Research on Knowledge Graph Creation

Integrated system for managing, querying, and generating responses related to interconnected data using a graph database, specialized loaders, natural language reasoning, and a multi-modal user interface. The system ingests structured, unstructured, and time-series data from diverse sources, transforms it into a unified graph representation, and enables sophisticated reasoning tasks across modalities. It provides a secure, scalable, and interoperable solution for integrating, analyzing, visualizing, and reasoning over heterogeneous data at scale.

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A system to accurately digitize physical documents and efficiently process digital text by correcting entity extraction errors and performing entity resolution using tokenization and distance metrics. It extracts entities from a text document, tokenizes the text based on position, compares extracted entities and tokenized strings for similarity, and generates mappings between entities and strings using correlation probabilities. This allows resolving entities to likely corresponding character strings while correcting for errors during extraction.

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<title>Abstract</title> A dynamic knowledge graph and service model were constructed to address the risk management needs of enterprises. By extracting, integrating, processing entities, a complete is formed, then designed achieve intelligent management. The experiment shows that entity extraction method based on TextRank algorithm proposed by research has an accuracy 82.7%, recall rate 80.9%, F1 score 81.8% in Class datasets. relationship fusion Bidirectional Encoder Representation from Transformers Bi directional Long Short-Term Memory (BERT-Bi-LSTM) transformers about 87.8% for fusion. response time enterprise 1000 transaction requests 11.3 minutes, maximum sustainable throughput 1918TPS, CPU utilization 54.7%, memory usage 3.0GB. above results indicate perform well multiple core indicators, which can effectively improve intelligence level

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In this research, we investigate methods for entity retrieval using graph embeddings. While various have been proposed over the years, most utilize a single embedding and linking approach. This hinders our understanding of how different impact retrieval. To address gap, effects three categories techniques five methods. We perform reranking entities distance between embeddings annotated wish to rerank. conclude that selection both linkers significantly impacts effectiveness For embeddings, incorporate structure textual descriptions are effective. linking, precision recall concerning concepts important optimal performance. Additionally, it is essential encompass as many possible.

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Unstructured scientific text plays a critical role in preserving, transferring, and developing research knowledge. Valuable outputs are often recorded forms such as patents, articles, project reports. Unlike generic text, literature usually follows specialized formats terminology. This significant difference leads to greater challenges opportunities for NLP (Natural Language Processing) researchers. To automate the process of extracting structuring domain-specific knowledge from unstructured this dissertation addresses these by leveraging methods automated materials science extraction. Through three case studies, explores use deep learning, LLM (Large Model) prompt-based techniques extract synthesis texts. Building on efforts, introduces an end-to-end, cost-effective framework designed large-scale extraction with domain experts loop. The demonstrates how combining light human guidance enables scalable, accurate, efficient processing literature. Together, contributions aim mitigate key bottlenecks support development AI-ready data.

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Automated generation of structured data objects from unstructured text using parsing and ranking techniques. The method involves breaking down an input text into substrings and matching them against a list of entities and patterns to classify and rank them. The highest ranked substrings are then used to generate a structured data object like a JSON object. This allows converting unstructured text into structured format without requiring manual mapping or predefined schema.

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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 &lt;strong&gt; Harvesting the Power and Knowledge of Large Language Models &lt;/strong&gt; Author Amanda Kau ( &lt;strong&gt; ORCID &lt;/strong&gt; : &lt;strong&gt; 0009000449499284 &lt;/strong&gt; ) 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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&lt;strong&gt; Harvesting the Power and Knowledge of Large Language&amp;nbsp;Models &lt;/strong&gt; &lt;strong&gt; Author &lt;/strong&gt; Amanda Kau &lt;strong&gt; (ORCID: &lt;/strong&gt; 0009000449499284 &lt;strong&gt; ) &lt;/strong&gt; 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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&lt;strong&gt; Integrating temporal data into static knowledge graphs &lt;/strong&gt; &lt;strong&gt; &lt;strong&gt; Author &lt;/strong&gt; &lt;/strong&gt; Amanda Kau &lt;strong&gt; (ORCID: &lt;/strong&gt; &lt;strong&gt; 0009000449499284 &lt;/strong&gt; &lt;strong&gt; ) &lt;/strong&gt; &lt;strong&gt; Introduction &lt;/strong&gt; Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular.

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