Detecting logical relationships in mechanical, electrical, and plumbing (MEP) systems with BIM using graph matching

With the continuous development of new media technology, the spiritual needs of the masses have been greatly satisfied and the aesthetic ability has also been significantly improved compared with the past. From the current point of view, “literary works,” as the spiritual food of contemporary people, are promoting social spirit. The use of natural language processing and knowledge graph technology can improve cultural cognition to promote the dissemination and development of classic English literature, which has become a necessary means of dissemination of classic English literature. Most of the existing classic English literary works are appreciated based on modern literature datasets. Nowadays, with the continuous development of new media technology, there are fewer studies on the dissemination and cultural cognition of classic English literary works. This makes it impossible for readers to obtain cultural cognition from classic English literary works, making it difficult for the dissemination and development of classic English literary works. In view of the above problems, using natural language processing and knowledge graph technology, taking Shakespeare's play “Hamlet” represented by classic English literary works as an example, the research on the construction method of knowledge graph is carried out and the cultural characteristics in literary works are extracted and analyzed. In parsing, a bidirectional gated recurrent unit network model based on hybrid character embedding is proposed. Based on n-gram embedding, by combining pretraining embedding and radical embedding, it can fully consider the rich semantic information in English literature works to extract. Feature: in terms of named entity recognition, based on the existing iterative atrous convolutional network model, an iterative atrous convolutional network model is proposed. To get the best sequence label and get the last labeled entity information, in terms of knowledge graph construction and visual query, a workflow method for building knowledge graph from unstructured text is proposed and a flask-based knowledge graph visual query system is designed, which applies the best model of the above two tasks. We decode the complete “Hamlet” text, extract entities and their semantic links as nodes and relationships in the knowledge graph, store knowledge through the graph database, and finally form a visual query system that combines the front and back end.

Against the limitations of traditional C language programming teaching—such as fragmented learning (failing to connect old and new knowledge), restricted teaching depth/breadth (insufficient in-depth analysis of knowledge points due to class time limits), and students’ over-reliance on textbook examples—and MOOCs’ inability to meet personalized needs (lacking tutor guidance), this study aims to improve teaching quality and students’ abilities (independent learning, problem-solving, innovation) by integrating knowledge graph and AI. First, a C language course knowledge graph was constructed via six steps: knowledge modeling (sorting core elements like data types and pointers), data preparation (collecting materials including 295 videos and 842 PPTs), information extraction (identifying relationships like inclusion and sequence), knowledge integration (hierarchical structure), storage (in graph database), and graphical display. Then, the knowledge graph was applied to teaching: enabling AI-assisted intelligent teaching (pre-class task design, in-class knowledge visualization, hierarchical after-class exercises), building a comprehensive evaluation system (learning trajectory analysis, knowledge mastery evaluation, resource recommendation), and planning personalized learning paths. Two semesters of practice showed the knowledge graph significantly boosted students’ learning interest and key abilities. Futurely, with tech innovation and expanded scenarios, knowledge graph will have broader prospects in education to advance programming course teaching reform.

Numerous large knowledge graphs, such as DBpedia, Wikidata, Yago and Freebase, have been developed in the last decade, which contain millions of facts about various entities in the world. These knowledge graphs have proven to be incredibly useful for intelligent Web search, question understanding, in-context advertising, social media mining, and biomedicine. As some researchers have pointed out, a knowledge graph is not just a graph database, but it should have a layer of conceptual knowledge, which is usually represented as a set of first-order rules. However, it is challenging to automatically extract first-order rules from large knowledge graphs. In particular, traditional models are usually unable to handle rule learning in large knowledge graphs. This chapter aims to present state-of-the-art techniques and models on learning first-order rules using representation learning. By first recalling some basics of rule learning in knowledge graphs, we will introduce useful techniques of embedding-based rule learning through major models such as RLvLR and TyRuLe, which embed paths in knowledge graphs into latent spaces for candidate rule search. Then we evaluate the rule learning efficiency and the quality of automatically learned rules by applying them in link prediction. Before the chapter is concluded, we will also discuss some future research problems in the area.

Knowledge graph (KG) is a database that stores the information and relationship between entities in the form of graph. This paper presents a knowledge graph construction method for grid substation knowledge graph based on multi-source data. The device connection, alarm information and ledgers are contained in KG, which benefits the substation operation and maintenance. First, entities and relationships are selected according to the actual composition of substation. Then, triples are extracted through domain dictionaries and conditional random field. Entity disambiguation is carried out before establishing the knowledge graph based on corresponding data sources. Finally, knowledge fusion is used to remove the redundancy in the database to complete the construction of the KG. Compared to traditional relational database, graph database can more intuitively show the connection relationship between devices. By adding more links to knowledge graph, it takes less time for monitoring personnel to locate accidents when dealing with alarm information.

To analyze the rules of moxibustion for low back pain by ZHOU Meisheng by using data mining and knowledge graph technology. Taking the medical cases of moxibustion for low back pain from ZHOU Meisheng's legacy manuscripts and existing works as the research objects, information on disease types, symptoms, tongue manifestations, pulse conditions, syndrome patterns, moxibustion methods and acupoints were collected. Frequency statistics and community analysis were conducted by the ancient and modern medical record cloud platform V 2.3.7, cluster analysis of high-frequency acupoints was performed by SPSS26.0, association rule analysis of high-frequency acupoints was performed by SPSS Modeler 18.0, and the generated linked data were imported into Cytoscape 3.9.1 for complex network analysis. Knowledge graph of moxibustion for low back pain by ZHOU Meisheng was constructed based on the results of data mining. The data storage and display of knowledge graph were realized through the Neo4j 3.5.25 graph database, and the Cypher query language was used for knowledge graph retrieval and discovery. A total of 219 medical cases were collected, involving 14 disease types, 85 related clinical symptoms, 5 related TCM syndrome types, and 6 moxibustion methods. The acupoints were mostly attributed to the governor vessel, the bladder meridian of foot-taiyang, non-meridian and non-acupoint areas. The core prescription of acupoints derived from complex network analysis included tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34), which were largely coincides with high-frequency acupoints. Cluster analysis obtained 4 cluster combinations. Community analysis yielded 6 communities, each corresponding to different acupoints.The constructed knowledge graph contained 187 nodes and 696 relationships, by retrieving clinical elements related to low back pain, the disease-moxibustion association graph, disease-acupoint association graph, accompanying symptom-acupoint association graph and syndrome type-matching point association graph were obtained. When treating low back pain with moxibustion, ZHOU Meisheng adopts the principle of promoting circulation, distinguishing diseases to determine the treatment, selecting acupoints according to the diseases, and matching points according to the symptoms.And taking tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34) as core acupoints, combined with tenderness point selection, acupoint selection based on meridian and zangfu syndrome differentiation, "sunshine area" acupoint selection, yin-yang acupoint matching. Additionally, he skillfully employs special points, such as Zhongzhu (KI15) and ear tips, pays attention to the reform of moxibustion tools, and innovates the moxibustion techniques, using distinctive moxibustion tools and methods to treat low back pain.

Ontologies define data organization and meaning in Knowledge Graphs (KGs). However, ontologies have generally not been taken into account when designing and generating Application Programming Interfaces (APIs) to allow developers to consume KG data in a developer-friendly way. To fill this gap, this work proposes a method for API generation based on the artefacts generated during the ontology development process. This method is described as part of a new phase, called ontology exploitation, that may be included in the last stages of the traditional ontology development methodologies. Moreover, to support some of the tasks of the proposed method, we developed OATAPI, a tool that generates APIs from two ontology artefacts: the competency questions and the ontology serialization. The conclusions of this work reflect that the limitations found in the state-of-the-art have been addressed both at the methodological and tooling levels for the generation of APIs based on ontology artefacts. Finally, the lines of future work present several challenges that need to be addressed so that the potential of KGs and ontologies can be more easily exploited by application developers.KeywordsOntology EngineeringApplication DevelopmentApplication Programming InterfaceOntology Artefacts

With the advancement in technology in recent years, IT administration and its infrastructure work on vast amount of data that needs to be stored and managed properly, for instance, handling of microservices which contains numerous systems. Client Complaint Management System is one such application system in every company. The IT Customer Support Department handles the complaints/queries of the customers where managing existing databases for support team is difficult due to its limitations to manage and maintain relationship of increasingly complex data. Knowledge Graph is a graph Database which stores and manages complex data by maintaining nodes and edges which define relationship between the nodes. This paper proposes an idea to create Knowledge graph for handling client complaint management system’s data with intention to solve the customer queries quickly and efficiently by using Naive Bayesian Classifier and Artificial Neural Networks for Text Classification, Natural Language Processing techniques and tools for generation of Knowledge graph. Using Knowledge Graph in this System for storing data will handle complex data relationships, reduce cost, ease the access for IT Administrators and staff for cross-referencing, increasing the speed and efficiency of the Data Management techniques as well.

Background The field of information extraction (IE) is currently exploring more versatile and efficient methods for minimization of reliance on extensive annotated datasets and integration of knowledge across tasks and domains. Objective We aim to evaluate and refine the application of the universal IE (UIE) technology in the field of Chinese medical expertise in terms of processing accuracy and efficiency. Methods Our model integrates ontology modeling, web scraping, UIE, fine-tuning strategies, and graph databases, thereby covering knowledge modeling, extraction, and storage techniques. The Enhanced Representation through Knowledge Integration-UIE (ERNIE-UIE) model is fine-tuned and optimized using a small amount of annotated data. A medical knowledge graph is then constructed, followed by validating the graph and conducting knowledge mining on the data stored within it. Results Incorporating the characteristics of whole-course management, we implemented a comprehensive medical knowledge graph–construction model and methodology. Entities and relationships were jointly extracted using the pretrained language model, resulting in 8,525 entity data points and 9,522 triple data points. The accuracy of the knowledge graph was verified using graph algorithms. Conclusion We optimized the construction process of a Chinese medical knowledge graph with minimal annotated data by utilizing a generative extraction paradigm, validating the graph’s efficacy and achieving commendable results. This approach addresses the challenge of insufficient annotated training corpora in low-resource knowledge graph construction, thereby contributing to cost savings in the development of knowledge graphs.

Electronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC. We aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients. The data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor's relationship to LC. The patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient's health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature. By collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

Abstract. Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology.

In contrast to heavy-handed ER-style data models in relational databases, knowledge graphs (or graph databases) capture entity semantics in terms of entity relationships and properties following a simple collect-as-you-go model. While this allows for a more flexible and dynamically adaptable knowledge representation, it comes at the price of more complex querying: with varying degrees of information sparsity, it will gradually become more difficult to figure out what an entity actually represents. Thus, matching the intended schema as specified by a query against actually occurring entity patterns in the graph database needs severe attention on a conceptual level. In this article, we analyze graph patterns as schema information from a graph pattern matching perspective. We argue that every query consists of a mixture of conceptual information (how entities are structured) together with evaluation information (further dependencies and constraints on data) and that this mixture is not always easy to divide. To arrive at truly schema-aware graph query processing, we propose several matching mechanisms, each mandating a specific semantic meaning of the graph pattern, and discuss their practical applicability.

The characteristics of neo4j graph database has been introduced, several methods of constructing graph database based on neo4j importing data have been compared and analyzed, and the “neo4j-admin” method has been finally selected to import the air crash data, to realize the visual display and query. Specific implementation steps as follows:(1) Data sets of air crash worldwide since 1908 [1] have been cleaned and preprocessed to remove incomplete lines and unimportant fields;(2) Crashed aircraft information table, accident details table and the above two table association relationship table have been designed and stored in the form of CSV files;(3)The CSV files have been imported into neo4j using the tool “neo4j-admin”, air crash knowledge graph has been generated; (4) This knowledge graph can be used to query the number of accidents since 1908, the place where the accidents occurred, the number of casualties and the time when the accidents occurred.

Aiming at the problems of monitoring points in the dam with characteristics of numerous and complex spatial relationships, a dam safety monitoring and early warning method based on graph database and aggregation model is proposed. Referring to the knowledge graph element characteristics, the dam monitoring points naming rules were clarified. According to the distribution characteristics of dam monitoring points, the decision rules of spatial adjacency relationship of monitoring points are drawn up, and the dam safety monitoring knowledge graph is constructed. Furthermore, based on the multi-level relationship query capability of the graph database, the correlation response mechanism for monitoring points is established to achieve automatic identification and intelligent grouping of abnormal monitoring points. Finally, an aggregation scoring model for dam monitoring points is proposed to achieve multi monitoring point dam safety monitoring and early warning under the condition of considering the number and spatial distribution of monitoring points comprehensively.

In the era of globalization and digitalization, the banking industry plays a crucial role in economic development through its stability and efficiency but faces various traditional and emerging risks. The development of artificial intelligence has brought revolutionary changes to bank risk management. Knowledge graph technology, which constructs a graph structure of entities and their relationships, provides new perspectives for risk identification and analysis. This study explores the application of knowledge graph technology in bank risk management using Neo4j, demonstrating its advantages in risk identification, assessment, and prediction. By leveraging the interconnected nature of data in a graph database, banks can uncover hidden patterns and relationships that traditional methods might overlook. This approach enables a more comprehensive and dynamic understanding of risk factors, allowing for proactive management and mitigation. Additionally, the use of Neo4j's advanced querying capabilities facilitates real-time analysis and visualization of complex risk scenarios, further enhancing decision-making processes in the banking sector. The integration of machine learning with knowledge graphs can also predict future risks with higher accuracy, making it an invaluable tool for modern risk management practices.

After years of developing English education in colleges and universities, its ecology is urgently in need of optimization and innovation in today’s rapidly changing environment. This paper introduces the knowledge graph into the study of English learning content in colleges and universities, collects the data on English learning content in colleges and universities, and extracts the effective data from the complicated related information data after constructing the ontology of the knowledge diagram manually. Then, a graphical database is used to store them. Create the entity-relationship joint extraction model, the head entity recognition module, the relationship module, and the tail entity recognition module. Then, correctly extract the entity relationships to build the knowledge graph for college and university English learning content. Conduct similarity matrix analysis and cluster analysis on high-frequency keywords in English learning content at colleges and universities and propose corresponding optimization suggestions. Among the high-frequency keywords in the English learning content of colleges and universities, vocabulary has the highest frequency of occurrence, which is 86 times. The top 6 keywords with the highest frequency are: vocabulary, CET-4, comprehension, CET-6, vocabulary, and expression, in order. The keywords are ranked in descending order of closeness distance: grammar (0.326) > CET-4 (0.285) > CET-6 (0.217) > comprehension (0.134) > expression (0.096). The content of English learning in colleges and universities can be clustered into 3 categories (English application skills, English fundamentals, and specific content).