Entity Relation Research Articles

WebGIS approach of entity-oriented search to visualize historical and cultural events

Abstract The chronological biography or ‘Nianpu’ is a special style of Chinese historical writing formed by a person’s life events in time order. It provides significant records for the research of historical events. However, from syntactic collation, structural, and semantic aspects, the heterogeneousness of Nianpu materials caused the lack of systematical information collation, which could hinder the research of historical events. The article presents a semantic WebGIS platform based on an entity-oriented search and visualization system to reorganize unstructured information. It delivers: (1) a Chinese historical event database to store and manage Chinese historical information derived from the chronological biography; (2) an ontology to integrate different historical datasets; and (3) a web geographic information system to visualize and analyze information of historical events. This platform achieves the reorganization of historical event information at spatial and temporal levels by the Web. It enables humanities researchers to explore the relationships of historical entities and their evolution visually and interactively. The platform is available at https://nianpu.pkudh.org/.

A Method of Domain Dictionary Construction for Electric Vehicles Disassembly.

Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. Herein, the construction of a domain dictionary for the disassembly of electric vehicles is a research work that has important research significance. Extracting high-quality keywords from text and categorizing them widely uses information mining, which is the basis of named entity recognition, relation extraction, knowledge questions and answers and other disassembly domain information recognition and extraction. In this paper, we propose a supervised learning dictionary construction algorithm based on multi-dimensional features that combines different features of extraction candidate keywords from the text of each scientific study. Keywords recognition is regarded as a binary classification problem using the LightGBM model to filter each keyword, and then expand the domain dictionary based on the pointwise mutual information value between keywords and its category. Here, we make use of Chinese disassembly manuals, patents and papers in order to establish a general corpus about the disassembly information and then use our model to mine the disassembly parts, disassembly tools, disassembly methods, disassembly process, and other categories of disassembly keywords. The experiment evidenced that our algorithms can significantly improve extraction and category performance better than traditional algorithms in the disassembly domain. We also investigated the performance algorithms and attempts to describe them. Our work sets a benchmark for domain dictionary construction in the field of disassembly of electric vehicles that is based on the newly developed dataset using a multi-class terminology classification.

NUMBERS-TYPES OF THE CLASSIFIER OF BIG DATA OBJECTS OF THE SOCIO-ECONOMIC SYSTEM

The N-scheme developed to replace the well-known g-scheme of the normal Markov algorithm is investigated. The numbers-types of the classifier of big data objects of socio-economic systems are introduced, theoretically substantiated and synthesized, which determine the N-scheme of the Markov algorithm. The purpose of such studies is determined – this is to find scientific and technical ways to counter money laundering, for which it is necessary to analyze a large amount of data on economic interactions (relationships) of individuals and legal entities that make up various socio-economic systems, where g-schemes are more expressive than the wellknown Markov g-schemes. It has been established that for this it is advisable to study the properties of combinations of initial, terminal and isomorphic morphisms (relationships) of objects of socio-economic systems. It was found that each word from the Markov alphabet A can be characterized by the morphism number n, which in turn can be represented as one of the letters of the alphabet M =tjilckmg, where its association method is defined by M, with the value f in the unit of measure “type”. It is noted that then for each object it is possible to collect data on all its initial, terminal, isomorphic relationships (interactions) and represent them in the form of w-syllables and w-words from letters of the alphabet M, completing the formation of the N-scheme of the Markov algorithm. Examples of categories and their type numbers are given, composed of letters of the alphabet M. It is noted that a certain lattice is formed from the type numbers, which can be used to solve NP-complete problems. It has been found that it is advisable to evaluate superlarge w-words of numbers-types, since they can contain fragments that are responsible for the entry of the algorithm into an infinite loop.

SAAED: Embedding and Deep Learning Enhance Accurate Prediction of Association Between circRNA and Disease.

Emerging evidence indicates that circRNA can regulate various diseases. However, the mechanisms of circRNA in these diseases have not been fully understood. Therefore, detecting potential circRNA–disease associations has far-reaching significance for pathological development and treatment of these diseases. In recent years, deep learning models are used in association analysis of circRNA–disease, but a lack of circRNA–disease association data limits further improvement. Therefore, there is an urgent need to mine more semantic information from data. In this paper, we propose a novel method called Semantic Association Analysis by Embedding and Deep learning (SAAED), which consists of two parts, a neural network embedding model called Entity Relation Network (ERN) and a Pseudo-Siamese network (PSN) for analysis. ERN can fuse multiple sources of data and express the information with low-dimensional embedding vectors. PSN can extract the feature between circRNA and disease for the association analysis. CircRNA–disease, circRNA–miRNA, disease–gene, disease–miRNA, disease–lncRNA, and disease–drug association information are used in this paper. More association data can be introduced for analysis without restriction. Based on the CircR2Disease benchmark dataset for evaluation, a fivefold cross-validation experiment showed an AUC of 98.92%, an accuracy of 95.39%, and a sensitivity of 93.06%. Compared with other state-of-the-art models, SAAED achieves the best overall performance. SAAED can expand the expression of the biological related information and is an efficient method for predicting potential circRNA–disease association.

An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.

In recent years, entity relation extraction has been a critical technique to help people analyze complex structured text data. However, there is no advanced research in food health and safety to help people analyze the complex concepts between food and human health and their relationships. This paper proposes an entity relation extraction method FHER for the few-shot learning in the food health and safety domain. For few-shot learning in the food health and safety domain, we propose three methods that effectively improve the performance of entity relationship extraction. The three methods are applied to the self-built data sets FH and MHD. The experimental results show that the method can effectively extract domain-related entities and their relations in a small sample size environment.

Legal Status of Participants of the Intra-Group Loan Agreement between the Companies (Pooling Cache) under the Management of a Unified Treasury Center

The article examines the problems of the emergence and development in Russia of one of the mechanisms of financial management — cash pooling. The legal nature of cash pooling as one of the types of intragroup loan agreements between companies is revealed. Three categories of participants in cash pooling have been identified. This article examines the legal status of only one category of cash pooling participants — legal entities united in a group under the control of a single treasury center. The analysis of the terms “single treasury center”, “central treasury” and other similar terms, which means a single center through which financial flows of organizations pass, is carried out.The question of what legal entities can be combined into a group under the control of a single treasury center is being resolved. The possibilities of participation in these relations of legal entities of various organizational and legal forms are considered. The article examines the peculiarities of participation in intra-group loan relations of a state company, state corporations and public-law companies united in a group with other legal entities under the management of a single treasury center.

Students’ Attendance Management in Higher Institutions Using Azure Cognitive Service and Opencv Face Detection & Recognition Attendance System

This research aimed at studying the current methods of attendance used at higher institutions of learning in Uganda and the feasibility of using facial biometrics as a new method of capturing attendance. Facial biometrics is distinct from other biometrics because it can be carried out without the consent of the person involved. As a result, the researcher developed a face recognition attendance system using OpenCV and Microsoft Azure CS. Questionnaires, interviews, and observations were used to capture data for the research. The data were analyzed using SPSS to get the requirements and systems functionalities. Object-Oriented Design tools were used to model the architecture of the system. Data Flow Diagram, Use-Case Diagram, Activity Diagram, and Flow Chart were used for processing whereas Entity Relation Diagram was used for data modeling. The system was designed to facilitate attendance management of a large number of attendees with ease. Efficiency and reliability were essential features of the system. Data visualization was provided to help management make informed and timely decisions on management matters that are related to attendance. The system was developed using python Tkinter, OpenCV, and Azure CS as mentioned above. The data (images) used by the system were stored in the cloud for accessibility by multiple users. The system was tested thoroughly using various testing types to uncover and fix errors and to minimize the severity of failures.

Entity and relation collaborative extraction approach based on multi-head attention and gated mechanism

Entity and relation extraction has been widely studied in natural language processing, and some joint methods have been proposed in recent years. However, existing studies still suffer from two problems. Firstly, the token space information has been fully utilized in those studies, while the label space information is underutilized. However, a few preliminary works have proven that the label space information could contribute to this task. Secondly, the performance of relevant entities detection is still unsatisfactory in entity and relation extraction tasks. In this paper, a new model GANCE (Gated and Attentive Network Collaborative Extracting) is proposed to address these problems. Firstly, GANCE exploits the label space information by applying a gating mechanism, which could improve the performance of the relation extraction. Then, two multi-head attention modules are designed to update the token and token-label fusion representation. In this way, the relevant entities detection could be solved. Experimental results demonstrate that GANCE has better accuracy than several competitive approaches in terms of entity recognition and relation extraction on the CoNLL04 dataset at 90.32% and 73.59%, respectively. Moreover, the F1 score of relation extraction increased by 1.24% over existing approaches in the ADE dataset.

An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion

Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model.

Decomposing and Recomposing Event Structure

AbstractWe present an event structure classification empirically derived from inferential properties annotated on sentence- and document-level Universal Decompositional Semantics (UDS) graphs. We induce this classification jointly with semantic role, entity, and event-event relation classifications using a document-level generative model structured by these graphs. To support this induction, we augment existing annotations found in the UDS1.0 dataset, which covers the entirety of the English Web Treebank, with an array of inferential properties capturing fine-grained aspects of the temporal and aspectual structure of events. The resulting dataset (available at decomp.io) is the largest annotation of event structure and (partial) event coreference to date.

RANCANG BANGUN SISTEM ADMINISTRASI KEPENDUDUKAN DI DESA KARANGUDI, NGRAMPAL, KABUPETEN SRAGEN

Karangudi Village Hall Office is an agency that does the logging process of the population especially in the process of making the petition for Resident Sign Cards (KTP), family card (KK), Birth Certificate, Certificate of death, the Residents come and go. Certificate processing in Karangudi Village already runs Hall Offices still using paper form completion as well as long time processing cover letters about 30 minutes and must be lined up with the other another activities, report the results of the examination of the processing of the population to generate reports of residents came, residents born, residents moved in, people die, a recapitulation of the population each month can not be done at any time. The methods where used to design system observation, interviewing, documentation, libraries. The design of the system is described by using the Context Diagram, Data Flow Diagram (DAD), Entity Relation Diagram (ERD) and Tiered Diagram. The system is built to produce population administration system application program in the village Karangudi , district Ngrampal , Sragen district in order for data processing much better population and maximum thus simplifying the process and the report can provide information on population.

A Entity Relation Extraction Model with Enhanced Position Attention in Food Domain

A Entity Relation Extraction Model with Enhanced Position Attention in Food Domain

Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications

Nowadays, the analysis of digital media aimed at prediction of the society’s reaction to particular events and processes is a task of a great significance. Internet sources contain a large amount of meaningful information for a set of domains, such as marketing, author profiling, social situation analysis, healthcare, etc. In the case of healthcare, this information is useful for the pharmacovigilance purposes, including re-profiling of medications. The analysis of the mentioned sources requires the development of automatic natural language processing methods. These methods, in turn, require text datasets with complex annotation including information about named entities and relations between them. As the relevant literature analysis shows, there is a scarcity of datasets in the Russian language with annotated entity relations, and none have existed so far in the medical domain. This paper presents the first Russian-language textual corpus where entities have labels of different contexts within a single text, so that related entities share a common context. therefore this corpus is suitable for the task of belonging to the medical domain. Our second contribution is a method for the automated extraction of entity relations in Russian-language texts using the XLM-RoBERTa language model preliminarily trained on Russian drug review texts. A comparison with other machine learning methods is performed to estimate the efficiency of the proposed method. The method yields state-of-the-art accuracy of extracting the following relationship types: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown on the presented subcorpus from the Russian Drug Review Corpus, the method developed achieves a mean F1-score of 80.4% (estimated with cross-validation, averaged over the four relationship types). This result is 3.6% higher compared to the existing language model RuBERT, and 21.77% higher compared to basic ML classifiers.

MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction.

Natural language processing (NLP) tasks aim to convert unstructured text data (e.g. articles or dialogues) to structured information. In recent years, we have witnessed fundamental advances of NLP technique, which has been widely used in many applications such as financial text mining, news recommendation and machine translation. However, its application in the biomedical space remains challenging due to a lack of labeled data, ambiguities and inconsistencies of biological terminology. In biomedical marker discovery studies, tools that rely on NLP models to automatically and accurately extract relations of biomedical entities are valuable as they can provide a more thorough survey of all available literature, hence providing a less biased result compared to manual curation. In addition, the fast speed of machine reader helps quickly orient research and development. To address the aforementioned needs, we developed automatic training data labeling, rule-based biological terminology cleaning and a more accurate NLP model for binary associative and multi-relation prediction into the MarkerGenie program. We demonstrated the effectiveness of the proposed methods in identifying relations between biomedical entities on various benchmark datasets and case studies. MarkerGenie is available at https://www.genegeniedx.com/markergenie/. Data for model training and evaluation, term lists of biomedical entities, details of the case studies and all trained models are provided at https://drive.google.com/drive/folders/14RypiIfIr3W_K-mNIAx9BNtObHSZoAyn?usp=sharing. Supplementary data are available at Bioinformatics Advances online.

Perancangan Sistem Informasi Formulir Waktu Penyediaan Dokumen Rekam Medis Rawat Inap Menggunakan Visual Studio 2010

The activity of receiving inpatients at a hospital must record the patient's arrival time until the medical record documentation is available or you could say until the patient goes home. So that the time of admission of inpatients is in accordance with the standard or not. This activity is still done manually (paperless) which is often a hassle for registration officers and prone to data manipulation. The purpose of this study is to create an information system for inpatient admission time efficiency forms to provide information related to patient admission times electronically that makes it easier for registration officers to input data or the time when patients arrive until medical record documents are available, reduce costs and minimize data manipulation. . The creation of an information system for the time efficiency of inpatient admissions uses data collection methods by conducting interviews, reviewing the literature, and observing. The system design method uses ERD (entity relation diagram) and DFD (data flow diagram) then implemented with Microsoft Visual Studio 2010 programming language.

Automatic Information Extraction in the Third-Generation Semiconductor Materials Domain Based on DKNet and MANet

The third-generation semiconductor materials (TGSMs) is a frontier scientific domain, where researchers need to consult extensive literature for the entity information on materials, devices, preparation methods, and experimental performances, and sort out the complex relations between them. However, the explosion of relevant papers has far exceeded researchers’ reading ability. In this article, the TGSM-field automatic information extraction is conducted based on entity recognition (ER) and relation extraction (RE) techniques. First, the corpora used for ER and RE in this field are created. Second, aiming at the complexity of the entities, a neural network using domain knowledge (DKNet) is proposed to improve ER performance. It uses the keyword sequence of each entity type as prior knowledge, adds a dedicated embedding to encode entity categories, then combines prior knowledge and encoded vectors with the context through a gated information fusion module to assist recognition. As for the indicative word dependence problem of entity relations, a multi-aspect attention-based network model (MANet) is proposed to enhance the attention to relation-indicative words, thereby improving the RE performance. Finally, F1 scores of 74.5 and 85.9 were achieved on the created ER and RE test sets, outperforming other advanced models by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.4~\sim ~10.1$ </tex-math></inline-formula> , which is the best performance of the TGSM-field automatic information extraction.

Making RDBMSs efficient on graph workloads through predefined joins

Joins in native graph database management systems (GDBMSs) are predefined to the system as edges, which are indexed in adjacency list indices and serve as pointers. This contrasts with and can be more performant than value-based joins in RDBMSs. Existing approaches to integrate predefined joins into RDBMSs adopt a strict separation of graph and relational data and processors, where a graph-specific processor uses left-deep and index nested loop joins (INLJ) for a subset of joins. In this paper we study and experimentally evaluate this technique's performance against an alternative technique that is based on using hash joins that use system-level row IDs (RIDs). In this alternative approach, when a join between two tables is predefined to the system, the RIDs of joining tuples are materialized in extended tables and optionally in RID indices. Instead of using the RID index to perform the join directly, we use it primarily in hash joins to generate filters that can be passed to scans using sideways information passing (sip), ensuring sequential scans. We further compare these two approaches against: (i) the default value-based joins of an RDBMS; and (ii) using materialized views that can avoid evaluating predefined joins completely and instead replace them with scans. We integrated our alternative approach to DuckDB and call the resulting system GRainDB. Our evaluation demonstrates that existing INJL-based approach can be very efficient when entity relations contain very selective filters. However, GRainDB's approach is more robust and is either competitive with or outperforms the INLJ-based approach across a wide range of settings. We further demonstrate that GRainDB far improves the performance of DuckDB, which uses default value-based joins, on relational and graph workloads with large many-to-many joins, making it competitive with a state-of-the-art GDBMS, and incurs no major overheads otherwise.

Historical aspects of the formation of the conceptual apparatus of the value theory in economic science

The emergence and development of the concept of value in terms of certain historical stages of economics are investigated in this paper. It is also investigated that the concept of value is fundamental problem in economics and primary source for understanding the purpose and motives of economic entities, areas of consumption, distribution and the exchange of their resources and income. The objective of the investigation, is to assess on the basis of comparative analysis of the methodology of the main provisions and conclusions of existing concepts of value, their significance for the process of learning about the functionality and relationships of economic entities in the current economy. While investigating the conceptual positions of economists-scientists concerning the theory of value at different historical stages of its existence and development, theoretical general scientific research methods (formalization, synthesis, abstraction and logical generalization) are used. The evolution of the theory of value, from ancient times until such historical schools as mercantilism, physiocracy, English political economics, French classical bourgeois political economics, Marxism, and marginalism is considered in this paper. The study of the theory of value in economics makes it possible to determine that the main scientific paradigms prevailed in the theory of value are the labor theory of value and the theory of marginal utility. The problem of preserving the potential of traditional theories of value, and the preconditions on which they are based on, is considered. In addition, the logical and historical relationship between the concepts of value is investigated; the impact of different concepts of value on the process of the market economy relationship understanding is evaluated. The practical value of scientific work is that clearer understanding of the concept of value at the theoretical level makes it possible to solve practical problems in determining the amount of costs, their structure, and methods of calculation for businesses. It is investigated that the concept of value remains relevant in modern economics, as it is the basis for understanding such important categories as price, production costs, profits, etc., and the laws of value determine the behaviour in market economy. It is found that the value, covering all centres of the economy, determines the motives and goals of economic entities; promotes the efficient distribution and use of resources and income, sustainable growth of production, its balance; affects the whole complex of economic relations arising in the process of social reproduction of material and spiritual goods, their production, exchange, distribution and consumption.

The Dunciad d’Alexander Pope et The London Spy de Ned Ward : expériences de visualisation textuelle

Text visualization is the technique of using graphs and charts to examine text as data. Often, these do not represent a text directly and instead display an output based on word counts, word sequences, and so on. This technique can provide insights into important keywords in a text, provide an overview of textual content, or reveal trends and patterns within one text or across many texts. This paper describes recent development of and experiments with several tools for the Grub Street Project to generate visualizations of eighteenth-century texts, especially in terms of the spatial relationships of words and entities on the pages of the original documents. Two examples are discussed in this article as test cases: Edward Ward’s The London Spy Compleat, in Eighteen Parts (1703) and Alexander Pope’s The Dunciad. With Notes Variorum (1729).

Internal and external sovereignty of post-Soviet unrecognized states

The article studies internal and external sovereignty of unrecognized or partially recognized post-Soviet states through the use of empirical analysis of quantitative data and critical analysis of subjective factors. It analyzes statistical and other information that reflects the demographic and socio-economic situation, foreign economic relations of such entities, and their impact on neighboring countries and regions. This is combined with the critical study of public opinions and popular identities based on surveys and 13 focus groups conducted in the fall of 2020 in Transnistria and Abkhazia. In all post-Soviet de facto states, the population has noticeably decreased (except for Nagorno-Karabakh before the 2020 war). The reason for the outflow of residents was the inability of such entities to provide citizens with decent incomes and their poor performance in economic competition with neighboring countries and regions, reflected in the dynamics of the key indicators per capita in terms of purchasing power parity. However, the economic and demographic situation cannot explain the sustainability of such entities. Strong political identity of citizens and their loyalty to the ruling regimes serve as the main basis for, and essential elements of, internal sovereignty. Participants of surveys highly value independence. They believe that it presumes economic independence, improved well-being, and the development of full-fledged international relations that helps to strengthen external sovereignty. Some respondents expressed disappointment with the fact that these goals have not been achieved in the course of 30 years. The geopolitical vision of the world by residents of post-Soviet de facto states is “Russia-centric”, although Russia is far from being idealized. In conclusion, the risks that Russia faces in connection with post-Soviet de facto states and with Moscow's involvement in the respective conflicts are assessed.