Knowledge Graph Construction Research Articles

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.

Knowledge-Powered Recommendation for an Improved Diet Water Footprint

According to WWF, 1.1 billion people lack access to water, and 2.7 billion experience water scarcity at least one month a year. By 2025, two-thirds of the world's population may be facing water shortages. This highlights the urgency of managing water usage efficiently, especially in water-intensive sectors like food. This paper proposes a recommendation engine, powered by knowledge graphs, aiming to facilitate sustainable and healthy food consumption. The engine recommends ingredient substitutes in user recipes that improve nutritional value and reduce environmental impact, particularly water footprint. The system architecture includes source identification, information extraction, schema alignment, knowledge graph construction, and user interface development. The research offers a promising tool for promoting healthier eating habits and contributing to water conservation efforts.

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

Abstract 3528: Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4

Abstract N6-methyladenosine (m6A) is the most abundant mRNA modification with a crucial role in cellular processes. Its involvement in cancers is well-established. Dysregulation of m6A is linked to cancer initiation, progression, and therapy resistance, impacting the tumor microenvironment (TME) and promoting metastasis. Understanding m6A's regulatory role in cancer is essential for identifying new therapeutic targets. We considered the construction of a knowledge graph (KG) representing molecular regulatory mechanisms (MRMs) of m6A in healthy conditions and different cancers from PubMed papers. Such a KG consists of interconnected triplets of (head node, regulatory relationship, tail node), each denoting regulation between genes or a gene and a phenotype/process. Advances in natural language processing (NLP) have provided tools such as SemRep and GNBR to automate the extraction of these triplets from the literature. However, these methods don’t extract contextual information, such as the type of cancers or cells in which these MRMs occur, leading to contradictory regulations in the constructed KG. In addition, existing approaches were designed to extract generic regulatory relations and therefore struggled in properly capturing unique concepts associated with m6A regulatory mechanisms. To tackle this challenge, we designed a novel ChatGPT-4 prompt to extract relational graphs from papers tailored for m6A regulatory mechanisms. An annotated dataset of 400 titles on m6A-related MRMs were created to evaluate the performance. We then applied the prompt to 1023 papers to create a m6A Molecular Regulatory Mechanism Knowledge Graph (m6A-MRM-KG). This graph illuminates m6A's roles in gene expression regulation, especially in cancer and immunity. Anticipated to enhance our understanding of cancer development, it provides insights into potential therapeutic strategies. The m6A-MRM-KG not only organizes information but also empowers researchers to uncover novel insights in the dynamic relationships of m6A modifications in diverse biological contexts. Citation Format: Sumin Jo, Xidong Wu, Yiming Zeng, Arun Das, Ting-He Zhang, David Alexander Spellman, Adam Ferris, Shou-Jiang Gao, Jianqiu (Michelle) Zhang, Yu-Chiao Chiu, Yufei Huang. Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3528.

Integrated knowledge graph construction framework for places-of-interest retrieval using a property graph database

ABSTRACT With recent technological advances, the efficient extraction and utilization of valuable information from large-scale data sources have become increasingly important. The development of knowledge graphs (KGs) based on logical relationships between data has garnered attention from various location-related services. To provide results that satisfy the diverse preferences of individuals, explicit attributes and implicit semantic context must be considered during the retrieval of places of interest (POIs). Most POI retrievals often involve not just examining detailed information about places but also specifying places for intended visits. Therefore, spatial knowledge regarding the surroundings of POIs, such as proximity and accessible routes, should be incorporated to support decision-making. In this study, we propose a comprehensive framework for constructing a KG for POI retrieval (PKG), which adeptly integrates the place attributes, semantic features, and spatial context of locations. The core objective of this framework is to acquire suitable data for facilitating POI retrieval that effectively considers diverse user preferences for places. After constructing a PKG of Orlando (FL, USA), we verified the practical applicability of the proposed framework by conducting 10 types of distinct POI retrieval queries catering to a range of user preferences. The graph queries returned a list of POIs that precisely aligned with the requirements of users on not only the explicit attributes of places but also the spatial and semantic features while providing detailed travel route information to these destinations. In conclusion, the PKG enabled POI retrieval that satisfied user preferences and diversified the retrieved places and the information provided. As the PKG offers flexibility in data integration without physical constraints, it can be expanded by incorporating information from various sources.

A novel joint extraction model based on cross-attention mechanism and global pointer using context shield window

Relational triple extraction is a critical step in knowledge graph construction. Compared to pipeline-based extraction, joint extraction is gaining more attention because it can better utilize entity and relation information without causing error propagation issues. Yet, the challenge with joint extraction lies in handling overlapping triples. Existing approaches adopt sequential steps or multiple modules, which often accumulate errors and interfere with redundant data. In this study, we propose an innovative joint extraction model with cross-attention mechanism and global pointers with context shield window. Specifically, our methodology begins by inputting text data into a pre-trained RoBERTa model to generate word vector representations. Subsequently, these embeddings are passed through a modified cross-attention layer along with entity type embeddings to address missing entity type information. Next, we employ the global pointer to transform the extraction problem into a quintuple extraction problem, which skillfully solves the issue of overlapping triples. It is worth mentioning that we design a context shield window on the global pointer, which facilitates the identification of correct entities within a limited range during the entity extraction process. Finally, the capability of our model against malicious samples is improved by adding adversarial training during the training process. Demonstrating superiority over mainstream models, our approach achieves impressive results on three publicly available datasets.

Higher-order GNN with Local Inflation for entity alignment

In the age of massive data, the construction of knowledge graph has increasingly become a forceful support for the downstream applications of artificial intelligence. However, the information of entities in knowledge graphs is usually incomplete, so it is urgent to supplement the relations of entities through entity alignment task. Frustratingly, the current entity alignment models are facing serious challenges. First, some models only focus on structural features and other auxiliary information (e.g., attributes, images and descriptions), but ignore the features of the entity itself can be scaled resulting in over-smoothing issue. Second, most models utilize higher-order networks to aggregate neighborhood information by stacking layers, but the training cost of these models are drastically higher. Third, most models are supervised or semi-supervised, but there are few pre-aligned seeds for alignment, which greatly limits the improvement of model performance. Hence, to address the above three issues, we propose a Higher-Order Graph Neural Network with Local Inflation for entity alignment, named HOLI-GNN. Specifically, we introduce a local inflation mechanism, which enlarges the each feature of entities to mitigate the impact of over-smoothing caused by neighborhood aggregation. Additionally, we propose a novel higher-order encoder to capture higher-order information. Furthermore, our model also employ currently popular iteration strategy to increase labeled entity pairs, which can markedly promote the performance of align task. Finally, we perform comprehensive experiments to validate the effectiveness of our model on benchmark datasets. The results strongly indicate that our model exhibits better performance than the state-of-the-art models.

ParTRE: A relational triple extraction model of complicated entities and imbalanced relations in Parkinson’s disease

The relational triple extraction of unstructured medical texts about Parkinson’s disease is critical for the construction of a medical knowledge graph. However, the triple entities in Parkinson’s disease are usually complicated and overlapped, which impedes the accuracy of triple extraction, especially in the case of rarely available corpus. Therefore, this study first builds a corpus about Parkinson’s disease. Then, a tagging-based three-stage relational triple extraction model is proposed, named ParTRE. To enhance the contextual representation of sentences, the proposed model employs BiLSTM modules to capture fine-grained semantic information. Additionally, a conditional normalization layer is used so that entity pairs can be extracted accurately from two complementary directions. As for the imbalanced relationship categories, an adaptive loss function strategy based on focal loss is derived by assigning different weights to relationship categories and reducing the loss of easy-to-classify samples. The model performance is evaluated on the Parkinson’s corpus and public datasets. The results indicate that the proposed model achieves an overall F1-score of 93.3 % on the Parkinson’s corpus and comparable performance on public datasets compared with the state-of-the-art methods. Moreover, a satisfactory result is achieved by the proposed model on conquering the overlapped entities and imbalanced relationship categories. Owing to demonstrated availability and validity, the proposed method can be integrated with medical knowledge graphs and therefore benefits medical intelligence.

Applying a Context-based Method to Build a Knowledge Graph for the Blue Amazon

ABSTRACT Knowledge graphs are employed in several tasks, such as question answering and recommendation systems, due to their ability to represent relationships between concepts. Automatically constructing such a graphs, however, remains an unresolved challenge within knowledge representation. To tackle this challenge, we propose CtxKG, a method specifically aimed at extracting knowledge graphs in a context of limited resources in which the only input is a set of unstructured text documents. CtxKG is based on OpenIE (a relationship triple extraction method) and BERT (a language model) and contains four stages: the extraction of relationship triples directly from text; the identification of synonyms across triples; the merging of similar entities; and the building of bridges between knowledge graphs of different documents. Our method distinguishes itself from those in the current literature (i) through its use of the parse tree to avoid the overlapping entities produced by base implementations of OpenIE; and (ii) through its bridges, which create a connected network of graphs, overcoming a limitation similar methods have of one isolated graph per document. We compare our method to two others by generating graphs for movie articles from Wikipedia and contrasting them with benchmark graphs built from the OMDb movie database. Our results suggest that our method is able to improve multiple aspects of knowledge graph construction. They also highlight the critical role that triple identification and named-entity recognition have in improving the quality of automatically generated graphs, suggesting future paths for investigation. Finally, we apply CtxKG to build BlabKG, a knowledge graph for the Blue Amazon, and discuss possible improvements.

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. 1

A Construction of Knowledge Graph for Semiconductor Industry Chain Based on Lattice-LSTM and PCNN Models

<p>This paper mainly focuses on building the knowledge graph of semiconductor industry chain. The main research contents include knowledge extraction, knowledge storage, and construction of knowledge graph in semiconductor field. The crawler technology and character recognition technology are used to obtain semiconductor industry chain information from the Internet, magazines, and institutions to establish the original data set. Then, Lattice Long Short-Term Memory (Lattice-LSTM) model is used to implement the entity extraction and recognition. The piecewise convolutional neural network (PCNN) model based on the sentence-level attention mechanism is used to extract relationships and obtain entity triples. The semiconductor dictionary library is constructed through the obtained structured data. The dictionary library and Chinese natural language toolkit HanLP are combined to annotate unstructured text data for knowledge extraction. Neo4j graph database is used to store the extracted data of semiconductor industry chain. Finally, Spring Boot and Vue technology are used to create a knowledge graph system.</p> <p> </p>

Visual Knowledge Graph Construction of Self-directed Learning Ability Driven by Interdisciplinary Projects

INTRODUCTION: The application of interdisciplinary information technology is becoming more and more widespread, and the application of visual knowledge mapping in the process of students' independent learning is also becoming more and more important; therefore, in this context, takes the history discipline as a starting point to study the construction of visual knowledge mapping of students' independent learning ability under the drive of interdisciplinary projects.OBJECTIVES: To enrich the means of student independent learning aids in China's history discipline and enhance the modernization level of China's history discipline construction; to solve the problem that student independent learning ability under the drive of China's interdisciplinary projects can not be visualized and observed; to further improve China's distance education environment and to enhance the educational capacity of the history discipline.METHODS: Firstly, the relevant modeling uses a visual knowledge map. Secondly, the neural network model assesses students' independent learning ability in history learning. Finally, the convolutional neural network model is used to assess the efficiency of the knowledge map.RESULTS: The Sig and Tanh function models have better robustness, and the ReLU and PReLU functions have weaker interdisciplinary driving performance. However, the iterative Knownledge1 and Knownledge2 models have better robustness of the visualized knowledge graph.CONCLUSION: In studying history, the interdisciplinary, project-driven, and independent learning ability of students could be more vital, and our country should vigorously develop new information network technology to improve the status quo of history discipline education in China.

Research on Knowledge Graph Construction Technology Based on Intellectual Property Legal Documents

Research on Knowledge Graph Construction Technology Based on Intellectual Property Legal Documents

A Semi-Supervised Failure Knowledge Graph Construction Method for Decision Support in Operations and Maintenance

A Semi-Supervised Failure Knowledge Graph Construction Method for Decision Support in Operations and Maintenance

“三全育人”视角下高校宿舍文化建设研究热点及趋势

From the perspective of “comprehensive education”, this article adopts scientific econometric analysis and knowledge graph construction to study the hotspots and trends in the construction of dormitory culture in universities. Using CiteSpace and VOSviewer software for analysis, it was found that the number of publications on the construction of dormitory culture in universities has significantly increased since 2018. The research hotspots mainly focus on keywords such as “three comprehensive educations”, “universities”, “dormitory culture”, and “vocational colleges”. The research mainly focuses on the connotation of dormitory culture, educational models, practical paths, and the role of party building guidance in talent cultivation. Future research should strengthen quantitative research, expand the scope of research objects, and conduct in-depth evaluations of the implementation effect of “three comprehensive educations” in dormitory culture construction, in order to provide more effective management strategies and training plans, and promote the continuous improvement and development of university dormitory culture. The research on the construction of dormitory culture in universities has flourished in recent years, especially from the perspective of “three comprehensive educations”, which has received widespread attention. This provides important theoretical guidance and support for universities to improve the comprehensive quality of students and cultivate well-rounded talents.

Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label.

The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995.

Knowledge‐driven spatial competitive intelligence for tourism

AbstractCompetition among tourism enterprises is an ineluctable component of sustainable tourism growth, requiring comprehensive studies to understand its dynamic and develop appropriate strategies. The literature employs text mining or statistical analyses to identify correlations between tourism areas as competitive relationships. However, this approach may not be fully applicable, due to the sparsity of crucial coexistence phenomena, and may fail to investigate fine‐grained attractions' competition inside destination using large‐scale geospatial data. To overcome the limitations, this study proposes a knowledge‐driven competitive intelligence framework for tourism management, utilizing knowledge graph (KG) construction and inference technologies. First, multi‐mode heterogeneous tourism data are integrated into a unified KG, including tourist check‐in, online text, and basic geographic information. Second, the spatial‐dependent GNN‐based model absorbing abundant spatial semantic knowledge from tourism‐oriented KG can enhance the performance of competition reasoning. Third, with multiple analyses via symbolic queries on KG, a comprehensive panorama of competition situations can be revealed.

A Joint Model of Entity Linking for RFC Protocols Knowledge Graph Construction

Applying knowledge representation and reasoning to downstream tasks has been considered a promising research direction, as it enables semantic analysis of network protocols. Knowledge Graph is a new way of collecting knowledge, and building a protocol knowledge graph based on RFCs can help us study and analyze network protocols more effectively. However, automatically constructing a protocol knowledge graph from RFCs poses a major challenge, particularly in terms of extracting and linking protocol entities, due to the semi-structured nature of RFC documents. In this paper, we propose a model that combines a fine-tuned language model with an RFC Domain Model to link entities in RFCs to categories in the protocol knowledge base. Firstly, we design a protocol knowledge base as the schema for protocol entity linking. Secondly, we use heuristic methods to identify protocol entities and infer their descriptions from the nearby contexts of their header fields. Finally, we conduct comprehensive experiments on the RFC dataset using our joint model and baseline methods for protocol entity linking. Experimental results demonstrate that our model achieves state-of-the-art performance in entity linking on the RFC dataset, outperforming all baseline methods. In addition, we release a protocol knowledge graph, RFC-KG1.

Construction of Knowledge Graph for Emergency Resources

Knowledge graphs can effectively organize and represent information related to emergency resources for unforeseen sudden events. In this study, we construct a model layer for the knowledge graph of emergency resources, focused on sudden events, through the classification and analysis of unforeseen disaster measures. This study defines eight interconnected entity types, each characterised by a set of attributes and engaging in one or more relationships with other entity types. Utilizing 121 incident investigation reports from the emergency management departments of various provinces and cities over the past five years, we select five entities with the highest frequency of occurrence along with their corresponding four relationships. We then design an extraction plan for these entities and relationships. Based on the completed knowledge graph data, we formulate 14 questions related to emergency resources for sudden events and construct 19 corresponding question-and-answer templates using a template-based question-answering (QA) approach. We retrieve the corresponding Cypher statement templates through template mapping and obtain the question answers through querying. Finally, we design a knowledge graph question-and-answer system using the Django web framework, which includes entity queries and knowledge QA functions, specifically for emergency resources related to sudden events.