Existing Knowledge Graph Research Articles

Knowledge Graph Completion for High-Speed Railway Turnout Switch Machine Maintenance Based on the Multi-Level KBGC Model

The incompleteness of the existing knowledge graphs in the railway domain creates information gaps, impacting their quality and effectiveness in the operation and maintenance of high-speed railway turnout switch machines. To address this, we propose a multi-layer model (KBGC) that combines KG-BERT, graph attention network (GAT), and Convolutional Embedding Network (ConvE) for knowledge graph completion in railway maintenance. KG-BERT fine-tunes a pre-trained BERT model to extract deep semantic features from entities and relationships, converting them into graph structures. GAT captures key structural relationships between nodes using an attention mechanism, producing enriched semantic and structural embeddings. Finally, ConvE reshapes and convolves these embeddings to learn complex entity interactions, enabling accurate link prediction. Extensive experiments on the HRTOM dataset, containing triplet data from high-speed railway turnout switch machines, demonstrate the model’s effectiveness, achieving an MRR of 50.8% and a Hits@10 of 60.7%. These findings show that the KBGC model significantly improves knowledge graph completion, aiding railway maintenance personnel in decision making and preventive maintenance, and providing new tools for railway maintenance applications.

MuHeQA: Zero-shot question answering over multiple and heterogeneous knowledge bases

There are two main limitations in most of the existing Knowledge Graph Question Answering (KGQA) algorithms. First, the approaches depend heavily on the structure and cannot be easily adapted to other KGs. Second, the availability and amount of additional domain-specific data in structured or unstructured formats has also proven to be critical in many of these systems. Such dependencies limit the applicability of KGQA systems and make their adoption difficult. A novel algorithm is proposed, MuHeQA, that alleviates both limitations by retrieving the answer from textual content automatically generated from KGs instead of queries over them. This new approach (1) works on one or several KGs simultaneously, (2) does not require training data what makes it is domain-independent, (3) enables the combination of knowledge graphs with unstructured information sources to build the answer, and (4) reduces the dependency on the underlying schema since it does not navigate through structured content but only reads property values. MuHeQA extracts answers from textual summaries created by combining information related to the question from multiple knowledge bases, be them structured or not. Experiments over Wikidata and DBpedia show that our approach achieves comparable performance to other approaches in single-fact questions while being domain and KG independent. Results raise important questions for future work about how the textual content that can be created from knowledge graphs enables answer extraction.

Interaction-knowledge semantic alignment for recommendation

In order to alleviate the issue of data sparsity, knowledge graphs are introduced into recommender systems because they contain diverse information about items. The existing knowledge graph enhanced recommender systems utilize both user–item interaction data and knowledge graph, but those methods ignore the semantic difference between interaction data and knowledge graph. On the other hand, for the item representations obtained from two kinds of graph structure data respectively, the existing methods of fusing representations only consider the item representations themselves, without considering the personalized preference of users. In order to overcome the limitations mentioned above, we present a recommendation method named Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR). By introducing a semantic alignment module, the semantic difference between the interaction bipartite graph and the knowledge graph is reduced. The representation of user is integrated during the fusion of representations of item, which improves the quality of the fused representation of item. To validate the efficacy of the proposed approach, we perform comprehensive experiments on three datasets. The experimental results demonstrate that the IKSAR is superior to the existing methods, showcasing notable improvement.

Heterogeneous Views and Spatial Structure Enhancement for triple error detection

Knowledge graph error detection is to identify erroneous triples in knowledge graphs that are inconsistent with objective facts in the real world. In practice, the quality of knowledge graphs is an indispensable foundation for the widespread and accurate knowledge application services such as intelligent retrieval and human machine dialogue. Technically, the existing knowledge graph error detection methods face the following two problems: few available negative samples of triples, and an uneven data distribution. That distribution is caused by the large disparity in the number of head and tail entities belonging to the same relationship and the disparity in the number of triples with different relationships. To alleviate these problems, this paper proposes an approach based on the Heterogeneous Views and Spatial Structure Enhancement (HVSSE) in a contrastive learning framework for triple error detection task. Specifically, the heterogeneous views are constructed to include four kinds of triple views, i.e., positive and negative triple views based on head or tail entity co-occurrence. Moreover, Graph-Spatial-Transformer with an explicit spatial structure encoding is designed to fully capture the contextual information of triple nodes. Thereby, driven by the framework of contrastive learning, our HVSSE model can not only learn more discriminative embedding of triples, but also capture the local structure of triples and global contextual information of knowledge graphs. Experimental results on five public datasets indicate that our proposed approach is superior to the state-of-the-art methods, showing its the effectiveness on triple error detection.

Creative influence prediction using graph theory

Creative influence is responsible for a considerable part of the creative process of an artist and can largely be associated with their social circle. It has been observed that the type and amount of relationships with other fellow artists correlates with the success of an artist. Most of the recent literature has focused on using artefact similarity as a proxy for creative influence between two artists. However, this approach neglects the significance of an artist’s social network. In this work, we rely on an ontology that comprehensively model the relationship between individuals as a Knowledge Graph and we design an explainable method based on graph theory to predict the influences of an artist given their social network. We evaluate our method on a dataset of relationships between Jazz musicians and achieve accurate results when compared to baselines that rely on the distribution of the data. Our results are aligned with relevant works from the socio-cognitive and psychology fields. We show that our method generalises to resources where information on influence is not directly available and can be used to enrich existing Knowledge Graphs. The code and the ontology developed is shared at https://github.com/n28div/influence_prediction under CC-BY license.

Spatio-temporal knowledge embedding method considering the lifecycle of geographical entities

With the emergence of substantial amounts of data featuring spatio-temporal characteristics, research on spatio-temporal knowledge has garnered widespread attention. However, the existing knowledge graph embedding methods are difficult to embed spatio-temporal knowledge in continuous time, failing to meet the unique embedding requirements of geographic entities for uninterrupted, constantly changing, and smoothly continuous embeddings. To address this issue, we begin by outlining a version-based representation of spatio-temporal knowledge. Building on this foundation, we propose a novel and effective knowledge graph embedding model called Version Embedding (VerE), which embeds continuous time into vector space, transforms geographic entities into corresponding versions using an attention mechanism, and calculates spatio-temporal knowledge scores under the constraint of version similarity regularization. Subsequently, we introduce a method for link prediction based on unknown time with the aim of assessing the model’s generalization capabilities in time representation. Finally, experiments conducted on two real-world datasets demonstrate significant performance improvement of VerE compared to most existing models, and its ability to maintain stability and continuity in link prediction under unknown time. These experiments confirmed the effectiveness of the proposed model and provided new perspectives and methods for spatio-temporal knowledge embedding.

Research on Knowledge Graph Construction Methods for News Domain

With the rapid development of the Internet and artificial intelligence technology, massive text data is growing exponentially, and the news industry is generating massive text data every moment. Most of the existing knowledge graphs focus on tourism, healthcare, finance and other fields, while there are relatively few in the news field, which is not well constructed. Aiming at the above problems, this paper firstly devotes to the construction of ontology library in news domain, and precisely defines 8 types of entities and 9 types of relations, which lays a solid foundation for the construction of news knowledge graph. Subsequently, with the help of web crawler technology, we extensively collected news text and image data, and carried out rigorous knowledge cleaning, extraction and fusion processing on these data to ensure the accuracy and completeness of the data. Finally, with the help of Neo4j graph database, the effective storage of news knowledge is realised, and the news domain knowledge graph is successfully constructed. It provides new ideas and means for information mining and utilisation in the news industry, and also provides rich, high-quality data support for downstream applications, which is expected to promote the intelligent development of the news field.

The RDF2vec family of knowledge graph embedding methods

Knowledge graph embeddings represent a group of machine learning techniques which project entities and relations of a knowledge graph to continuous vector spaces. RDF2vec is a scalable embedding approach rooted in the combination of random walks with a language model. It has been successfully used in various applications. Recently, multiple variants to the RDF2vec approach have been proposed, introducing variations both on the walk generation and on the language modeling side. The combination of those different approaches has lead to an increasing family of RDF2vec variants. In this paper, we evaluate a total of twelve RDF2vec variants on a comprehensive set of benchmark models, and compare them to seven existing knowledge graph embedding methods from the family of link prediction approaches. Besides the established GEval benchmark introducing various downstream machine learning tasks on the DBpedia knowledge graph, we also use the new DLCC (Description Logic Class Constructors) benchmark consisting of two gold standards, one based on DBpedia, and one based on synthetically generated graphs. The latter allows for analyzing which ontological patterns in a knowledge graph can actually be learned by different embedding. With this evaluation, we observe that certain tailored RDF2vec variants can lead to improved performance on different downstream tasks, given the nature of the underlying problem, and that they, in particular, have a different behavior in modeling similarity and relatedness. The findings can be used to provide guidance in selecting a particular RDF2vec method for a given task.

Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development

This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.

Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development

This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.

Domain-specific knowledge graph rule pattern mining based on generative adversarial networks

Most existing knowledge graphs (KGs) in specific domains suffer from problems of insufficient structural knowledge mining, superficial constraint of rules, incomplete system of rule patterns and higher error rate in the process of automated rule generation. In this paper, we present an adversarial generative approach for rule mining based on generative adversarial networks (GANs). The method firstly extracted a rule set according to a specific rule pattern defined manually, the rule set is then used as the adversarial training dataset for the GAN, That is, the discriminator determines whether a rule is true or not by learning the pattern of the rule set, and the generator tricks the discriminator by forging rules and improves according to the feedback from the generator.Finally, a generator is obtained to generate new rules that conform to the rule pattern, and a discriminator is obtained to determine the confidence of the automatically constructed triples.

Fast knowledge graph completion using graphics processing units

Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate N×N×R vector operations, where N is the number of entities and R is the number of relation types. It is very costly.In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define transformable to a metric space and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is transformable to a metric space. After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem.

Fair large kernel embedding with relation-specific features extraction for link prediction

Knowledge graph embedding is a crucial technique for addressing the challenge of incomplete knowledge graphs, and convolutional neural networks are widely applied in this domain. However, convolutional neural networks face limitations in effectively capturing long-range dependencies between entities and relations in feature maps. Moreover, existing knowledge graph embedding models often adopt a uniform perspective on all relations within a knowledge graph, thereby overlooking the relation-specific features. To address the two issues, two novel knowledge graph embedding models, Large Kernel Embedding (LKE) and Large Kernel Embedding with Relation-specific Features Extraction (LKER), are proposed. The effectiveness of knowledge graph embedding is improved by integrating fair large kernel attention and introducing a parallel branch for relation-specific feature extraction in these models. Specifically, fair large kernel attention is incorporated into LKE, capturing long-range dependencies within the model. Based on LKE, LKER adds a parallel relationship-specific feature extraction branch to obtain more comprehensive relationship-specific feature information. The proposed models are extensively evaluated on five benchmark datasets, and the results show that the proposed models yield significant performance improvements in link prediction compared to classical and latest knowledge graph embedding models.

Multi-Modal Knowledge Graph Construction and Application: A Survey

Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strengths and weaknesses of different solutions. We finalize this survey with open research problems relevant to MMKGs.

KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance.

Knowledge graphs have been commonly used to represent relationships between entities and are 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 did not consider various user needs and characteristics of knowledge graphs. Exploratory approaches specifically designed to uncover and summarize insights in knowledge graphs have not been well studied yet. In this article, 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 the exploration of knowledge graphs with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and helping explore the entire network.

Event-Centric Temporal Knowledge Graph Construction: A Survey

Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.

Distant Supervision-based Relation Extraction for Literature-Related Biomedical Knowledge Graph Construction

Background: The task of relation extraction is a crucial component in the construction of a knowledge graph. However, it often necessitates a significant amount of manual annotation, which can be time-consuming and expensive. Distant supervision, as a technique, seeks to mitigate this challenge by generating a large volume of pseudo-training data at a minimal cost, achieved by mapping triple facts onto the raw text. Objective: The aim of this study is to explore the novelty and potential of the distant supervisionbased relation extraction approach. By leveraging this innovative method, we aim to enhance knowledge reliability and facilitate new knowledge discovery, establishing associations between knowledge from specific biomedical data or existing knowledge graphs and literature. Methods: This study presents a methodology to construct a biomedical knowledge graph employing distant supervision techniques. Through establishing links between knowledge entities and relevant literature sources, we methodically extract and integrate information, thereby expanding and enriching the knowledge graph. This study identified five types of biomedical entities (e.g., diseases, symptoms and genes) and four kinds of relationships. These were linked to PubMed literature and divided into training and testing datasets. To mitigate data noise, the training set underwent preprocessing, while the testing set was manually curated. Results: In our research, we successfully associated 230,698 triples from the existing knowledge graph with relevant literature. Furthermore, we identified additional 205,148 new triples directly sourced from these studies. Conclusion: Our study markedly advances the field of biomedical knowledge graph enrichment, particularly in the context of Traditional Chinese Medicine (TCM). By validating a substantial number of triples through literature associations and uncovering over 200,000 new triples, we have made a significant stride in promoting the development of evidence-based medicine in TCM. The results underscore the potential of using a distant supervision-based relation extraction approach to both validate and expand knowledge bases, contributing to the broader progression of evidence-based practices in the realm of TCM.

MPTN: A message-passing transformer network for drug repurposing from knowledge graph

Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.

Knowledge graph embedding with inverse function representation for link prediction

Knowledge graphs, which contains billions of facts, have become a useful resource for many AI-related downstream tasks, such as knowledge inference, personalized search. However, although existing knowledge graphs contain billions of facts, most knowledge graphs are still incomplete due to the inability to manually access all the facts in the world. In order to automatically predict missing triples, researchers have proposed many knowledge graph embedding models for link prediction task. However, most existing models have flaws in handling symmetric relations, as they will get the same constant |r|=1 or r=0 for any symmetric relations. In order to address such flaws, we propose a novel model called InversEF, which can get different representations for symmetric relations by learning inverse function representation of triples. In InversEF, for the complex relations, we explicitly capture the structured information of each entity by using the interaction between entities and relations. Due to the symmetry between the function and its corresponding inverse function, InverseEF can work well on the symmetric and inverse relations. Experimental results show that, InversEF achieves new state-of-the-art performance.

A hierarchical spatio‐temporal object knowledge graph model for dynamic scene representation

AbstractSpatio‐temporal knowledge is essential in understanding the dynamic aspects of complex scenes. However, existing knowledge graphs have limitations, such as inadequate time description, inflexible expression of semantic relationships, and difficulties in accessing GIS platforms. The article proposes the spatio‐temporal object knowledge graph (STOKG), consisting of the object concept layer, spatio‐temporal object layer, and dynamic version layer. To demonstrate the practical usefulness of the STOKG model, the Henan epidemic knowledge graph is created using epidemiological data from early 2020, which shows the dynamic evolution of the spatio‐temporal objects of cases from the geography and semantic perspectives. Finally, the STOKG model is compared with the existing models in terms of accuracy, completeness and repetitiveness. The experimental results show that the STOKG model provides a more flexible and comprehensive approach to representing spatio‐temporal knowledge, which is useful for applications in fields such as geography, epidemiology, and environmental science.