Entity Relation Research Articles

APIE: An information extraction module designed based on the pipeline method

Information extraction (IE) aims to discover and extract valuable information from unstructured text. This problem can be decomposed into two subtasks: named entity recognition (NER) and relation extraction (RE). Although the IE problem has been studied for years, most work efforts focused on jointly modeling these two subtasks, either by casting them into a structured prediction framework or by performing multitask learning through shared representations. However, since the contextual representations of entity and relation models inherently capture different feature information, sharing a single encoder to capture the information required by both subtasks in the same space would harm the accuracy of the model. Recent research (Zhong and Chen, 2020) has also proved that using two separate encoders for NER and RE tasks respectively through pipeline method are effective, with the model surpassing all previous joint models in accuracy. Thus, in this paper, we design An Pipeline method Information Extraction module called APIE, APIE combines the advantages of both pipeline methods and joint methods, demonstrating higher accuracy and powerful reasoning abilities. Specifically, we design a multi-level feature NER model based on attention mechanism and a document-level RE model based on local context pooling. To demonstrate the effectiveness of our proposed approach, we conducted tests on multiple datasets. Extensive experimental results have shown that our proposed model outperforms state-of-the-art methods and improves both accuracy and reasoning abilities.

Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders

The fault maintenance scenario in coal-mine equipment intelligence is composed of videos, images, signals, and repair process records. Text data are not the primary data that reflect the fault phenomenon, but rather the secondary processing based on operation experience. Focusing on the difficulty of extracting fault knowledge from the limited textual maintenance process records, a forward static full-connected topology network modeling method based on domain knowledge from four dimensions of physical structure, internal association, condition monitoring, and fault maintenance, is proposed to increase the efficiency of constructing a fault-maintenance knowledge graph. Accurately identifying the intrinsic correlation between the equipment anomalies and the faults’ causes through only domain knowledge and loosely coupled data is difficult. Based on the static full-connected knowledge graph of the cantilever roadheader, the information entropy and density-based DBSCAN clustering algorithm is used to process and analyze many condition-monitoring historical datasets to optimize the entity relationships between the fault phenomena and causes. The improved DBSCAN algorithm consists of three stages: firstly, extracting entity data related to fault information from the static fully connected graph; secondly, calculating the information entropy based on the real dataset describing the fault information and the historical operating condition, respectively; and thirdly, comparing the entropy values of the entities and analyzing the intrinsic relationship between the fault phenomenon, the operating condition data, and the fault causes. Based on the static full-connected topology storage in the Neo4j database, the information entropy and density-based DBSCAN algorithm is computed by using Python to identify the relationship weights and dynamically display optimized knowledge graph topology. Finally, an example of EBZ200-type cantilever roadheader for smart maintenance is studied to analyze and evaluate the forward and four-mainlines knowledge graph modeling method and the dynamic entity relations optimization method for static full-connected knowledge graph.

Joint Entity Relation Extraction Based on LSTM via Attention Mechanism

Joint Entity Relation Extraction Based on LSTM via Attention Mechanism

Economic Entrepreneurship Skills in High School Students as a Pathway to Future Economic Growth

The subject of this research focuses on the impact of Economic entrepreneurship education in Indonesian high schools. Exploratory qualitative case studies were conducted and the data collection techniques included semi structured interviews, focus group discussion and documentation review. The study results indicate that knowledge-explicitness discrepancy in GAME and EIE courses curb entrepreneurial skills amongst students. The first student related challenge is that existing entrepreneurship education is seen as not applicable to their future career goals and thus requires contextually relevant solutions. The roles, responsibilities and relations of entities in schooling systems are highly legalistic, bureaucratic and hierarchical and limit teachers from practicing creativity. Implication of the results of this study are drawn in terms of emphasizing the value of experiential learning, topicality of entrepreneurship content in high schools of Indonesia, need to have flexible policies to improve the quality of teaching and learning of entrepreneurship in the high schools of Indonesia. Suggestions are made to create what is necessary in schools to help prepare children for future economic development.

APT Attack Detection Based on Graph Convolutional Neural Networks

Advanced persistent threat (APT) attacks are malicious and targeted forms of cyberattacks that pose significant challenges to the information security of governments and enterprises. Traditional detection methods struggle to extract long-term relationships within these attacks effectively. This paper proposes an APT attack detection model based on graph convolutional neural networks (GCNs) to address this issue. The aim is to detect known attacks based on vulnerabilities and attack contexts. We extract organization-vulnerability relationships from publicly available APT threat intelligence, along with the names and relationships of software security entities from CVE, CWE, and CAPEC, to generate triple data and construct a knowledge graph of APT attack behaviors. This knowledge graph is transformed into a homogeneous graph, and GCNs are employed to process graph features, enabling effective APT attack detection. We evaluate the proposed method on the dataset constructed in this paper. The results show that the detection accuracy of the GCN method reaches 95.9%, improving by approximately 2.1% compared to the GraphSage method. This approach proves to be effective in real-world APT attack detection scenarios.

Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation.

Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports. For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format. Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evaluated the performance of the entire pipeline of our system. In addition, the ratio of annotated entities in the reports was measured to validate the coverage of the clinical information with our information model. The microaveraged F1-scores of our best-performing model for entity extraction and relation extraction were 96.1% and 97.4%, respectively. The microaveraged F1-score of the 2-stage system, which is a measure of the performance of the entire pipeline of our system, was 91.9%. Our system showed encouraging results for the conversion of free-text radiology reports into a structured format. The coverage of clinical information in the reports was 96.2% (6595/6853). Our 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.

A Contextual Dependency-Aware Graph Convolutional Network for extracting entity relations

Dependency trees reflect rich structural information, which can effectively guide models to understand text semantics and are widely used for relation extraction. However, existing dependency-based models suffer from noise in dependency trees, which distorts the modeling of contextual information, particularly when introducing dependency types, further exacerbating the propagation and accumulation of errors. In addition, rule-based pruning strategies used to eliminate noise in dependency trees may lose crucial information for capturing relational patterns, whereas unconstrained learning-based pruning strategies may introduce more noisy edges. These two aspects of dependency trees may cause models to produce semantic confusion, which is detrimental to relation extraction. In this study, we propose a Contextual Dependency-Aware Graph Convolutional Network (C-DAGCN) that works on complete dependency trees and perceives the importance of word dependencies from multiple angles without considering the noise in dependency trees. This study includes three parts. First, the sequence model captures semantic information for the input sentence and its words. We considered the appropriate outputs as word and sentence-level representations based on model characteristics. Second, the structure model enhances the description of the sentence structure and its guidance. We consider that position knowledge implies associations between words; therefore, we propose the Current Word Global Relative Position Knowledge (CW-GRPK) to globally enhance text structure modeling and reduce the models’ overreliance on single noisy structure guidance. A dependency-aware module weighs heuristic syntactic knowledge and non-heuristic CW-GRPK to capture fine-grained word-level interactions that effectively distinguish words related to relational semantics. Subsequently, a dependency-guided module, based on the results of the dependency-aware layer, encodes a knowledge-enhanced word graph without losing global linguistic patterns. Third, we constructed high-level linguistic representations based on the results of the sequence and structure model in C-DAGCN as a relational representation and mapped it to the relational decision space for relational prediction. The experimental results demonstrate that our method outperforms nine strong baseline models on two benchmark datasets. Compared with the best baseline Attentive Graph Convolutional Networks (A-GCN) model (Tian et al., 2021), our proposed C-DAGCN model with 90.15 macro-average F1 and 79.52 micro-average F1 achieves 0.28 macro-average F1 and 0.47 micro-average F1 improvement on the SemEval2010-Task8 and ACE2005 datasets, respectively. 11Our code is available at https://github.com/dymyyc/RE_C-DAGCN. The released code when run produces results which are the same as those reported in this paper.

CLRN: A reasoning network for multi-relation question answering over Cross-lingual Knowledge Graphs

Cross-lingual Knowledge Graphs-based Question Answering (CLKGQA) requires the question answering (QA) system to combine the knowledge graphs (KGs) in different languages to obtain answers to input questions. In previous works, the common idea is to merge Cross-lingual Knowledge Graphs (CLKGs) into a single KG through aligned entity pairs and then treat it as a traditional KG-based QA. However, as demonstrated by Tan et al. (2023), existing Entity Alignment (EA) models cannot generate highly accurate aligned entity pairs for CLKGs. Therefore, two issues need to be addressed in the CLKGQA task: (1) Remove the dependency of the QA model on the fused KG; (2) Improve the performance of the EA model in obtaining aligned entity pairs from locally isomorphic CLKGs. To solve the above two issues, this paper presents Cross-lingual Reasoning Network (CLRN), a novel multi-hop QA model that allows switching knowledge graphs at any stage of the multi-hop reasoning. Furthermore, we establish an iterative framework that combines CLRN and EA model, in which CLRN is used for extracting potential alignment triple pairs from CLKGs during the QA process. The extracted triple pairs provide pseudo-aligned entities, and the additional aligned entity pairs are used to mine missing relations between entities in CLKGs. These pseudo-aligned entity pairs and relations improve the performance of the EA model, resulting in higher accuracy in QA. Extensive experiments demonstrate the effectiveness of the proposed model, which outperforms the baseline approaches. Through iterative enhancement, the performance of the EA model has also been improved by > 1.0 % in [email protected] and [email protected], and the improvement is statistically significant in the confidence interval of p<0.01. Moreover, our work discusses the correlation between QA and EA from the side of QA, which has reference value for the follow-up exploration of related communities. We have open-sourced our dataset and code, which is available at the URL https://github.com/tan92hl/Cross-lingual-Reasoning-Network-for-CLKGQA.

Contextual Reinforcement, Entity Delimitation and Generative Data Augmentation for Entity Recognition and Relation Extraction in Official Documents

Transformer architectures have become the main component of various state-of-the-art methods for natural language processing tasks, such as Named Entity Recognition and Relation Extraction (NER+RE). As these architectures rely on semantic (contextual) aspects of word sequences, they may fail to accurately identify and delimit entity spans when there is little semantic context surrounding the named entities. This is the case of entities composed only by digits and punctuation, such as IDs and phone numbers, as well as long composed names. In this article, we propose new techniques for contextual reinforcement and entity delimitation based on pre- and post-processing techniques to provide a richer semantic context, improving SpERT, a state-of-the-art Span-based Entity and Relation Transformer. To provide further context to the training process of NER+RE, we propose a data augmentation technique based on Generative Pretrained Transformers (GPT). We evaluate our strategies using real data from public administration documents (official gazettes and biddings) and court lawsuits. Our results show that our pre- and post-processing strategies, when used co-jointly, allows significant improvements on NER+ER effectiveness, while we also show the benefits of using GPT for training data augmentation.

Community answer recommendation based on heterogeneous semantic fusion

The community question-answering system has gradually replaced the search engine as the primary way for people to acquire and share knowledge. Users' interactive behavior establishes complex relationships among multiple entities in the question-answering community. With the accumulation of question-and-answer data, retrieving relevant answers to a new question is time-consuming. Some researchers have conducted related research on answer recommendation, mainly dependent on word-based question–answer literal matching, ignoring text semantics and connected entity relationships. In this paper, we propose a Community Answer Recommendation Model Based on Heterogeneous Semantic Fusion, called CARHSF, which combines text semantic and multi-aspect features to enhance answer recommendations. Firstly, we employ the PCFG to build parse trees of text and extract hierarchical phrases through the iterative traversal method. Meanwhile, we utilize phrase embedding to efficiently quantify semantic relatedness. Secondly, we construct a heterogeneous information network to fuse text semantics and entity-relationship features. Finally, we represent entity relations through the heterogeneous graph neural network and supply the recommended answers based on entity correlations. Comparative experiments on three real datasets demonstrate that CARHSF has advantages in answer recommendation tasks.

A Combined Semantic Dependency and Lexical Embedding RoBERTa Model for Grid Field Relational Extraction

Relationship extraction is a crucial step in the construction of a knowledge graph. In this research, the grid field entity relationship extraction was performed via a labeling approach that used span representation. The subject entity and object entity were used as training instances to bolster the linkage between them. The embedding layer of the RoBERTa pre-training model included word embedding, position embedding, and paragraph embedding information. In addition, semantic dependency was introduced to establish an effective linkage between different entities. To facilitate the effective linkage, an additional lexically labeled embedment was introduced to empower the model to acquire more profound semantic insights. After obtaining the embedding layer, the RoBERTa model was used for multi-task learning of entities and relations. The multi-task information was then fused using the parameter hard sharing mechanism. Finally, after the layer was fully connected, the predicted entity relations were obtained. The approach was tested on a grid field dataset created for this study. The obtained results demonstrated that the proposed model has high performance.

Unleashing the power of context: Contextual association network with cross-task attention for joint relational extraction

Extraction of entities and their relationships remains a challenge in information extraction tasks such as opinion recognition and event detection. Research efforts has been dedicated to training named entity recognition (NER) models and relation extraction (RE) models simultaneously, in order to leverage the semantic associations among such sub-tasks to improve their performance. However, how to reasonably capture the interdependence between these two sub-tasks is still not clear. Well trained joint extraction models often do not generalize well to examples from wide sources due to that existing strategies may overly rely on shallow heuristics via entity mentions and fail to make enough use of contextual associations among such sub-tasks. With this in mind, we propose a novel contextual association network for joint relational extraction (called CARE) with cross-task attention mechanism, which makes use of the correlated contextual information produced by the NER and RE sub-tasks explicitly, and achieves significantly better performance comparing with the state-of-the-art models. We use experimental results on four publicly available datasets to test the efficacy of our proposed model. Experimental results demonstrate that the proposed mechanism can use the semantic information produced by NER and RE sub-tasks to boost one another in a complementary way.

Large-scale Monocular Depth Estimation in the Wild

Estimating the relative depth of a single image (Monocular Depth Estimation) is a significant step towards understanding the general structure of the depicted scenery, the relations of entities in the scene and their interactions. When faced with the task of estimating the depth of a scene without the use of Stereo images, we are dependent on the availability of large-scale depth datasets and high-capacity models to capture the intrinsic nature of depth. Unfortunately, creating large-scale datasets of depth images is not a trivial task. To overcome this limitation, In this work, a new approach is proposed to accumulate Depth & Surface Normal datasets from the world of different Video Games in an easy and reproducible way. This work also introduces a new loss function to better incorporate the relation between the Depth and the Surface Normal of a scene which results in higher quality depth estimations that also produce more uniform surface normals. Qualitative and quantitative comparisons are provided between the proposed method and the best approaches of the last nine years and have competitive performance to the State of the Art by having ≈24 times less parameters for the proposed method. To further prove the effectiveness of this approach, an Ablation Study is also provided. Experiments on this dataset shows that using this new loss function alongside synthetic datasets increases the accuracy of ”Monocular Depth Estimation in the Wild” tasks where other approaches usually fail to generalize.

Multiple dependence representation of attention graph convolutional network relation extraction model

AbstractDependency analysis can better help neural network to capture semantic features in sentences, so as to extract entity relation. Currently, hard pruning strategies and soft pruning strategies based on dependency tree structure coding have been proposed to balance beneficial additional information and adverse interference in extraction tasks. A new model based on graph convolutional networks, which uses a variety of representations describing dependency trees from different perspectives and combining these representations to obtain a better sentence representation for relation classification is proposed. A newly defined module is added, and this module uses the attention mechanism to capture deeper semantic features from the context representation as the global semantic features of the input text, thus helping the model to capture deeper semantic information at the sentence level for relational extraction tasks. In order to get more information about a given entity pair from the input sentence, the authors also model implicit co‐references (references) to entities. This model can extract semantic features related to the relationship between entities from sentences to the maximum extent. The results show that the model in this paper achieves good results on SemEval2010‐Task8 and KBP37 datasets.

Hierarchical relation and entity rotation-based multi-feature knowledge graph embedding

Knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph Embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model’s performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results.

CDTier: A Chinese Dataset of Threat Intelligence Entity Relationships

Cyber Threat Intelligence (CTI), which is knowledge of cyberspace threats gathered from security data, is critical in defending against cyberattacks.However, there is no open-source CTI dataset for security researchers to effectively apply enormous CTI information for security analysis in the field of threat intelligence, particularly in the field of Chinese threat intelligence. As a result, for network security research and development, this paper constructed a Chinese CTI entity relationship dataset–CDTier, which includes: 1) A threat entity extraction dataset composed of 100 CTI reports, 3744 threat sentences and 4259 threat knowledge objects; 2) A dataset for entity relation extraction including 100 CTI reports, 2598 threat sentences and 2562 knowledge object relations. CDTier is, as far as we know, the first CTI dataset. On the CDTier, we trained 4 models for threat entity extraction and relation extraction using well-established and widely used deep learning methods in the NLP. The results showed that the model trained on CDTier extracts knowledge objects and their relationships described in threat intelligence more accurately. This significantly minimizes threat intelligence analysts' work while assessing threat intelligence. The CDTier may be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MuYu-z/CDTier</uri> .

A contrastive learning framework for safety information extraction in construction

Typically named entity recognition (NER) and relation extraction (RE) from safety documentation (e.g., accident reports) adopt a pipeline processing approach whereby tasks are split into two sub-tasks. As a result, error propagation occurs between components, and useful information from one task may go unexploited by the other. Additionally, training sets to perform NER and RE from safety documentation are often limited and context-specific. Thus, our research addresses the following question: How can we accurately identify entities and extract relations from safety documentation using limited training sets? This paper utilizes ‘contrastive learning’ to tackle our research question. It proposes a contrastive learning-based cascade binary tagging framework (CasRel) to automatically and synchronously extract entities and relations from safety documents. A five-fold cross-validation process is used to validate the effectiveness and feasibility of our approach. Results from the validation process achieve an average precision of 77.8%, recall of 58.7%, and F1-score of 66.9%, outperforming CasRel with an increase of about 10% in precision, 5% in recall, and 7% in F1-score. Thus, our approach can accurately recognize entities and extract relations from safety documentation. The contributions of our study are twofold: (1) an improved unified model is developed to recognize and extract the entity and relation from safety documents to reduce error propagation and improve its accuracy; and (2) the concept of ‘contrastive learning’ is introduced in the design of the joint entity and relation extraction model with limited training sets.

Transformer-based approach to variable typing

The upsurge of multifarious endeavors across scientific fields propelled Big Data in the scientific domain. Despite the advancements in management systems, researchers find that mathematical knowledge remains one of the most challenging to manage due to the latter's inherent heterogeneity. One novel recourse being explored is variable typing where current works remain preliminary and, thus, provide a wide room for contribution. In this study, a primordial attempt to implement the end-to-end Entity Recognition (ER) and Relation Extraction (RE) approach to variable typing was made using the BERT (Bidirectional Encoder Representations from Transformers) model. A micro-dataset was developed for this process. According to our findings, the ER model and RE model, respectively, have Precision of 0.8142 and 0.4919, Recall of 0.7816 and 0.6030, and F1-Scores of 0.7975 and 0.5418. Despite the limited dataset, the models performed at par with values in the literature. This work also discusses the factors affecting this BERT-based approach, giving rise to suggestions for future implementations.

Tell me your position: Distantly supervised biomedical entity relation extraction using entity position marker

A significant amount of textual data has been produced in the biomedical area recently as a result of the advancement of biomedical technologies. Large-scale biomedical data can be automatically obtained with the help of distant supervision. However, the noisy data brought by distant supervision methods makes relation extraction tasks more difficult. Previous work has focused more on how to restore mislabeled relationships, but little attention has been paid to the importance of labeled entity locations for relationship extraction tasks. In this paper, we present a “four-stage” model based on BioBERT and Multi-Instance Learning by using entity position markers. Firstly, the sentence is marked with position. Secondly, BioBERT, a biomedical pre-trained language model, is used in the final sentence feature vector representation not only with the global position marker but also with the start and end marker of both the head and tail entity. Thirdly, the aggregation of sentence vectors in the bag is used as the vector feature of the bag by three aggregation methods, and the performance of different sentence feature vectors combined with different bag encoding methods is discussed. At last, relation classification is performed at the bag level. According to experimental results, the presented model significantly outperforms all baseline models and contributes to noise reduction. In addition, different bag encoding methods need to match corresponding sentence encoding representation to achieve the best performance.

Entity–relation triple extraction based on relation sequence information

Data overlap is a significant challenge in the task of entity–relation triple extraction. This task includes two research lines, line one first identifies entities and then predicts relations while line two completely shuffles the order. The methods in line two are more conducive to the optimization of the data overlap problem. Recent works have made breakthroughs in dealing with overlapping data, but there are still some defects such as difficulty in convergence and poor performance on datasets with numerous relations. To solve the above problems, we adopt a two-step strategy of first extracting subjects, and then predicting relation–object pairs. Considering the absence of connectivity between the two steps in the conventional method, we adopt the relation sequence as input in both steps and propose the TERS model. The relation sequence can connect two steps and improve the single-step and comprehensive extraction capability of the model. The TERS model consists of two modules. The first module performs information interaction and filters invalid subjects through the Text Relation Attention method. The second module implements multiple iterations of information interaction through the Information Flow method. The combination of these two modules contributes to the strong entity–relation triple extraction capability of our model. We evaluate our method on three public datasets. Extensive experiments show that our TERS model outperforms previous state-of-the-art models in triple extraction and overlapping data processing. Compared with other two-step extraction models, the advantages of our model are more obvious.