Research on Knowledge Graph Creation

Building ontologies using document analysis and large language models (LLMs) to efficiently and accurately grow ontologies from unstructured data without requiring expert knowledge. The method involves leveraging ML models to identify missing links in an existing ontology, then using LLMs to determine the relationship types for those links. This reduces the reliance on experts and confabulating LLMs by using ML to initially identify potential gaps, then LLMs to fill in the details.

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A data processing pipeline using machine learning to intelligently ingest, aggregate, manage, and transform data from disparate sources for improved accuracy, contextualization, and formatting. The pipeline leverages machine learning models to generate structured data objects from ingested data, filter and transform features based on domain tasks, and render contextualized visualizations. This addresses challenges of time-consuming ingestion, resource-intensive transformation, and inaccurate datasets for downstream tasks. The pipeline includes models for formatting, contextualization, and task-specific requirements.

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Automatically building graphical models and ontologies for manufacturing plants and processes using standardized formats to enable querying and analysis of manufacturing data. The method involves generating virtual representations of plant parts and process steps using a hierarchical template model, converting non-standardized input data to the standardized format, and providing responses to user queries using the standardized format. This enables consistent and interoperable representation and analysis of manufacturing data across different hardware and software platforms.

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Audio-based device fault detection method and apparatus for industrial devices using drones to collect audio data, preprocess it, extract features, and apply graph neural networks to identify faults. The drone monitors devices by collecting initial audio. Preprocessing removes noise and silences. Features like frequency, duration, intensity are extracted. A graph is constructed from similarity of features. Fault detection uses this graph and a neural network. The graph helps identify associations between audio segments. For reactors, faults are confirmed by comparing parameters from abnormal audio. A language model improves the graph by mining entity relationships.

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Efficiently clustering and matching records from multiple sources to identify entities and resolve duplicates, even when records have varying information. The method involves using a scoring model to determine probabilities that pairs of records represent the same entity based on their features. These probabilities are used to build a weighted graph connecting the records. Connected component analysis prunes weak links below a threshold. Then optimal weighted clustering groups the records into final clusters with unique identifiers. This allows efficient entity resolution of large numbers of records with varying data.

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Intelligent named entity recognition that extracts entities from unstructured data, attaches domain-specific context to them, and interprets entity-related information in context. The technique involves labeled entity extraction, reverse question-answering to predict entity keys, and entity alignment to map predicted keys to domain keys. This enables contextualizing entities in chat sessions and resolving entities in unstructured data.

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Aligning and enriching multiple knowledge graphs to create a more comprehensive and accurate knowledge base for AI applications. The alignment is done using a subgraph typing technique that assigns synthetic types to nodes based on their structure and semantics. This allows matching nodes across graphs even if they have different labels or no explicit type labels. The matching is done by identifying valid node-node mappings based on subgraph type combinations that match other valid mappings.

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Automated knowledge graph updating using language models and prompts to efficiently update knowledge graphs based on documents without manual involvement. The system constructs prompts with hints from knowledge graph information to request language models for generating update queries. This leverages the graph context to generate appropriate queries even when document descriptions are incomplete. By using the knowledge graph to guide prompt construction, it avoids language models generating incorrect updates from unsuitable prompts.

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Efficiently extracting entity relationships from long texts using a combination of large language models and smaller relationship agents. The method involves: 1) Running a target long text through a large language model to get a list of important keywords. 2) Running the keyword list through multiple smaller relationship agents to generate regular expressions for different relationships. 3) Using the regular expressions to extract relationships from a set of texts. This leverages the large language model's comprehension for keyword extraction while delegating relationship identification to smaller, specialized agents.

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Generating knowledge graphs for data warehouses to facilitate usage of the data. The method involves extracting entities, relations, and attributes from the data warehouse using techniques like entity recognition, relation discovery, and schema inference. These extracted elements are then organized into a knowledge graph that provides a structured and interconnected view of the data. This graph can be queried and analyzed using graph database technologies to provide insights and recommendations based on the underlying data.

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Generating more accurate and efficient natural language responses to user queries using AI techniques like knowledge graphs, vector searches, and large language models (LLMs). The method involves breaking down user queries into tasks, searching a knowledge graph for relevant entities, then searching a vector database for text chunks associated with those entities. This refined search scope improves the accuracy and efficiency of the vector search. The LLM is then used to process the retrieved text chunks and generate a response.

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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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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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Knowledge graphs have been conceived to collect heterogeneous data and knowledge about large domains, e.g. medical or engineering domains, and to allow versatile access to such collections by means of querying and logical reasoning. A surge of methods has responded to additional requirements in recent years. (i) Knowledge graph embeddings use similarity and analogy of structures to speculatively add to the collected data and knowledge. (ii) Queries with shapes and schema information can be typed to provide certainty about results. We survey both developments and find that the development of techniques happens in disjoint communities that mostly do not understand each other, thus limiting the proper and most versatile use of knowledge graphs.

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The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we... Read More

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The use of knowledge graphs has gained significant traction in a wide variety of applications, ranging from recommender systems and question answering to fact checking. By leveraging the wealth of information contained within knowledge graphs, it is possible to greatly enhance various downstream tasks, making reasoning over knowledge graphs an area of increasing interest. However, despite its popularity, knowledge graph reasoning remains a challenging problem. The first major challenge of knowledge graph reasoning lies in the nature of knowledge graphs themselves. Most knowledge graphs are incomplete, meaning that they may not capture all the relevant knowledge required for reasoning. As a result, reasoning on incomplete knowledge graphs can be difficult. Additionally, real-world knowledge graphs often evolve over time, which presents an additional challenge. The second challenge of knowledge graph reasoning pertains to the input data. In some KG reasoning applications, users may be unfamiliar with the background knowledge graph, leading to the possibility of asking ambiguous questio... Read More

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Knowledge graph (KG) are powerful tools for organizing and managing knowledge, and have been widely applied in many fields. KG describes the objective physical world intuitively using the semantic network, and it explicitly represents and stores related data, enabling intelligent applications like semantic search, intelligent question answering, and decision support. This paper begins with a brief history of the development of KG, as well as an explanation of its concept and technical advantages. Next, it introduces the key technologies of Information extraction, knowledge fusion and knowledge processing. Finally, it summarizes the status of research on knowledge graph and the progress made in their application in vertical areas.

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A knowledge graph is a special kind of graph data, which consists of a triad. Each node in the knowledge graph has several attributes and their attribute values. The storage of the knowledge graph has been the object of academic research, and in this paper, we conduct an in-depth study on the knowledge graph data indexing and compression storage algorithm supported by the RDF graph model, and propose an optimization algorithm for the storage query after the second-level compression. The core of this paper is that after the second-level compression of the k2-tree tree, the sub-matrices are prioritized in terms of the size of data blocks, and when retrieving data, they are retrieved according to the priority, so that the blocks in front are both subject and object at the same time, which can improve the efficiency of data reading, so that the parts with more information will be retrieved first, instead of the traditional sequential retrieval, which tends to retrieve the null values or the data with less information.

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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 human users. However, there is a lack of effective tools for data analysts 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 a usage scenario 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... Read More

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Knowledge graphs are widely used in industry and studied within the academic community. However, the models applied in the development of knowledge graphs vary. Analysing and providing a synthesis of the commonly used approaches to knowledge graph development would provide researchers and practitioners a better understanding of the overall process and methods involved. Hence, this article aims at defining the overall process of knowledge graph development and its key constituent steps. For this purpose, a systematic review and a conceptual analysis of the literature was conducted. The resulting process was compared to case studies to evaluate its applicability. The proposed process suggests a unified approach and provides guidance for both researchers and practitioners when constructing and managing knowledge graphs.

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Automatic knowledge graph construction aims to manufacture structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized structured knowledge beyond informative data. In addition, researchers have also been exploring new ways of handling sophisticated construction tasks in diversified scenarios. Thus, there is a demand for a systematic review of paradigms to organize knowledge structures beyond data-level mentions. To meet this demand, we comprehensively survey more than 300 methods to summarize the latest developments in knowledge graph construction. A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution. The processes of knowledge acquisition are reviewed in detail, including obtaining entities with fine-grained types and their conceptual linkages to knowledge graphs; resolving coreferences; and extracting entity relationships in complex scenarios. The survey co... Read More

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Abstract With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the develop... Read More

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CCKS 2023 proceedings on knowledge representation and knowledge graph reasoning, knowledge acquisition and knowledge base construction.

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Knowledge graph is an important knowledge representation method in the era of big data. It has become one of the key technologies of artificial intelligence and has been applied in different fields. However, there are relatively few studies on university knowledge graphs combined with academic social networks. Therefore, in this paper, we combine the academic social network SCHOLAT to complete the construction of the university knowledge graph. We first construct the ontology of the knowledge graph, then extract and fuse knowledge from data that come from different sources, and add the output knowledge to the knowledge graph. The university knowledge graph has 191,089 entities and 1,638,275 relationship pairs after the construction is completed, and we store it in the Neo4j database to provide knowledge reserve for subsequent applications. In addition to the construction, we also conduct an application analysis to study its application in university knowledge graph-based Q &A system.

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The paper presents selected applications of graph models in knowledge engineering. Starting from the basic definition of a graph as a set of nodes connected by edges, the article presents possible extensions of this concept aimed at increasing the power of expression and the ability to process knowledge. In particular, the work focuses on selected applications of graph models in research areas explored by the members of the KRaKEn research team.

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Since its development, knowledge graph has been widely used in the field of data processing.In this paper, by designing and implementing knowledge graph construction and visualization tools, we aim to solve the problems that the current knowledge graph construction process in general fields is not standardized and the construction cost is high, we adopt the Django framework of python to develop web pages, the secondary graph database stores data, and Echarts visually displays knowledge graph, thus realizing a knowledge graph construction tool with the functions of resource introduction, text annotation, extraction and recognition, and graph overview, and propose a solution for knowledge graph construction with convenient operation and comprehensive functions, so as to show knowledge intuitively in a visual way.In this paper, by comparing with different types of knowledge graph platforms in China, such as Huawei Cloud, KGCloud, and Taoist Knowledge graph, we adopt the Django framework of python to develop web pages, the secondary graph database stores data, and Echarts visually displa... Read More

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