Types Of Entities Research Articles

Note the hierarchy: Taxonomy-guided prototype for few-shot named entity recognition

Few-shot named entity recognition (FS-NER) aims at identifying entities from plain text via a handful of samples. Many efforts based on prototype learning hardly model the hierarchy in the entity types from global perspectives, which tends to cause ambiguous prototypes. To tackle the challenge and make full use of fine-grained taxonomy information, we propose a novel approach called Taxonomy-Guided Prototype (TaGuProto) for FS-NER, a decomposed framework which can automatically extract class-agnostic entity spans, explicitly incorporate taxonomic hierarchy information into prototype representations and effectively bridge the representation gaps between entity spans and taxonomic description embeddings. In addition, the taxonomy knowledge provides instructive supervision signals for discrepant and discriminative entity representations. Extensive experimental results on six public datasets demonstrate that our model achieves significant and consistent improvement, with TaGuProto achieving an average performance gain of 8.7% over the previous best model (SDNet).

The Net Zero Emissions Decision Model of the Sustainable Path of Chinese Business Parks

Business parks account for 30% of China’s total carbon emissions. Exploring emissions reduction approaches for business parks is crucial to achieve a net-zero emissions target, as well as for achieving a representative example for all types of emissions entities. Business parks mainly adopt two types of emissions reduction approaches: energy-saving renovations and purchasing carbon reduction products. However, there are limited studies focusing on the optimal combinations of the two approaches for reaching net-zero emissions and evaluating the cost effectiveness. To find a feasible and quantified way to build net-zero business park, a comprehensive path decision model is proposed. The problem is broken down into two parts: the optimal carbon reduction portfolio and the optimal electricity saving were researched. For the optimal product portfolio, the Markowitz theory is employed to balance the risk of carbon reduction products with the expected cost. In the part of optimal electricity saving, considering a ten-year life cycle, the total cost includes renovation investment, carbon reduction products cost, and cost saving of electricity consumption reduction. Based on the energy consumption, technical, and price data, the combination of energy-saving renovations and carbon reduction products is optimized. The model suggests a business park can save 24% of energy consumption through renovation investment and purchase CCER as 66% of the carbon reduction product portfolio. Taking only purchasing carbon reduction products as a benchmark to assess economic efficiency, implementing an optimized level of energy-saving renovation is found to save 16% of the comprehensive cost for the life cycle required to achieve zero carbon emissions. This model provides a new comprehensive optimization idea that will help future parks make decisions to achieve zero-carbon emission targets.

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 3).

SkiQL: A unified schema query language

Most NoSQL systems are schema-on-read: data can be stored without first having to declare a schema that imposes a structure. This schemaless feature offers flexibility to evolve data-intensive applications when data change frequently. However, freeing from declaring schemas does not mean their absence, but rather that they are implicit in data and code. Therefore, diagramming tools similar to those available for relational systems are also needed to help developers and administrators to design and to understand NoSQL schemas.Visualizing diagrams is not practical if schemas contain hundreds of database entities, so exploration or query facilities are then needed. In schemaless NoSQL stores, data of the same entity can be stored with different structure (e.g., non-uniform types and optional fields), which can increase the difficulty of having readable diagrams.NoSQL schema management tools should therefore have three main components: schema extraction, schema visualization, and schema query. As there are four main NoSQL data models, it is convenient for such tools to be built on a generic data model so that they provide platform-independence (of data models and data stores) to query and visualize schemas. With the aim of favoring the creation of generic database tools, the authors of this paper defined the U-Schema unified data model that integrates the four main NoSQL data models as well as the relational model.This paper is focused on querying NoSQL and relational schemas which are represented as U-Schema models. We present the SkiQL language designed on U-Schema to achieve a platform-independent schema query service. SkiQL provides two constructs: schema-query and relationship-query. The former allows to obtain information of entity or relationship types, and the latter that of the aggregations or references (relations among types). We will show how SkiQL was evaluated by calculating well-known metrics for languages as well as using a survey with developers with experience in NoSQL.

Structural characterisation of bioactive compounds of Gymnosporia senegalensis (Lam.) Loes. using advanced analytical technique like FT-IR, GC-MS and 1H-NMR spectroscopy

The present investigation was carried out to characterise bioactive components from G. senegalensis by using Fourier-transform infra-red (FT-IR) spectroscopy, 1H-nuclear magnetic resonance spectroscopy, and gas chromatography-mass spectrometry (GC-MS). The FTIR analysis confirmed the presence of > CH2, -CH3, C = C-C, C-H, C-F, C = C, -C = N-, C-C = N-, and -OH functional groups. The 1H-NMR spectrum revealed the presence of structures of four bioactive compounds i.e. tetratetracontana derivative, β-carotene, amyrin, and terpineol. GC-MS revealed the presence of different types of high and low molecular weight chemical entities with varying quantities including volatile and essential oil, monoterpenoid, tetraterpenoid, carotenoid, terpenoid, triterpenes, and nortriterpenes. From the results, it could be concluded that G. senegalensis contains various bioactive compounds of biological and pharmacological importance. Overall, this study will provide insight into the characterisation and development of drugs from medicinal plants.

Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews

BackgroundEvidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction.MethodsWe evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies.Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models. Using human annotation as a reference, we assessed the ability of the models to identify biomedical terms of interest (entities) and their relations. We also pretrained BERT on a corpus of 100,000 open access clinical publications and/or enhanced context-dependent entity classification with a conditional random field (CRF) model.Performance was measured using the F1 score, a metric that combines precision and recall. We defined successful matches as partial overlap of entities of the same type.ResultsFor entity recognition, the pretrained BERT+CRF model had the best performance, with an F1 score of 73% in SLR 1 and 70% in SLR 2. Entity types identified with the highest accuracy were metrics for progression-free survival (SLR 1, F1 score 88%) or for patient age (SLR 2, F1 score 82%). Treatment arm dosage was identified less successfully (F1 scores 60% [SLR 1] and 49% [SLR 2]). The best-performing model for relation extraction, pretrained BERT relation classification, exhibited F1 scores higher than 90% in cases with at least 80 relation examples for a pair of related entity types.ConclusionsThe performance of BERT is enhanced by pretraining with biomedical literature and by combining with a CRF model. With refinement, machine learning may assist with manual data extraction for SLRs.

Formulating the potentials of clustering of event data over multiple entities for decision support: a network embeddings approach

ABSTRACT Event data from business processes evidence their patterns, behaviors, and dysfunctions. Analytics techniques like clustering and sorting can reveal relevant insights, when data are correlated with a single case identifier. However, when multiple entities are involved, unidimensional models are challenged. We introduce a novel method for analyzing business processes involving multiple interacting entity types. Our approach employs embedding representations to capture pairwise similarities among entity types and their interrelationships. An optimization problem encompasses similarity matrices, cross-entity relationship matrices, and embeddings. An iterative algorithm refines this model, yielding embedding representations and cluster assignments for each entity type. Formulating our method across three diverse business scenarios demonstrates its practicality and potential. Our results, through a proof of concept using real-world data, underscore the value of accounting for the multifaceted nature of business processes, showing substantial improvements and qualitative distinctions compared to unidimensional models.

SANCTIONS EVASION AND VIRTUAL ASSETS: IMPLICATIONS FOR NATIONAL SECURITY

2022 was the year marking a significant increase in the use of virtual assets for illicit activities such as sanctions evasion. Most of the entities conducting these activities are linked to Russia, North Korea and Iran, which are subject to international sanctions imposed by the international community. The paper presents key elements about the use of virtual assets in illicit activities by sanctioned entities and highlights the necessity to increase defence and intelligence resources for better data analysis on this type of entities. Analyzing data about virtual assets transactions requires strong collaboration between public and private organizations, with a focus on an intelligence-led approach, considering the growing links between cybercrime, money laundering, terrorist financing, special operations conducted by adversaries. In order to support this collaboration, it is essential to prioritize the education of decision-makers on the necessity to focus on technical data.

Biomedical named entity recognition using TCN approaches and bio tagging

<p>Biomedical named entity recognition (BNER) is to identify instances in biomedical field such as chemical compounds, drugs, genes, RNA, DNA and proteins used in extracting information. It extracts relation between various drugs and their usage, profiles of similar and related drugs with help of machine learning approach. The efficiency in biomedical field is still in research for further improvement even many supervised methods are applied. The proposed method combines two algorithms and improve performance based on features used. It uses conditional random field (CRF) for entity identification and classification of temporal conventional network (TCN) to detect and recognize subtypes in BNER. Datasets such as GENIA and CHEMDNER corpus are used for evaluation with different entity types. Results shows that proposed methods performed better compared to other machine learning approach. The detailed study of TCN has been discussed. The classification of BNER is mapped with various classification methods to enhance result of high recognition.</p>

PDB-BRE: A ligand-protein interaction binding residue extractor based on Protein Data Bank.

Proteins typically exert their biological functions by interacting with other biomolecules or ligands. The study of ligand-protein interactions is crucial in elucidating the biological mechanisms of proteins. Most existing studies have focused on analyzing ligand-protein interactions, and they ignore the additional situational of inserted and modified residues. Besides, the resources often support only a single ligand type and cannot obtain satisfied results in analyzing novel complexes. Therefore, it is important to develop a general analytical tool to extract the binding residues of ligand-protein interactions in complexes fully. In this study, we propose a ligand-protein interaction binding residue extractor (PDB-BRE), which can be used to automatically extract interacting ligand or protein-binding residues from complex three-dimensional (3D) structures based on the RCSB Protein Data Bank (RCSB PDB). PDB-BRE offers a notable advantage in its comprehensive support for analyzing six distinct types of ligands, including proteins, peptides, DNA, RNA, mixed DNA and RNA entities, and non-polymeric entities. Moreover, it takes into account the consideration of inserted and modified residues within complexes. Compared to other state-of-the-art methods, PDB-BRE is more suitable for massively parallel batch analysis, and can be directly applied for downstream tasks, such as predicting binding residues of novel complexes. PDB-BRE is freely available at http://bliulab.net/PDB-BRE.

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition.

In the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain. To address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest. Experimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models.

Innovation in hotel enterprises during the Covid-19 pandemic and the Ukraine conflict. Poland: case study

Entrepreneurship is one of the most important components of the modern market economy. Important features of entrepreneurship include expansiveness, innovation and the ability for companies to adapt to changing environmental conditions. These elements are also noticeable in the case of hotel companies, which are among the oldest types of business entities operating in the world. Innovation in hospitality contributes to the improvement of competitive market positioning, as well as effects on the interest of customers. The aim of the publication is to discuss current examples of innovative activities of hotel companies. The article takes into account the significant impact of the Covid-19 pandemic and the war in Ukraine on innovations within these enterprises. For the purpose of achieving the objectives of the publication, the Polish hotel market was taken into account. The subject of the publication concerns an important issue of innovation in the hotel industry. The hotel sector has been particularly affected by unexpected changes in the economic environment. However, despite the difficult situation and uncertain future, the Polish hospitality still provides the examples of innovative activities worth discussing. The motives for choosing the subject of the publication are also the author's personal scientific interests, as well as a limited number of the references on this subject, especially from the most recent period. The paper describes and discusses the concept of innovation, especially in the hotel sector. The examples of the innovative activities in the Polish and foreign hotel companies are described. The most important achievements and successes in this field are presented. The article poses the following research thesis: The Covid-19 pandemic and the war crisis have not significantly affected innovation in the hotel companies in Poland. The article was created using book materials, journals, and netographic information. The research method used in the publication is the analysis of the existing data and literary criticism. The results of the research positively verify the thesis.

Fine-grained cybersecurity entity typing based on multimodal representation learning

Fine-grained cybersecurity entity typing based on multimodal representation learning

Research on named entity recognition of Traditional Chinese Medicine chest discomfort cases incorporating domain vocabulary features

Objective:To promote research on knowledge extraction and knowledge graph construction of chest discomfort medical cases in Traditional Chinese Medicine (TCM), this paper focuses on their named entity recognition (NER). The recognition task includes six entity types: “syndrome”, “symptom”, “etiology and pathogenesis”, “treatment method”, “medication”, and “prescription”. Methods:We annotated data in a BIO (B-begin, I-inside, O-outside) manner. For the characteristics of medical case texts, we proposed a custom dictionary method that can be dynamically updated for word segmentation. To compare the effect of the method on the experimental results, we applied the method in the BiLSTM-CRF model and IDCNN-CRF model, respectively. Results:The models using custom dictionaries (BiLSTM-CRF-Loaded and IDCNN-CRF-Loaded) outperformed the models without custom dictionaries (BiLSTM-CRF and IDCNN-CRF) in accuracy, precision, recall, and F1 score. The BiLSTM-CRF-Loaded model yielded F1 scores of 92.59% and 93.23% on the test set and validation set, respectively, surpassing the BERT-BiLSTM-CRF model by 3.59% and 4.87%. Furthermore, when analyzing the results for the six entity categories separately, we found that the use of custom dictionaries has a marked impact, with the categories of “etiology and pathogenesis” and “syndrome” demonstrating the most noticeable improvements. By comparing the F1 scores, we observed that the entity category “medication” yielded the highest performance, reaching F1 scores of 96.04% and 96.48% on the test set and validation set, respectively. Conclusion:We propose a word segmentation method based on a dynamically updated custom dictionary. The method is combined with the BILSTM-CRF and the IDCNN-CRF models, which enhances the model to recognize domain-specific terms and new entities. It can be widely applied in dealing with complex text structures and texts containing domain-specific terms.

Multisource hierarchical neural network for knowledge graph embedding

Link prediction for knowledge graphs aims to obtain missing nodes in triples. In recent years, link prediction methods have made specific achievements in knowledge graph embedding. However, knowledge graphs are characteristic of the heterogeneity of multiple types of entities and relations. A vital issue is efficiently extracting complex graph information and constructing a knowledge-semantic fusion of multiple features. To overcome these issues, a novel link prediction framework based on a multisource hierarchical neural network for knowledge graph embedding (MSHE) is proposed. In particular, mapping functions obtain entities and relations from low- to high-dimensional mapping sources. The combination of mapping sources and entity-relation sources constitutes multisource knowledge information, which facilitates the integration of complex heterogeneous entities and relations. Unlike training a single independent network, the hierarchical embedding network proposed in this paper accumulates feature information at multiple levels. Then, to fuse feature information, our Highway multilayer perceptron (MLP) inductively generates high-quality knowledge information. Through extensive experiments, MSHE’s knowledge graph embedding outperformed the state-of-the-art baselines on FB15k-237 and YAGO3-10. Furthermore, MSHE achieves a Hits@10 score that is 3.8% and 2.7% higher than that of ComplexGCN on FB15K-237 and WN18RR, respectively. MSHE achieves a higher score in Hits@1 than DCN 10.0% in the dataset YAGO3-10. The experiments show that the MSHE achieved excellent results in the four datasets of comparative experiments.

Inter span learning for document-level relation extraction

The entity-relationship extraction model has a significant influence in relation extraction. The existing model cannot effectively identify the entity-relationship triples in overlapping relationships. It also has the problem of long-distance dependencies between entities. In this paper, an inter span learning for document-level relation extraction model is proposed. Firstly, the model converts input of the BERT pre-training model into word vectors. Secondly, it divides the word vectors to form span sequences by random initial span and uses convolutional neural networks to extract entity information in the span sequences. Dividing the word vector into span sequences can divide the entity pairs that may have overlapping relationships into the same span sequence, partially solving the overlapping relationship problem. Thirdly, the model uses inter span learning to obtain entity information in different span sequences. It fuses entity type features and uses Softmax regression to achieve entity recognition. Aiming at solving the problem of long-distance dependence between entities, inter span learning can fuse the information in different span sequences. Finally, it fuses text information and relationship type features, and uses Linear Layer to classify relationships. Experiments demonstrate that the model improves the F1-score of the DocRED dataset by 2.74% when compared to the baseline model.

Marshall–Olkin power-law distributions in length-frequency of entities

Entities involve important concepts with concrete meanings and play important roles in numerous linguistic tasks. Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts. In this paper, we are curious to know whether there are some common characteristics that connect those different forms of entities. Specifically, we investigate the underlying distributions of entities from different types and different languages, trying to figure out some common characteristics behind those diverse entities. After analyzing twelve datasets about different types of entities and eighteen datasets about entities in different languages, we find that while these entities are dramatically diverse from each other in many aspects, their length-frequencies can be well characterized by a family of Marshall–Olkin power-law (MOPL) distributions. We conduct experiments on those thirty datasets about entities in different types and different languages, and experimental results demonstrate that MOPL models characterize the length-frequencies of entities much better than two state-of-the-art power-law models and an alternative log-normal model. Experimental results also demonstrate that MOPL models are scalable to the length-frequency of entities in large-scale real-world datasets.

Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation

Monitoring and evaluating the health of the cloud manufacturing service ecosystem (CMSE) is critical to ensuring the long-term development of the cloud manufacturing service platform. The behavior patterns of three types of market entities, service demander (SD), service provider (SP) and platform operator (PO), have an important impact on the evolution trend of CMSE. The formulation of platform transaction rules and development of operation and regulation strategies need clarified the evolution law of the CMSE. Therefore, the evolution framework of the CMSE is constructed, the behavior modes of market entities such as SP, SD, and PO are established respectively, and multi-agent behavior simulation experiments are carried out. Simulation analysis showed that the more sensitive the SP was to the transaction activity in the system, the earlier the ecosystem in which it was located enters the stable period. When the sensitivity k of SD to the number of SPs that are available for required services in the system was between 0.45 and 0.5, the evolution trend of CMSE would show an obvious turning point from rapid growth to shrinkage. PO could adopt flexible charging strategies at different stages of the evolution of the CMSE to maximize revenue.

OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs

Different knowledge graphs for the same domain are often uniquely housed on the Web. Effectively linking entities from different graphs is critical for building an open and comprehensive knowledge graph. However, linking entities across different sources has thus far faced various challenges, including the increasingly large-scale volume of the data, the heterogeneity of the graphs, and the ambiguity of real-world entities. To address them, we propose a unified framework LinKG. Specifically, we decouple the problem into different linking tasks based on the unique properties of each type of entity. To link word sequence based entities, we propose an LSTM-based method to capture word dependencies. To link entities of large scale, we utilize the hashing technique and convolutional neural networks for scalable and accurate linking. To link ambiguous entities, we propose heterogeneous graph attention networks to leverage heterogeneous structural information. Finally, to validate the design choices of different LinKG modules, we characterize the relationships between different tasks based on the single-domain and multi-domain transfer models. Extensive experiments demonstrate the effectiveness of LinKG with an overall F1-score of 95.15%, based on which we deploy and release the Open Academic Graph (OAG)—the largest publicly available heterogeneous academic graph to date.

Pengaruh Beban Kerja Stres Kerja dan Komunikasi Terhadap Semangat Kerja (Studi pada Karyawan PT Besi Baja Metalindo Mojokerto)

A company is one of the types of business entities protected by law as the place where the production process of a good or service is used to meet the needs of society. To the company’s goals, it requires the contribution of good employees. Cultivating a high working spirit should be considered by the company’s managers as an obligation whose nature is constant. Especially with regard to the workload that can cause work stress, communication between employees that can lead to an increase or decrease in working spirit. The aim of this survey is to know the impact of workload, work stress, and communication on employee work spirit. This research population is the entire staff of PT Iron Steel Metalindo of Mojokerto district. The sample was 78 using saturated sampling techniques. Data analysis techniques use double linear regression analysis with the help of the SPSS 25 program. Research findings show that workload variables have a negative and significant influence on the work spirit, work stress has a negative, significant impact on the working spirit, and communication has a positive and significant effect on the job spirit.