Entity Relationship Research Articles

Sistem Informasi Perpustakaan Berbasis Client Server di SMK Bintara Rancaekek

SMK Bintara Rancaekek still has an educational support system that has not been computerized, one of which is library management. The process of managing library data is handled by one officer, starting from searching for book data, recording loan transactions, return transactions, calculating fines, and generating reports, all of which are still done manually or have not been computerized. As a result, several issues arise, including difficulties in managing borrowing and returning data, time-consuming recapping, and the risk of recording errors and data loss. The purpose of this research is to assist officers in managing book data, visits, borrowing and returning transactions, as well as minimizing data loss and recording errors, simplifying the calculation of fines, and generating reports quickly. The method used in this research is the Rational Unified Process (RUP). The design uses analysis tools such as flowmaps, Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), Data Dictionaries, and Structure Charts. The design and development of this library information system are expected to address the aforementioned problems and provide ease of operational work, especially in managing the borrowing and returning of library books.

Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models

Data security is a critical concern for Internet users, primarily as more people rely on social networks and online tools daily. Despite the convenience, many users are unaware of the risks posed to their sensitive and personal data. This study addresses this issue by presenting a comprehensive solution to prevent personal data leakage using online tools. We developed a conceptual solution that enhances user privacy by identifying and anonymizing named entity classes representing sensitive data while maintaining the original context by swapping source entities for functional data. Our approach utilizes natural language processing methods, combining machine learning tools such as MITIE and spaCy with rule-based text analysis. We employed regular expressions and large language models to anonymize text, preserving its context for further processing or enabling restoration to the original form after transformations. The results demonstrate the effectiveness of our custom-trained models, achieving an F1 score of 0.8292. Additionally, the proposed algorithms successfully preserved context in approximately 93.23% of test cases, indicating a promising solution for secure data handling in online environments.

A large-scale Chinese patent dataset for information extraction

Information extraction is an important foundation for automated patent analysis. Deep learning methods show promising results for information extraction, the performance of such methods heavily depends on the available corpus. To promote research on Chinese information extraction and evaluate the performance of related systems, we present a novel dataset, named CPIE, and make it publicly available. The dataset consisting of five thousands records of Chinese patent documents. The data were annotated by a tagging team using an on-line annotation tools. The dataset was evaluated using a state-of-the-art information extraction method that involves named entity recognition and relationship classification. The results shed light on new challenges and promote information extraction research.

JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps.

Biomedical Relation Extraction (RE) is central to Biomedical Natural Language Processing and is crucial for various downstream applications. Existing RE challenges in the field of biology have primarily focused on intra-sentential analysis. However, with the rapid increase in the volume of literature and the complexity of relationships between biomedical entities, it often becomes necessary to consider multiple sentences to fully extract the relationship between a pair of entities. Current methods often fail to fully capture the complex semantic structures of information in documents, thereby affecting extraction accuracy. Therefore, unlike traditional RE methods that rely on sentence-level analysis and heuristic rules, our method focuses on extracting entity relationships from biomedical literature titles and abstracts and classifying relations that are novel findings. In our method, a multitask training approach is employed for fine-tuning a Pre-trained Language Model in the field of biology. Based on a broad spectrum of carefully designed tasks, our multitask method not only extracts relations of better quality due to more effective supervision but also achieves a more accurate classification of whether the entity pairs are novel findings. Moreover, by applying a model ensemble method, we further enhance our model's performance. The extensive experiments demonstrate that our method achieves significant performance improvements, i.e. surpassing the existing baseline by 3.94% in RE and 3.27% in Triplet Novel Typing in F1 score on BioRED, confirming its effectiveness in handling complex biomedical literature RE tasks. Database URL: https://codalab.lisn.upsaclay.fr/competitions/13377#learn_the_details-dataset.

Sistem Informasi Manajemen Aset PT. Citra Putra Kebun Asri Jorong Estate Berbasis Web

Assets generally state that assets are resources controlled by the company as a result of past events where future economic benefits are expected to be obtained by the company. Company assets originate from transactions or other events that occurred in the past. Companies usually acquire assets through outlays in the form of purchases or self-production. The Head of Administration independently handles asset data collection and is still done manually using Microsoft Excel. Recording of asset items based on space, fields and conditions is not yet in the application, so the admin does not know the exact number of indoor asset items, fields and does not know the condition of asset items, if re-data of indoor asset items, fields and conditions takes a long time, it is necessary development of applications for recording indoor assets, fields and conditions of asset items. This condition can also lead to the possibility of data collection errors and also the length of time in searching for data if needed at any time. Asset Management Information System PT. Citra Putra Kebun Asri Jorong Estate Web-based using MySQL database. The designs used are Entity Relationship Diagrams (ERD), Data Flow Diagrams (DFD), and Flowcharts. The programming language used is Hypertext Preprocessor (PHP). The system development model used is the waterfall model. The functionality test used is Blackbox testing.

Data Modeling Essentials: Techniques, Best Practices, and Future Trends

Data modeling, in essence, is based on the process of data structuring and organization to effectively handle, analyze, and make informed decisions across organizations. This paper reviews the major concepts and methods significant for modern data architecture in "Data Modeling Essentials: Techniques, Best Practices, and Future Trends.". We start by considering the base techniques, namely Entity-Relationship and Dimensional modeling, and look into their applications in operational and analytical data systems. The paper also covers more advanced topics that include Data Vault modeling for agile data warehousing and NoSQL modeling to accommodate unstructured and semi-structured data, increasingly important in today's big data era. The paper also gives an overview of best practices in developing scalable, flexible data models: stakeholder communication, documentation, and the use of automation tools in accelerating model development and consistency. Data quality, adherence to regulations, and alignment of models with business objectives are among the practices identified as crucial in today's fast-moving data environments. New trends are taking precedence, and data modeling is continuously evolving. We will review the rise of AI- assisted data modeling, graph database structures for highly interconnected data, and model-driven development to accelerate system deployments. This paper examines actionable insights for data professionals, IT teams, and business leaders on how to design robust, agile models that improve the quality of data, enhance decision-making, and foster innovation in an ever-changing data ecosystem. Keywords Data Modeling, Entity-Relationship (ER) Modeling, Dimensional Modeling, Data Quality, Scalable Data Models, Model Driven Development (MDD), Data Management, Structured & Unstructured Data, Future Trends in Data Modeling.

Elucidating Sex-Specific Immune Profiles in a Breast Cancer Model.

Breast cancer is commonly thought of as a "women's disease". However, men are increasingly diagnosed with the disease, and their mortality rates are disparately higher than those of female patients. The abundance and composition of the immune microenvironment are determinants of breast cancer progression and survival. It is well documented that there are sex-specific differences in the immune response to several diseases, including various cancers. However, the effects of these differences in the context of breast cancer remain to be explored. This study demonstrates sex differences in the hormonal and immune landscape of the MMTV-PyMT transgenic murine model of female and male ER+ breast cancer using single-cell RNA sequencing (scRNA-Seq), whole-slide immunohistochemistry, and flow cytometry. Mammary tumors of transgenic male mice had increased estrogen receptor alpha expression and enriched nuclear binding signatures compared to female tumors. In the tumor immune compartment, male mice had lower intratumoral leukocyte infiltration. Yet, scRNA-Seq analysis reveals a more immunostimulatory microenvironment and increased antitumor immune populations in the primary and metastatic lungs as compared to transgenic females. Despite a more favorable innate immune profile, the metastatic burden was increased in male mice. Our data support a sex-dependent immune response in mammary carcinoma associated with the tumor, and likely host, hormonal environment. With emerging therapeutics targeting the tumor immune microenvironment, characterizing immune profiles is critical for optimizing their use in all breast cancer patients.

Sistem Informasi Jual Beli Kelapa Sawit Berbasis Web pada Peron Reskianto

In Surau Gading village, the majority of the population is employed as oil palm farmers. Every two weeks, farmers harvest oil palm fruit. After harvesting the oil palm fruit, the farmers sell it to Peron Resdianto, who is a young entrepreneur in the village of Surau Gading. Almost every day, the platform owner purchases agricultural products made from palm oil. Because a large number of customers sell their agricultural products, palm oil platform owners have to make sales to the factory once every two days. Once the palm oil mill receives the sales proceeds, the platform owner disburses payments to the palm oil farmers who sell their agricultural products. As information technology advances, it has now spread to almost all fields and is developing at a rapid pace. Information technology's application is evolving, as evidenced by the constant advancements in the field. The author wants to design a web-based information system for selling and buying palm oil on the Resdianto platform using the PHP and My SQL programming languages. We designed this system using system modeling tools, personal home page (PHP), MYSQL, HTML, data flow diagrams (DFD), Xampp, flowcharts, and entity relationship diagrams (ERD). Peron Reskianto can improve performance by creating a web-based information system for buying and selling palm oil, which will assist platform owners in collecting data on palm oil purchases and sales. This information system guarantees security and reduces errors.

Named Entity Recognition and Relationship Extraction for Biomedical Text: A comprehensive survey, recent advancements, and future research directions

Named Entity Recognition and Relationship Extraction for Biomedical Text: A comprehensive survey, recent advancements, and future research directions

PubChem 2025 update.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.

The Application of GCN Algorithm in Building Construction Knowledge Graph Updating under the Combination of Artificial Intelligence and Knowledge Management

To promote the updating and iteration of the construction field, the construction knowledge graph can expand the professional knowledge system within the field and provide scientific and reasonable management decisions. The research adopts deep learning technology to update the knowledge graph in computer vision. The specific update method is to extract entities and analyze entity relationships in the framework of the knowledge graph, to achieve the application of the knowledge graph in the field of construction. Afterwards, a graph convolutional neural network is used to integrate semantic information and construct a network model of semantic embedding vectors. Finally, zero sample learning technologies are combined to enhance prior information during the construction process, thus demonstrating a better backward process for updating iterations. Through testing and validation of the dataset, it is found that the highest TOP-1 accuracy of the zero sample object detection model is 77%, 30%, and 45%, respectively, under different proportions of the dataset. The TOP-1 accuracy of the zero sample action recognition model increases by 22%, 23%, and 41%, respectively, compared to the former. In addition, the detection model combined with semantic embedding vectors has an average accuracy of 45% for unknown class tasks at a ratio of 8:2. This proves that the performance of the zero sample human object interaction detection model based on semantic embedding vectors is superior, providing a knowledge foundation and theoretical support for the updating of knowledge graphs in the field of construction.

Proline Analogues in Drug Design: Current Trends and Future Prospects.

Proline analogues are versatile chemical building blocks that enable modular construction of small-molecule drugs and pharmaceutical peptides. Over the past 15 years, the FDA has approved over 15 drugs containing proline analogues in their structures, five in the last three years alone (daridorexant, trofinetide, nirmatrelvir, rezafungin, danicopan). This perspective offers an analysis of the most common types of proline analogues currently trending in drug design. We focus on examples of fluoroprolines, α-methylproline, bicyclic proline analogues, and aminoprolines, while also highlighting proline analogues that remain underrepresented. We supplement our analysis with physicochemical information regarding the specific molecular properties of these moieties. Additionally, we discuss several intriguing cases where nonproline residues were replaced with proline analogues as a strategy to eliminate unwanted hydrogen bond donor sites. In conclusion, we present some suggestions for the future exploration of this promising class of molecular entities in drug discovery.

Концептуально-онтологические аспекты множественности предка в информационных моделях «сущность-связь»

The design of information models in the development of automated systems involves constructing entity-relationship models, which define classes of entities and interclass relationships of the ancestor-descendant type, serving as a conceptual and ontological foundation for the subsequent creation of databases. This article explores aspects of representing the semantic constraints of the subject area imposed on relationships between entities in entity-relationship models. A special type of relationship between classes of entities, termed multiple ancestor (MA) relations, is highlighted, where several instances of the ancestor entity may exist for a single instance of the descendant entity. The article analyses the possible semantic constraints that arise in such cases and the anomalies they generate. Based on the introduced concepts of ascending kinship lines and the MA predicate, a formalisation of MA integrity is presented. Examples of formal MA constraints are provided, including positive (requiring the coincidence of ancestor instances), negative (requiring the mismatch of ancestor instances), and mixed. The mutual influence of several MA constraints with partially intersecting lines of ascending kinship is also examined. Finally, the feasibility of implementing MA constraints in relational database models is evaluated.

Genetic diagnosis in XY disorders of sex development

XY disorders of sex development (XY DSD) comprise a class of heterogeneous genetic entities that result in discrepancies between chromosomal, gonadal, and phenotypic sex due to a reduction in androgen synthesis or action. The chief categories of disorders include gonadal dysgenesis, disorders with reduced androgen production (biosynthetic defect – either alone or in conjunction with impaired glucocorticoid and/or mineralocorticoid synthesis), and insensitivity to androgen action. While conventional diagnostic modalities, encompassing karyotyping, biochemistry, radiology, and, in a few cases, diagnostic laparoscopy, help in formulating a provisional diagnosis, molecular genetic testing is key to arriving at a precise etiology. Besides ending the diagnostic uncertainty, a molecular diagnosis helps to predict the natural course in terms of pubertal development and potential for fertility, thus contributing to decisions on the gender of rearing; and guides on surveillance for extragenital features and the risk of recurrence in subsequent pregnancies. This paper broadly discusses the genetic basis of XY DSD, different modalities of genetic testing, and their utility and limitations.

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

IMPLEMENTATION OF THE FUZZY TSUKAMOTO METHOD TO EARLY DETECTION TYPES OF GASTRIC DISEASES

Abstract: The stomach is a critical organ within the human digestive system, and gastric diseases represent significant health concerns often underestimated by the public. Factors such as poor dietary habits, stress, and bacterial infections contribute to these ailments, whose initial symptoms can easily be misinterpreted due to their similarity with other gastrointestinal disorders. The knowledge gap exists in the diagnostic challenges faced by both patients and healthcare professionals, necessitating the development of improved diagnostic tools. This study aims to enhance the diagnostic process for gastric diseases through the implementation of an expert system utilizing the Fuzzy Tsukamoto method. By employing questionnaires and expert interviews to gather data on symptoms, we develop a comprehensive fuzzy inference system. Results indicate that the system accurately classifies stomach ailments based on user-reported symptoms, facilitating early diagnosis and treatment. The novelty of this research lies in the application of the Fuzzy Tsukamoto method, which allows for nuanced symptom analysis and inference, providing a more accessible means for the general public to identify potential gastric diseases. Furthermore, the system design includes flowcharts, data flow diagrams, and entity relationship diagrams to ensure a user-friendly interface. The implications of this study are significant, as it offers a robust tool for early detection of gastric diseases, thereby potentially improving patient outcomes and reducing healthcare costs associated with late-stage interventions. This expert system not only assists in diagnosing gastric conditions but also serves as a valuable resource for enhancing the understanding of symptomology in gastroenterology.

Power text information extraction based on multi-task learning

In order to improve the analysis and processing speed of power system fault text in actual business scenarios, a power fault text information automatic extraction model based on pre-training and multi-task learning is proposed. The pre-training model is used to learn the context information of power text words, and the first-order and second-order fusion features of words are mined to enhance the representation ability of features. The multi-task learning framework is used to combine the learning of named entity recognition and relationship extraction tasks to achieve mutual complementation and mutual promotion between entity recognition and relationship extraction, thereby improving the performance of power fault text information extraction. Finally, the model is verified by the daily business data of a power grid data center. Compared with other models, the accuracy and recall rate of power fault text entity recognition and relationship extraction are improved.

A Method for Extracting Power Entity Relationships Based on Hybrid Neural Networks

In response to the challenges of entity relationship extraction in unstructured text, the author proposes a power entity relationship extraction method based on a hybrid neural network. This method aims to overcome the limitations of existing models in accurately representing contextual environment information, thereby improving the accuracy of the extraction model to meet practical application needs. Firstly, a Bidirectional Gated Recurrent Unit (BiGRU) was designed to better capture contextual information in text sequences. This helps the model to better understand the relationships between entities. Secondly, an attention mechanism was adopted to enable the model to automatically focus on sequence features that have a significant impact on relationships. This helps the model to extract entity relationships more accurately in complex text environments. Finally, a segmented convolutional neural network (PCNN) was introduced to further improve the accuracy of relationship extraction by learning the environmental feature information in the adjusted sequence. This enables the model to better understand the contextual relationships between entities. On the publicly available English dataset SemEval2010Task8, this method achieved satisfactory results, achieving an F1 value of 85.62%. These experiments have confirmed the effectiveness of our method, providing new ideas and support for automatic extraction of entity relationships, and are expected to play an important role in the field of information extraction.

178 (PB166): Patient-derived models of ER+ HER2 - metastatic breast cancers reveal KRAS-MAPK signalling pathway as therapeutic target for overcoming resistance to trastuzumab-deruxtecan

178 (PB166): Patient-derived models of ER+ HER2 - metastatic breast cancers reveal KRAS-MAPK signalling pathway as therapeutic target for overcoming resistance to trastuzumab-deruxtecan

Perancangan Sistem Informasi Manajemen Klien Digital Agency PT. XYZ berbasis Website

PT XYZ operates in the service sector and is a creative agency that produces product videos for its clients. During this time PT. XYZ has not implemented a management information system to manage the resources they have and maintain relationships with clients. Ease of access via various platforms is the reason why applications are built based on websites. The stages used in the research correspond to the stages in the software development life cycle (SDLC) in the planning, analysis and design phases. Interviews and literature studies are used to explore the business processes that occur. It was found that there were three types of users, namely super admin, admin, and client. The processes handled by the system include employee registers, client registers, recording incoming orders, project monitoring, and checking employee availability. The diagrams used to explain the system plan that will be created are use case diagrams, activity diagrams, sequence diagrams, and entity relationship diagrams. Lo-fi designs are also created using the Figma application. Further research can continue to the implementation, support and security stages.