Domain Entities Research Articles

MBJELEL: An End-to-End Knowledge Graph Entity Linking Method Applied to Civil Aviation Emergencies

Aviation emergency management is playing a more and more important role in the aviation field. How to make effective use of massive heterogeneous and multi-source aviation accident knowledge has become a great challenge for aviation emergency management. Aiming at the problems such as too long physical length, mixed and composite entities, similar character of domain entity names, information difference between entities, separation of codes between entities, coding errors during transmission, etc., the construction method of knowledge map of civil aviation emergencies is studied. In previous research methods, entity link is always divided into two parts, that is, first detection and then disambiguation, which makes the mentioned entity and the candidate entity are encoded separately, and there is error transmission between the two parts, modules cannot communicate with each other, and the close association between entities cannot be well learned. In this paper, we proposed an end-to-end entity linking method based on two-layer BiLSTM model joint coding vectorize each word of civil aviation text information, and then concatenate feature vectors into two-layer BiLSTM model to obtain high-level context representation. Because the joint encoding of boundary information can reduce the error transmission, information is exchanged between candidate entities during the initial encoding to enhance the closeness between candidate entities and candidate entities. The experimental results show that compared with other sota models, the F1 value of the proposed model reaches 88.97%.

How to classify domain entities into top-level ontology concepts using large language models

How to classify domain entities into top-level ontology concepts using large language models

An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus

Mining entities from open Chinese mechanical equipment texts have become prevailing in the intelligent manufacturing field. However, compared to named entities in other domains, it is very hard to determine entity boundaries in mining Chinese mechanical equipment texts because there is no unified standard for entity simplification, and digits and units are mixed in the text. To address the issue, this paper presents an entity boundary-define strategy and constructs a mechanical equipment-oriented corpus called MECorpus using open Chinese mechanical equipment texts by combining domain knowledge. A multi-neural network collaboration model Adaptive-BERT-BiLSTM-CRF-Rating (ABBCR) is then proposed for mechanical equipment named entity recognition. The novelty of ABBCR is characterised by its adaptive input mechanism and rating score ability for identified entities. Various experiments about ABBCR model selection, evaluation and application are conducted on MECorpus. Experimental results show that the ABBCR model provides high-quality mechanical equipment entities for constructing the mechanical equipment knowledge graph. ABBCR combined with the large language model has proved to be a promising method to manage complex mechanical equipment expertise.

Formation of the Idea of the Library as an Institution in 18th-Century Europe. A Qualitative and Quantitative Approach

The paper illustrates the LIBMOVIT project - - Libraries on the Move: Scholars, Books, Ideas Traveling in Italy in the 18th Century - whose main research focus is the European Eighteenth century socio-cultural framework in which the library as an institution acquired an historical, social, public and dynamic dimension. This context will be analysed through a study of the Eighteenth century sources connected to the learned journey experience of the Grand Tour, in particular those contained in the Angiolo Tursi collection - one of the largest travel literature collections in Italy - held at the Marciana national library in Venice. The paper presents the planned approach of the research: first, a classification and an organization of a corpus of relevant documents for the knowledge of travel literature in connection to the libraries world will be created; in particular, the sources will be identified, further bibliographical information will be added, and new sources will be integrated to the corpus and selected documents will be digitized. After that, the research will proceed through a double analysis – traditional and computational – of the texts collected in the corpus is to be developed. First, all the library and bibliographical aspects described by travellers will be studied according to the traditional approach in humanities research to collect important information about the history of libraries (location, decoration, catalogues, opening hours, access, collections, cited books and documents), the travellers and their companions (professions, nationality, reason to travel), the people met (scholars, librarians, superintendents) and the subjects and ideas discussed during the visits in the libraries. Second, the texts will be computationally analysed through several Natural Language Processing (NLP) techniques, starting from the automatic text recognition until arriving to more complex lexical and terminological analysis and Named Entity Recognition (NER). This work is meant to support the previously described qualitative study and will also allow to produce Linked open data about the domain entities (e.g. libraries, people, books) in view of their publication in the semantic web in order to ease and promote their exploration, visualisation and reuse.

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification

Few-shot named entity recognition (NER) aims to recognize novel named entities in low-resource domains utilizing existing knowledge. However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversarial attacks in Few-shot NER. In this work, we comprehensively explore and assess the robustness of few-shot NER models under textual adversarial attack scenario, and found the vulnerability of existing few-shot NER models. Furthermore, we propose a robust two-stage few-shot NER method with Boundary Discrimination and Correlation Purification (BDCP). Specifically, in the span detection stage, the entity boundary discriminative module is introduced to provide a highly distinguishing boundary representation space to detect entity spans. In the entity typing stage, the correlations between entities and contexts are purified by minimizing the interference information and facilitating correlation generalization to alleviate the perturbations caused by textual adversarial attacks. In addition, we construct adversarial examples for few-shot NER based on public datasets Few-NERD and Cross-Dataset. Comprehensive evaluations on those two groups of few-shot NER datasets containing adversarial examples demonstrate the robustness and superiority of the proposed method.

Named entity recognition in aerospace based on multi-feature fusion transformer

In recent years, along with the rapid development in the domain of artificial intelligence and aerospace, aerospace combined with artificial intelligence is the future trend. As an important basic tool for Natural Language Processing, Named Entity Recognition technology can help obtain key relevant knowledge from a large number of aerospace data. In this paper, we produced an aerospace domain entity recognition dataset containing 30 k sentences in Chinese and developed a named entity recognition model that is Multi-Feature Fusion Transformer (MFT), which combines features such as words and radicals to enhance the semantic information of the sentences. In our model, the double Feed-forward Neural Network is exploited as well to ensure MFT better performance. We use our aerospace dataset to train MFT. The experimental results show that MFT has great entity recognition performance, and the F1 score on aerospace dataset is 86.10%.

Multi-modal semantics fusion model for domain relation extraction via information bottleneck

Domain relation extraction (DRE) aims to understand domain semantics of unstructured text to mine structural knowledge. However, most existing DRE models may fail to fully represent the domain entity that is usually absent in the corpus of pre-trained language models. In addition, almost all these models directly use contextual semantics for relation classification that ignore purifying the task relevant features. To address these challenging problems, we propose a novel and effective Multi-Modal Semantics Fusion (MMSF) model, which can automatically capture domain semantics to discover the important semantics contained in images, thus enhancing the representations of the domain entity by exploiting the multi-modal semantics. Afterwards, we introduce heterogeneous graph neural networks to learn contextual semantics. To obtain the task relevant features for DRE, we design a new purified feature architecture by exploring the mutual information between contextual semantics and the labels of domain relation. With the enhanced representations of the domain entity and purified the task relevant features, the satisfactory results of relation classification can be guaranteed. Extensive experiments on two real domain relation extraction datasets demonstrate the superiority of our proposed model over several state-of-the-art models. The source code and experiment details of this paper can be obtained from https://github.com/SWT-AITeam/MMSF.

SMAAMA: A named entity alignment method based on Siamese network character feature and multi-attribute importance feature for Chinese civil aviation

With the growth of demand for air-space integrated transportation industry, the civil aviation transportation industry plays an increasingly important role in the national economy and transportation. The diversity and diversified development of civil aviation emergencies have greatly affected the decision-making efficiency for emergencies in civil aviation emergency management systems. Constructing a knowledge graph of civil aviation and improving the reliability and richness of the knowledge graph has become an urgent problem to be solved. There are some problems during the construction of a civil aviation domain knowledge graph such as long entity length, existing hybrid and composite entities, the similarity of the font features of domain entity names, a difference of information between entities, separated coding between entities, and error-prone in the transmission process of coding. So we research the construction method of knowledge graph for civil aviation emergencies to resolve these problems. (1) We propose a font pre-training model and incorporate a character feature layer into the embedding layer. (2) We propose a multi-attribute attention alignment method based on the Siamese network; entities with similar structures in the civil aviation knowledge base are input into the font layer for pre-training; the complete semantic information of entities and the importance of different attributes to entities are learned through two sub-networks respectively to improve the integrity of civil aviation knowledge graph. Experiments were carried out on the self-built aviation emergency and public data sets, respectively. The experimental results show that compared with other models, the F1 value of the proposed model can reach 96.97%, which verifies the feasibility of the model.

Improving Low-Resource Chinese Named Entity Recognition Using Bidirectional Encoder Representation from Transformers and Lexicon Adapter

Due to their individual advantages, the integration of lexicon information and pre-trained models like BERT has been widely adopted in Chinese sequence labeling tasks. However, given their high demand for training data, efforts have been made to enhance their performance in low-resource scenarios. Currently, certain specialized domains, such as agriculture, the industrial sector, and the metallurgical industry, suffer from a scarcity of data. Consequently, there is a dearth of effective models for entity relationship recognition when faced with limited data availability. Inspired by this, we constructed a suitable small balanced dataset and proposed a based-domain-NER model. Firstly, we construct a domain-specific dictionary based on mine hoist equipment and fault text and generate a dictionary tree to obtain word vector information. Secondly, we use a Lexicon Adapter to obtain the vector information of the domain-specific dictionary feature words matched using the characters and calculate the weights between their word vectors, integrating position encoding to enhance the positional information of the word vectors. Finally, we incorporate word vector information into the feature extraction layer to enhance the boundary information of domain entities and mitigate the semantic loss problem caused via using only character feature representation. Experimental results on a manually annotated dataset of mine hoist fault texts show that this method outperforms BiLSTM, BiLSTM-CRF, BERT, BERT-BiLSTM-CRF, and LEBERT, effectively improving the accuracy of named entity recognition (NER) for mine hoist faults.

Data Trusted Sharing Delivery: A Blockchain-Assisted Software-Defined Content Delivery Network

The 6G wireless network aims to forge new spectrum, high technical standards of high time and phase synchronization accuracy, and 100% geographic coverage to connect trillions of devices flexibly and efficiently in the future. However, as connectivity increases and applications become novel, it is a challenge to ensure the privacy and security of networks and applications. Blockchain is seen as a promising technology that can improve efficiency, reduce costs, mitigate security and privacy threats, and establish a trusted data-sharing environment. This paper presents a trusted framework based on block chain technology from the perspective of how to build a trusted software-defined content delivery network. As the peer node of blockchain, SDN (Software Defined Network) controller establishes trust between different regions and a wide range of participants, realizing peer autonomy and flexible business orchestration. The two main purposes of the architecture are to enhance the security of network communications and establish trust relationships between entities in different domains. It includes trusted communication based on routing sandbox, service choreography based on block chain, proxy server selection strategy based on MPC (model predictive control) and optimization consensus based on PBFT. Some simulation experiments verify the effectiveness of the theoretical method.

Urban Knowledge Graph Aided Mobile User Profiling

Nowadays, the explosive growth of personalized web applications and the rapid development of artificial intelligence technology have flourished the recent research on mobile user profiling, i.e., inferring the user profile from mobile behavioral data. Particularly, existing studies mainly follow the data-driven paradigm to develop feature engineering and representation learning on such data, which however suffer from the robustness issue, i.e., generalizing poorly across datasets and profiles without considering semantic knowledge therein. In comparison, the rising knowledge-driven paradigm built upon the knowledge graph (KG) offers a potential solution to mitigate such weakness. Therefore, in this paper, we propose a Knowledge Graph aided framework for Mobile User Profiling (KG-MUP). Specifically, to distil semantic knowledge among data, we firstly construct an urban knowledge graph (UrbanKG) with domain entities like users, regions, point of interests (POIs), etc. identified, as well as semantic relations for home, workplace, spatiality, etc. extracted. Moreover, we leverage tensor decomposition and graph neural network to obtain knowledgeable user representations from UrbanKG. In addition, we introduce several customized features to quantify individual mobility characteristics for mobile user profiling. Extensive experiments on three real-world mobility datasets demonstrate that KG-MUP achieves state-of-the-art performance on user profile inference tasks. Moreover, further results also reveal the importance of various semantic knowledge to user profile inference, which provides meaningful insights on user modeling with mobile behavioral data.

MODELING AND IMPLEMENTATION OF DOMAIN EVENTS IN THE DOMAIN-DRIVEN ARCHITECTURE ON THE .NET CORE PLATFORM

A software architecture centered on a domain model can provide significant advantages over other types of architectures in the long-term development and maintenance of systems with complex domain logic. At the same time, domain-driven design and approaches to software implementation of systems are relatively new concepts and continue to develop in application to various platforms, technologies and programming languages, and are of considerable interest to designers and developers. The presented work examines existing approaches to modeling and implementing domain events on the .NET Core platform in domain-driven architecture, which is one of the newest patterns. There are two approaches to implementing domain event behavior: immediate and delayed event propagation. These two approaches are analyzed and their features are described in detail. The implementation of instant propagation of domain events within a command execution transaction in the CQRS architecture is described. This implementation allows you to get rid of external dependencies, achieve purity of domain entities, as it eliminates the need to inject services, repositories in entities, and also prevents memory leaks and is safe for multithreaded use. Schematically depicts the abstract process of an external command entering a domain model, which causes a change in the state of an aggregate and the propagation of side effects with domain events. This process takes into account the capabilities of the Entity Framework object-relational mapping framework to retrieve context objects that have been changed during process. The entire stack of objects involved in this activity is located in the shared process memory, and the interaction occurs in synchronous mode. For the conceptual detection of events and aggregates, the event storming technique is used, the features of which are discussed in the article.

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems.

This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning

Current research on knowledge graph construction is focused chiefly on general-purpose fields, whereas constructing knowledge graphs in vertically segmented professional fields faces numerous difficulties. To solve the problems of complex relation types of domain entities, the lack of a large amount of annotated corpus, and the difficulty of extraction, this study proposed a method for constructing domain-annotated datasets based on publicly available texts on the web, which integrates remote supervision and semi-supervision. For the relational triad extraction of a given core entity (an entity lexicon defined semi-automatically by experts), an inflated gate attention network structure for increasing the perceptual field of the model is proposed. In addition, a relational extraction model, Ro-DGANet, was designed based on this structure, incorporating the idea of a probability graph. The Ro-DGANet model was experimentally evaluated on the publicly available Chinese datasets LIC2019 and CHIP2020 and compared with the mainstream relation extraction models, achieving the best results with F1 values of 82.99% and 66.39%, respectively. Finally, the Ro-DGANet model was applied to the relation extraction task of equipment components in industrial scenarios and to the relation extraction task of core knowledge points of programming languages. The analysis results show that the proposed method is applicable to open relation extraction among core entities in different domains with reliable performance and portability.

A Chinese Named Entity Recognition Method Based on ERNIE-BiLSTM-CRF for Food Safety Domain

Food safety is closely related to human health. Therefore, named entity recognition technology is used to extract named entities related to food safety, and building a regulatory knowledge graph in the field of food safety can help relevant authorities to regulate food safety issues and mitigate the hazards caused by food safety problems. However, there is no publicly available named entity recognition dataset in the food safety domain. In contrast, the non-standardized Chinese short texts generated from user comments on the web contain rich implicit information that can help identify named entities in specific domains (e.g., food safety domain) where the corpus is scarce. Therefore, in this paper, named entities related to food safety are extracted from these unstandardized texts on the web. However, the existing Chinese named entity identification methods are mainly for standardized texts. Meanwhile, these unstandardized texts have the following problems: (1) their corpus size is small; (2) there are various new and wrong words and noise; (3) and they do not follow strict syntactic rules. These problems make the recognition of Chinese named entities for online texts more challenging. Therefore, this paper proposes the ERNIE-Adv-BiLSTM-Att-CRF model to improve the recognition of food safety domain entities in unstandardized texts. Specifically, adversarial training is added to the model training as a regularization method to alleviate the influence of noise on the model, while self-attention is added to the BiLSTM-CRF model to capture features that significant impact entity classification and improve the accuracy of entity classification. This paper conducts experiments on the public dataset Weibo NER and the self-built food domain dataset Food. The experimental results show that our model achieves a SOTA performance of 72.64% and a good performance of 69.68% for F1 values on the public and self-built datasets, respectively. The validity and reasonableness of our model are verified. In addition, the paper further analyses the impact of various components and settings on the model. The study has practical implications in the field of food safety.

Using terms and informal definitions to classify domain entities into top-level ontology concepts: An approach based on language models

The classification of domain entities into top-level ontology concepts remains an activity performed manually by an ontology engineer. Although some works focus on automating this task by applying machine-learning approaches using textual sentences as input, they require the existence of the domain entities in external knowledge resources, such as pre-trained embedding models. In this context, this work proposes an approach that combines the term representing the domain entity and its informal definition into a single text sentence without requiring external knowledge resources. Thus, we use this sentence as the input of a deep neural network that contains a language model as a layer. Also, we present a methodology used to extract two novel datasets from the OntoWordNet ontology based on Dolce-Lite and Dolce-Lite-Plus top-level ontologies. Our experiments show that by using the transformer-based language models, we achieve promising results in classifying domain entities into 82 top-level ontology concepts, with 94% regarding micro F1-score.

Exploring named entity recognition and relation extraction for ontology and medical records integration

The available natural language data in electronic health records is of noteworthy interest to health research and development. Nevertheless, their manual analysis is not feasible and poses a challenge to accessing valuable information in these records. This paper presents an approach to automatically extract information from these unstructured medical records using Domain Entity Recognition and Relation Extraction, structuring the results through a domain ontology. We developed our work in the oncology domain, an attention-demanding field. The main contribution of this work lies in integrating multiple resources in a complete methodology to accomplish this task. We developed a new entity and relation annotated dataset of medical evolutions in Brazilian Portuguese, containing 1622 documents, 146,769 entities, and 111,716 relations. We attained 78.24 % accuracy for entity and relation extraction in the exams domain. Healthcare specialists evaluated the approach regarding entity recognition and relation extraction positively and considered the methodology valuable to health professionals.

Retracted: Research of Vertical Domain Entity Linking Method Fusing Bert‐Binary

Retracted: Research of Vertical Domain Entity Linking Method Fusing Bert‐Binary

A unified semantic description and user’s permissions knowledge reasoning for multiple domain cyberspace based on knowledge graph

Because multiple domain cyberspace joint attacks are becoming more widespread, establishing a multiple domain cyberspace defensive paradigm is becoming more vital. However, although some physical domain and social domain information is incorporated in present approaches, total modeling of cyberspace is absent, therefore thorough modeling of cyberspace is becoming increasingly necessary. This paper proposed a knowledge graph based multiple domain cyberspace modeling approach. A knowledge graph of multiple domain cyberspace is produced by extracting multiple domain entity information and entity relations such as physical domain, social domain, network domain, and information domain, so that semantic information of multiple domain cyberspace may be described consistently. At the same time, this paper proposed a user’s permissions reasoning method based on multiple domain cyberspace knowledge graph to address the user’s permissions reasoning that relies on artificial reasoning principles. Through the model learning knowledge graph triples characteristics and rules, and implementing automatic reasoning of user’s permissions, this proposed method can abandon the artificial model of writing reasoning rules, allowing the machine to learn the reasoning rules using machine learning and other methods. Experimental results showed that the proposed method can learn relevant reasoning rules and accomplish automated reasoning of user’s permissions, and that the method’s accuracy and recall rates are higher than those of path ranking and translating embeddings.