UMEAD: Unsupervised Multimodal Entity Alignment for Equipment Knowledge Graphs via Dual-Space Embedding

The representation learning of knowledge graph refers to embedding entities and relations in knowledge graph into a low-dimensional dense vector space. Existing knowledge graph embedding models mostly chose Euclidean Space as their vector space and consider each fact triple in knowledge graph independently. However, Euclidean Space is unable to represent knowledge effectively due to its strict constraints and mathematical expression. Besides, entities in knowledge graph are not isolated, while these models ignore the association between entities. To solve the problems above, we proposed a text-enhanced knowledge graph representation learning model in Hyperbolic Space. We utilize rich semantic information of entity description by using Transformer Encoder to enhance the ability of knowledge representation. Besides, we embed entities and relations into Hyperbolic Space, which can better capture hierarchical information of the knowledge graph. Experiments on benchmark dataset show that our method achieves better performance compared with other state-of-art methods.KeywordsKnowledge graphRepresentation learningEmbedding modelHyperbolic space

Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.

Cross-lingual entity alignment (EA) aims to find the equivalent entities between cross-lingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.

With the rapid development of intelligent coal mine, the field of coal mine also ushered in a large number of heterogeneous data information. Aiming at the data information existing in the field of coal mine, a coal mine safety knowledge atlas centered on coal mine accident cases is constructed. The construction of knowledge graph mainly uses crawler technology to obtain text data of coal mine, Albert-BilSTM-CRF model to realize entity recognition, Albert-BilSTM-ATT to complete relation extraction, and obtain triplet form in the field of coal mine safety. Finally, the obtained triplet form is imported into Neo4J graph database. Form a structured knowledge graph.

Since the causes of aviation accidents and risks are complicated, concealed, unpredictable and difficult to be investigated, in order to achieve the efficient organization and knowledge sharing of the historical cases of aviation risk events, this paper put forward the method of constructing vertical knowledge graph for aviation risk field. Firstly, the data-driven incremental construction technology is used to build aviation risk event ontology model. Secondly, the pattern-based knowledge mapping mechanism, which transform structured data into RDF (Resource Description Framework) data for storage, is proposed. And then the application, update and maintenance of the knowledge graph are described. Finally, knowledge graph construction system in aviation risk field is developed; and the data from American Aviation Safety Reporting System (ASRS) is used as an example to verify the rationality and validity of the knowledge graph construction method. Practice has proved that the construction of knowledge graph has a guiding significance for the case information organization and sharing on the field of aviation risk.

An educational knowledge graph provides students and teachers with detailed knowledge organization and a clear concept structure by extracting knowledge points and relationships from textbooks. A high-fidelity knowledge graph is essential for precise teaching and personalized learning. However, as an important step in knowledge graph construction, coreference resolution is often ignored or left to the end. This neglect leads to a loss in the high fidelity of the knowledge graph and may also cause clearly underestimated focuses and fewer associations between knowledge points, although the ratio of the pronouns to the entire corpus is very small (less than 5‰). In this paper, a rule and semantic-based method is proposed to resolve coreference in the knowledge graph constructed from a biology textbook. Compared with the other three algorithms, it has a better precision ratio and recall ratio. By comparing the two knowledge graphs constructed before and after coreference resolution, it can be seen that the focus has changed significantly to better align with the text, and the associations between the knowledge points are more consistent with intuition. This outcome suggests that coreference resolution improves the high fidelity of an educational knowledge graph.

Virtual geographic scenes can significantly facilitate the visualization and comprehension of the real world. However, the multitude of object types and complex relationships they contain make it challenging to display embedded geographic knowledge. Knowledge graphs help organize object entities and their relationships within scenes, which makes it easier to store and express geographic knowledge. Current methods for constructing knowledge graphs for virtual geographic scenes suffer from issues such as weak automation, low dimensionality, and poor cognitive efficiency. Thus, this paper presents a method for automatically constructing knowledge graphs by integrating intelligent systems. The method involves designing a multiagent system to extract entities and relationships hidden in virtual geographic scenes. In addition, a three-dimensional visualization approach is proposed for knowledge graphs constrained by spatiotemporal relationships. A virtual scene containing 2,183 objects was selected as the experimental area for constructing and displaying the knowledge graph. The results demonstrate that the method constructs a knowledge graph with 12,958 relationships within 300 s in common network and hardware settings. In addition, a knowledge graph offers better completeness at small scales compared to other geographic knowledge graphs, and it can also enhance the cognitive efficiency of understanding scene relationships.

Arabic Knowledge Graph Construction: A close look in the present and into the future

The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the use of knowledge graphs (KGs) for representing seismic data and metadata to improve data exploration and analysis, focusing on usability, flexibility, and extensibility. Using constraints derived from domain knowledge in seismology, we define a semantic model of seismic station and event information used to construct the KGs. Our approach utilizes the capability of KGs to integrate data across many sources and diverse schema formats. We use schema-diverse, real-world seismic data to construct KGs with millions of nodes, and illustrate potential applications with three big-data examples. Our findings demonstrate the potential of KGs to enhance the efficiency and efficacy of seismological workflows in research and beyond, indicating a promising interdisciplinary future for this technology.

<p>With the development of digital education, colleges and universities are facing challenges in managing and utilizing digital resources. As an effective way of knowledge organization and representation, Knowledge graph can transform digital resources into structured entities, attributes and relationship forms, and provide personalized learning support and resource recommendation. By analyzing the characteristics, construction status and existing problems of digital resources in vocational colleges, this paper puts forward the overall plan and methods of digital resources knowledge graph construction, including data collection and sorting, knowledge graph construction, verification and optimization, and gives the general process design of knowledge graph construction; Then, this paper discusses the typical application scenarios of digital resources knowledge graph in vocational colleges, such as learning resource recommendation, path planning, teaching assistance and resource sharing; Finally, the challenges in this field, including data quality, knowledge graph updating and maintenance, privacy and security, are discussed, and the future trends and research directions are prospected. The research results are of great significance for promoting the informatization and intelligent development of vocational college education.</p> <p> </p>

Knowledge graph completion is an important research issue in knowledge graph construction, knowledge engineering, and natural language processing. A knowledge graph is a knowledge support for realizing accurate knowledge services in general and professional fields. It is also an important breakthrough foundation in information retrieval, question-and-answer interactions, and information recommendation. The low quality and small scale of the knowledge graph are the main bottlenecks that hinder its wide applications. The purpose of knowledge graph completion is to build a large-scale and high-quality knowledge graph for continuously updating and expanding the knowledge graph. Aiming at the difficulty of knowledge graph completion methods to extract deep semantic features from auxiliary information, such as unstructured texts, this study proposes a knowledge graph completion method based on parsing graph embedding and weighted graph convolutional network. This method uses the weighted graph convolutional network to model the semantic dependency parsing of the entity description and construct the semantic dependency parsing graph embedding. Furthermore, it introduces a multi-grained sentence-embedding generation method of the entity description, which is intended to build entity representation learning that can capture multi-grained semantics and deep-level semantic features. The experimental results on two public datasets show that the proposed knowledge graph completion approach outperforms the existing methods, thereby demonstrating its effectiveness and superiority.

Knowledge graph (KG) is one of key technologies for intelligently answering questions, which can reduce customer service’s costs and improve its self-service capabilities. However, the description of questions is often ambiguous, and the operation and maintenance of online KG based QA services introduces a high cost. To address the above issues, this paper proposes a semantic enhancement based dynamic construction of domain knowledge graph for answering questions. We first employ a model combining LSTM and CRF to identify entities, and then propose a semantic enhancement method based on topic comparison to introduce external knowledge. We employ heuristic rules to get optimal answers, and then periodically update the global KG according to the integer linear programming solver’s results. Our approach can achieve a high precise answering results with a low response delay by accurately recognizing entities, automatically mapping domain knowledge to the KG, and online updating the KG. The experimental results show that our approach compared with the traditional method improves the precision, recall and F-measure by 6.41%, 16.46% and 11.17%, respectively.

Under the background of widespread digital teaching, ideological and political education in higher education institutions faces challenges in delivering targeted resources and personalised recommendations. Traditional manual recommendation methods are no longer sufficient to meet demand, and knowledge graph-based recommendation offers a novel approach to addressing this challenge. However, existing research exhibits shortcomings in knowledge graph construction, recommendation algorithms, scenario adaptation, and experimental validation. This study constructs and validates a knowledge graph-based personalised recommendation framework for ideological and political education in higher education institutions. This framework integrates multi-dimensional entities encompassing resources, users, and scenarios in knowledge graph construction. The recommendation algorithm combines path features from the knowledge graph with user profiling. Scenario adaptation aligns with the ideological and political education objective of ‘value guidance’ to accommodate students' intellectual dynamics. Experimental validation is based on authentic data from university ideological and political courses alongside student behavioural data. The functional layer of this system encompasses the user interface, knowledge graph, data management, algorithms, database, and resource repository. Core modules include the ideological and political education knowledge graph, data management, personalised recommendation algorithms, and feedback optimisation. This system enables the systematic management and precise recommendation of ideological and political resources, providing technological support for tailored teaching approaches in higher education institutions. It holds significant practical value in advancing the digital transformation and enhancing the quality of ideological and political education within universities.

One table and seven figures of this paper. Table 1 presents Wang Zai’s personal information for more consistent question answering. Figure 1 is an overview of Sogou knowledge graph construction framework. The framework could be divided into three parts: Data Preparation contains operations including collecting data from various sources, extracting data from both structured source and free text and normalizing data; Knowledge graph construction contains all models to build a knowledge graph based on the extracted and normalized data; Application is composed of applications or services of a knowledge graph. A box with solid line represents an operation or model to process data while a box with dashed line represents the intermediate data. Figure 2 shows overview of Sogou knowledge graph storage architecture. Figure 3 shows after query entity linking, entity-related information and pictures are shown in search results. Figure 4 shows that search engine also gives related entity recommendations, due to entity relations in our KG. Figure 5 shows long text entity service used for Anchor Text generation. Figure 6 presents KBQA in Sogou Search Engine, which answers the query directly in the first search result. In Figure 7, a user is having a dialogue with our conversational assistant, Wang Zai.

Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge graphs, which is liable to cause high error rates in dynamic knowledge graph construction. In this article, we propose a knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which can resolve facts confliction with high precision by combing time attributes, semantic embedding representations, and graph structure features. Kgedl first trains the semantic embedding vector through the relationships between entities. Then, the path embedding vector is trained from the graph structures of knowledge graphs, and the time attributes of entities are combined with the semantic and path embedding vectors. Finally, Kgedl uses a recurrent neural network to make the inconsistent facts appear in the dynamic evolution of the knowledge graph consistent. A large number of experiments on real datasets show that Kgedl outperforms the state-of-the-art methods. Especially, Kgedl achieves 23% higher performance than the classical method numerical Probabilistic Soft Logic (nPSL).in the metric HITS@10. Also, extensive experiments verified that our proposal possess better robustness by adding noise data.