem is that current methods for Knowledge Graph Embedding rely on the graphs topology, and essential information about entities and relations has not been fully employed, failing to utilize the graphs ontology to limit the spurious growth of edges, leading to inaccurate, misleading, and fabricated knowledge. This research aims to establish a method to restrict the spurious growth of host KGs by imposing an upper bound on edge embedding using the claims and the hosts ontology graph. Through this research, a Claim-Ontology Signature artifact is designed to facilitate open-environment KG completion. This artifact establishes the upper bound for the type of edges predicted by the link prediction algorithm, thus preventing the spurious growth of edges within the Knowledge Graph. Further, the artifact is evaluated in the context of three use ca... Read More

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Knowledge graph has emerged as a useful resource and tool for representing real-world entities and their relations, which gained increasing importance in the fields of deep learning and machine learning.This research aims to investigate the academic publications of knowledge graphs between 2013 and 2022 based on the core collection of the Web of Science and examine hot topics and the latest developments in this subject.Thus, the present research adopted a bibliometric analysis to explore the indicators, which mainly focus on different variables from the diachronic productivity of scientific publications to the most prolific countries and the leading publication journals.By means of VOSviewer software, the most productive authors and the frequency of author keywords were further analyzed.The results manifest that dramatic growth has been identified in the past five years due to the output of publications regarding this subject, and the frequently explored themes were mainly conducted from six dimensions, focusing on ontology modelling, knowledge extraction, knowledge graph embedding, ... Read More

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<strong> Integrating temporal data into static knowledge graphs </strong> <strong> <strong> Author </strong> </strong> Amanda Kau <strong> (ORCID: </strong> <strong> 0009000449499284 </strong> <strong> ) </strong> <strong> Introduction </strong> Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular.

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The use of knowledge graphs in the construction of intelligent information and analytical systems provides to effectively structure and analyze knowledge, process large volumes of data, improve the quality of systems, and apply them in various domains such as medicine, manufacturing, trade, and finance. However, domain-specific knowledge graph engineering continues to be a difficult task, requiring the creation of specialized methods and software. One of the main trends in this area is the use of various information sources, in particular tables, which can significantly improve the efficiency of this process. This paper proposes an approach and a tool for automated extraction of specific entities (facts) from tabular data and populating them with a target knowledge graph based on the semantic interpretation (annotation) of tables. The proposed approach is implemented in the form of a special processor included in the Talisman framework. We also present an experimental evaluation of our approach and a demo of domain knowledge graph development for the Talisman framework.

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Enterprise knowledge graphs combine business data and organizational knowledge by means of a semantic network of concepts, properties, individuals and relationships. The graph-based integration of previously unconnected information with domain knowledge provides new insights and enables intelligent business applications. However, knowledge graph construction is a large investment which requires a joint effort of domain and technical experts. This paper presents a practical step-by-step procedure model for building an RDF knowledge graph that interconnects heterogeneous data and expert knowledge for an industry use case. The self-contained process adapts the "Cross Industry Standard Process for Data Mining" and uses competency questions throughout the entire development cycle. The procedure model starts with business and data understanding, describes tasks for ontology modeling and the graph setup, and ends with process steps for evaluation and deployment.

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Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with time, and disregard their dynamic evolution over time. As a result, temporal knowledge graphs have attracted more attention because a large amount of structured knowledge exists only within a specific period. Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. The representation learning of temporal knowledge graphs incorporates time information into the standard knowledge graph framework and can model the dynamics of entities and relations over time. In this paper, we conduct a comprehensive survey of temporal knowledge graph representation learning and its applications. We begin with an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. Next, we propose a taxonomy based on the core technologies of temporal knowledg... Read More

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This exploratory study sought to deepen the understanding of the benefits of using the knowledge graph technological solution for knowledge management. Through a bibliometric analysis of 45 academic articles selected from the Scopus database, with no time range determined, it was observed that the term has been addressed in academic productions on various topics with a greater number of publications from Asian researchers (71%). To understand the relationships of the keywords used by the authors of these articles, the VOSviewer tool was used to create the graph, making it possible to visualize the terms' clusters. It was identified that the theme is emerging, as the first article published in the Scopus database took place in 2011 and an expressive growth of publications can be observed in 2020. The production of academic knowledge with the use of the coupled knowledge graph stands out to the design of algorithms that support tutoring and curation activities with a focus on knowledge management in the areas of Health - including during the Covid-19 pandemic - and Education. The trans... Read More

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A Knowledge Graph is a directive graph where the nodes state the entities and the edges describe the relationships between the entities of data. It is also referred to as a Semantic Network. The massive amount of data generated every day can be transformed into knowledge via knowledge graphs for the effective use of these data. Knowing the classification of Knowledge Graphs is required to adapt to different requirements of Knowledge Graphs. Knowledge Graphs are primarily classified concerning their building techniques and their usages. In building techniques, it is considered how the Knowledge Graph is built. For example, the graph can be constructed as a triplet, quadruplet, etc., or created from structured data, e.g., database, or unstructured data, e.g., text, image, etc. On the other hand, Knowledge Graphs can be used for various purposes. For example, Knowledge Graphs can be used for Information Retrieval, Semantic Query, etc., or different types of data visualization. Nowadays, Knowledge Graph is one of the trending topics in the modern technology-dependent world. However, clea... Read More

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Knowledge graphs, which indicate knowledge as a semantic graph, have stirred up notable worries in the professional as well as academic communities. Many researchers believe that their ability to provide semantically structured information holds great promise for the development of more intelligent machines. This trait has important potential solutions for many tasks including information retrieval recommendations, data visualization, and question-answering. The use of knowledge graphs to depict the structural relationships between items is becoming increasingly common. In the last few years, there has been a remarkably rapid expansion in the application of knowledge representation, visualization, and reasoning in diverse fields like natural language processing and computer vision. This has resulted in the production of numerous open-source and enterprise-supported knowledge graphs. The tools used for generating knowledge graphs also create hype in the market as these tools can generate and analyze knowledge graphs within minutes. This paper compares knowledge graph generation tools ... Read More

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