Representation Of Knowledge Base Research Articles

A multi-view representation learning framework for commonsense knowledge bases

Commonsense knowledge bases play an essential role in a wide range of natural language processing tasks. This paper studies the problem of representation learning for commonsense knowledge bases to effectively incorporate their knowledge into numerical models. Most existing knowledge base representation learning methods are difficult to apply to commonsense knowledge bases since they are much sparser than general knowledge bases. Hence, in this paper, we propose a novel method for commonsense knowledge base representation learning. Specifically, we first model the nodes from multiple views, including word/phase information, context information, and graph information. Then, we design a scoring function to measure whether the commonsense triplets are established through relation representation learning. We conduct extensive experiments on two tasks and the results show that our proposed model outperforms other knowledge base representation learning methods.

Advancing Chatbot Conversations: A Review of Knowledge Update Approaches

Conversational systems like chatbots have emerged as powerful tools for automating interactive tasks traditionally confined to human involvement. Fundamental to chatbot functionality is their knowledge base, the foundation of their reasoning processes. A pivotal challenge resides in chatbots' innate incapacity to seamlessly integrate changes within their knowledge base, thereby hindering their ability to provide real-time responses. The increasing literature attention dedicated to effective knowledge base updates, which we term content update, underscores the significance of this topic. This work provides an overview of content update methodologies in the context of conversational agents. We delve into the state-of-the-art approaches for natural language understanding, such as language models and alike, which are essential for turning data into knowledge. Additionally, we discuss turning point strategies and primary resources, such as deep learning, which are crucial for supporting language models. As our principal contribution, we review and discuss the core techniques underpinning information extraction as well as knowledge base representation and update in the context of conversational agents.

The Design and Application of the Course "Financial Statement Analysis" Under Hybrid Teaching Mode

Hybrid teaching mode is the organic combination of online and offline teaching, so as to enrich the existing teaching resources and teaching means, and promote the flexible development of course teaching. And the financial statement analysis as train students of accounting basic theory knowledge and application ability of professional courses, teaching purpose is to promote students' understanding of the financial statements, learn to use financial statement analysis and the method of evaluation of enterprises operating results and financial condition, and so on, the traditional teaching mode cannot meet the teaching needs, Therefore, this paper aims to study the design and application of the course "Financial Statement Analysis" in the hybrid teaching mode.

GraphMemDialog: Optimizing End-to-End Task-Oriented Dialog Systems Using Graph Memory Networks

Effectively integrating knowledge into end-to-end task-oriented dialog systems remains a challenge. It typically requires incorporation of an external knowledge base (KB) and capture of the intrinsic semantics of the dialog history. Recent research shows promising results by using Sequence-to-Sequence models, Memory Networks, and even Graph Convolutional Networks. However, current state-of-the-art models are less effective at integrating dialog history and KB into task-oriented dialog systems in the following ways: 1. The KB representation is not fully context-aware. The dynamic interaction between the dialog history and KB is seldom explored. 2. Both the sequential and structural information in the dialog history can contribute to capturing the dialog semantics, but they are not studied concurrently. In this paper, we propose a novel Graph Memory Network (GMN) based Seq2Seq model, GraphMemDialog, to effectively learn the inherent structural information hidden in dialog history, and to model the dynamic interaction between dialog history and KBs. We adopt a modified graph attention network to learn the rich structural representation of the dialog history, whereas the context-aware representation of KB entities are learnt by our novel GMN. To fully exploit this dynamic interaction, we design a learnable memory controller coupled with external KB entity memories to recurrently incorporate dialog history context into KB entities through a multi-hop reasoning mechanism. Experiments on three public datasets show that our GraphMemDialog model achieves state-of-the-art performance and outperforms strong baselines by a large margin, especially on datatests with more complicated KB information.

Abstract 210: Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration

Abstract Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kesserwan A. Chimene, Laura Fuqua, Lisa Dyer, Huiling Xu, Jeffrey Jean, Laveniya Satgunaseelan, Liying Zhang, Ted W. Laetsch, Donald W. Parsons, Ryan Schmidt, Lynn M. Schriml, Kristen L. Sund, Shashikant Kulkarni, Subha Madhavan, Xinjie Xu, Rashmi Kanagal-Shamana, Marian Harris, Yasmine Akkari, Nurit Paz Yacov, Panieh Terraf, Malachi Griffith, Obi L. Griffith, Gordana Raca. Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 210.

VICAI System for Teaching Computer Engineering.(Dept.E)

This paper introduces a Visual Intelligent Computer Aided Instruction (VICAI) System It consists of three subsystems, the first concerns with the personal computer hardware and architecture (80386SX processor). The second subsystem is focused on its peripherals. While the third subsystem gives advices for repairing any troubleshooting that might occurs in personal computer systems. This is done through a dialogue between the system and the user. For surface and deep representation of knowledge base. The system is designed using both rules and frames. Also, the system is highly use visual display and color graphics which are likely accepted by the user.

Database, Registries and Monitoring of Congenital and Genetic Malformation.(Dept.E)

With technical and industrial advances which have become a significant part of today's economic growth, remarkable advances have been made in the development and use of technical capabilities that enhance the endeavor of the medical profession. The developed countries have adopted the essential ''corner stone'' of database and registries development for monitoring birth defects. The use of National and Regional Registries has become ''a must'' to have a database that is necessary for rehabilitation, control and prevention programs. In Saudi Arabia, towards this objective, a study to establish a database and national registry to monitor birth defects and determine frequency, type, distribution and magnitude of the problem has been initiated in coordination with the Department of Human Genetics and the Computer department at the National Research Center aiming at developing an Arab Registry for Congenital and Genetic Malformation. The Prince Center for Disability Research (PSCDR)as well as the outpatient clinic for the human genetics department have developed a comprehensive research plan to study various aspects of handicaps and to assess the present care delivery status and consequently devise the most appropriate and applicable plan of care. Specific forms have been designed or collection of relevant information. The completeness and accuracy of reporting is ascertained by close supervision and adequate training of the team (composed of doctors, nurses and social workers) and a system by which uniformity of diagnosis can be curtained. It is expected that the data derived from this study will establish a regional registries of the handicapped, incorporating congenital and genetic anomalies in Arabs and will provide the basis for further research in prevention and care programs combating the handicap problem. Towards this goal, this paper uses a pediatric knowledge base acquired from medical geneticists and proposes a system containing mainly three modules a knowledge base module, a knowledge processing module, and an interface module. This endeavor concentrates mainly on the genetic symptoms knowledge base representation that allows the knowledge processing module to perform diagnosis advise according to the abnormal features and assist in detecting new syndromes. The system can be extended and gene or the medical problem domains.

Predictive approaches for the UNIX command line: curating and exploiting domain knowledge in semantics deficit data

The command line has always been the most efficient method to interact with UNIX flavor based systems while offering a great deal of flexibility and efficiency as preferred by professionals. Such a system is based on manually inputting commands to instruct the computing machine to carry out tasks as desired. This human-computer interface is quite tedious especially for a beginner. And hence, the command line has not been able to garner an overwhelming reception from new users. Therefore, to improve user-friendliness and to mark a step towards a more intuitive command line system, we propose two predictive approaches that can benefit all kinds of users specially the novice ones by integrating into the command line interface. These methods are based on deep learning based predictions. The first approach is based on the sequence to sequence (Seq2seq) model with joint learning by leveraging continuous representations of a self-curated exhaustive knowledge base (KB) comprising an all-inclusive command description to enhance the embedding employed in the model. The other is based on the attention-based transformer architecture where a pretrained model is employed. This allows the model to dynamically evolve over time making it adaptable to different circumstances by learning as the system is being used. To reinforce our idea, we have experimented with our models on three major publicly available Unix command line datasets and have achieved benchmark results using GLoVe and Word2Vec embeddings. Our finding is that the transformer based framework performs better on two different datasets of the three in our experiment in a semantic deficit scenario like UNIX command line prediction. However, Seq2seq based model outperforms bidirectional encoder representations from transformers (BERT) based model on a larger dataset.

Course of Engineering Project Management for Automatic Engineering

For the new requirements of engineering certification, a modular teaching plan of engineering project management is proposed that is also combined with the professional features of vehicle engineering. Specifically, the teaching objectives of this course are established by engineering certification. Meanwhile, the course content is modularized and adopted different teaching method, such as lecture, interactive module, heuristic teaching method, etc. The improvement plan of course fully focuses on student-centered and output-oriented principle which strengthens the initiative of students to master basic theory knowledge. And it is helpful for students to have the ability to meet the engineering certification standards.

A Bibliometric Analysis and Visualization of Passive Optical Network Research in the Last Decade

Passive optical networks (PONs) are the most successful broadband access solutions for its wide bandwidth, low-cost deployment worldwide, and maintenance. In the last decade, the diverse and prevalent research related to PONs sustains 330+ science citation index (SCI) articles per year, with 9.18 average citations per article, making it "big literature data". This article, for the first time, presents the graphical representation of knowledge base, knowledge domain, and knowledge evolution of PON research using co-citation analysis based on 3381 SCI publications worldwide from 2010 to 2019 in bibliometric visualization tool- CiteSpace. The bibliographic analysis focused on 528 important research articles, 34 hotspots, 155 research frontier keywords, 481 core authors, 185 cited authors, 58 countries, 254 institutions, and 118 journals. Based on the frequency of keywords, Modulation, WDM PON, Transmission, Semiconductor optical amplifier, and Dynamic bandwidth allocation are the dominant and turning points in the structural basis of PON research. The most prominent research hotspots appearing in recent years are Energy efficiency, NG-EPON, and Chaotic encryption. The trend of future research on PONs is from TWDM PON issues, fronthaul implementation in NG-PON2, 5G-PON and encryption methods for physical layer security. The leading core authors, institutes, countries, and journals were also identified related to the PON research. This analysis would serve as a comprehensive guide on the status and future research trends on PONs for the aspirant researchers.

An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering

Question Answering (QA) requires understanding of queries expressed in natural languages and identification of relevant information content to provide an answer. For closed-world QAs, information access is obtained by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems. Our study of multiple question answering methods finds that the neural networks are able to produce state-of-art results in different question answering domains, and inclusion of additional information via KB embeddings further improve the performance of such approaches. Further progress in QA can be improved by incorporating more powerful representations of KBs.

Nursing students' first-time experiences in pediatric clinical practice in Taiwan: A qualitative study

BackgroundA pediatric nursing practicum is essential in equipping students with basic knowledge of theory, professional attitudes, and skills for the clinical setting. However, students often face multiple challenges in the practicum due to gaps between theory and practice. PurposeTo describe nursing students' first-time experiences in pediatric clinical practice in Taiwan. MethodsA phenomenological approach with purposive sampling was used. Twenty participants were interviewed individually, in person, within a month of completion of a pediatric nursing practicum. Interviews were semi-structured and digitally recorded. Data analysis followed Colaizzi's methods; epochs (bracketing) focused analysis on students' experiences and maintained objectivity. FindingsThree themes and related subthemes emerged: (a) orienting to the pediatric unit (becoming familiar with common treatments and surroundings in the pediatric unit; recognizing people in the unit); (b) encountering challenges in pediatric and family-centered care (navigating communication between child and families; student nurse-patient relationships: maintaining a good rapport with children and their families; being prepared for quick role changes); (c) translating knowledge into clinical practice thoughtfully (providing desirable and correct responses for children and their families; providing a safe and friendly environment for children and their families). ConclusionsStudent nurses engaged in a variety of care practices necessitated by patients' differing ages, developmental levels, and family needs. Ensuring students' successful completion of the demanding pediatric practicum, including innovative communication, technical skills, and role transitions, is challenging yet achievable. Clinical relevanceGaining a fuller understanding of nursing students' experiences in the pediatric nursing practicum can assist nursing professionals in preparing students to provide competent care for children and their families.

Автоматизований аналіз стану пацієнта та оцінка сеансів медичних процедур (гіпокситерапії)

The task of creating an expert system for conducting an automated assessment of patients' status and analyzing the results of conducting medical sessions is examined. This system is part of an automated software and hardware complex for hypoxic therapy. The paper describes the proposed architecture of the expert system and the interaction of its main modules. The developed formal model of representation of the knowledge base is described. This model is based on product rules. The proposed Knowledge Base representation allows for the presentation of rules with different levels of nesting conditions and consequences, as well as maintaining rules using MySQL. The model is adapted to use the Rete algorithm. The article describes the output mechanism through the «mapping-operation» procedure and the Rete algorithm, which is a quick comparison tool with the template. The created expert system conducts an automated assessment of patients' condition and analysis of diagnostic sessions and course of treatment based on parameters of the respiratory and cardiovascular system of a person. This expert system has improved the effectiveness and safety of procedures for hypoxic therapy, which in the future ensure the use of automated software and hardware for hypoxic therapy, not only in medical centers, but also at home. Also, this system can work with other medical devices or as an independent program for assessing medical procedures and providing medical advice. The main improvements will be to fill the knowledge base with the necessary rules and facts. The paper presents the algorithm's work and the presentation of the rules on the example of the evaluation of the diagnostic session.

Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process

Knowledge discovery with geo-spatial information processing is of prime importance in geomorphology. The temporal characteristics of evolving geographic features result in geo-spatial events that occur at a specific geographic location. Those events when consecutively occur result in a geo-spatial process that causes a phenomenal change over the period of time. Event and process are essential constituents in geo-spatial dynamism. The geo-spatial data acquired by remote sensing technology is the source of input for knowledge discovery of geographic features. This article performs qualitative inference of geographic process by identifying events causing geo-spatial deformation over time. The evolving geographic features and their types have association with spatial and temporal factors. Event calculus-based spatial knowledge formalism allows reasoning over intervals of time. Hence, representation of Event Attributed Spatial Entity (EASE) Knowledge is proposed. Logical event-based queries are evaluated on the formal representation of EASE Knowledge Base. Event-based queries are executed on the proposed knowledge base and when experimented on, real data sets yielded comprehensive results. Further, the significance of EASE-based spatio-temporal reasoning is proved by evaluating with respect to query processing time and accuracy. The enhancement of EASE with a direction for further development to explore its significance towards prediction is discussed towards the end.

Identifying the ‘knowledge base’ or ‘intellectual structure’ of research on international business, 2000–2015: A citation/co-citation analysis of JIBS

The purpose of this study consists in analyzing the existing literature on international business from all the research papers published in Journal of International Business Studies (JIBS) during the period of time comprised between 2000 and 2015. The authors apply bibliometric methods to that end –in particular, document citation and co-citation analyses (DCAs)– so that they can try and identify the main research lines within the aforementioned scientific field or domain; in other words, the ‘intellectual structure’ of the discipline examined. Social networks analysis was also used to carry out the representation or visualization of such structure or knowledge base. The highly interdisciplinary nature of the research undertaken, since the study field still keeps its characteristic diversity and interdisciplinarity or the need to integrate a variety of theoretical frameworks or approaches into the development of a widely-accepted theory about the field under study become evident. Amongst these theories would be the new institutional theory, the strategic theory or perspective or others such as the transaction cost theory or the agency theory, applied to the context of internationalization and to the field of international business, on whose integration the future development of this field may largely depend.

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.

CASOA: An Architecture for Agent-Based Manufacturing System in the Context of Industry 4.0

The fourth industrial revolution involves the advanced topics, such as industrial Internet of Things, cyber-physical system and smart manufacturing that address increasing demands for mass customized manufacturing. The agent-based manufacturing is a highly distributed control paradigm that can cope with these challenges well. This paper gives an overview of agent-based architectures for manufacturing systems. Besides, a cloud-assisted self-organized architecture is presented by comprising smart agents and cloud to communicate and negotiate through networks. Ontological representations of knowledge base are constructed to provide the information basis for decision-making of agents, which enables dynamic reconfiguration among agents in a collaborative way to achieve agility and flexibility. Furthermore, the agents’ interaction behavior is modeled to structure the agents hierarchically to reduce the complexity, because the interactions among agents in distributed system are difficult to understand and predict. The experimental results show that the presented architecture can be easily deployed to build smart manufacturing system and can improve the adaptiveness and robustness of the manufacturing system when dealing with mixed multi-product tasks.

Learning Distributed Representations of Texts and Entities from Knowledge Base

We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.

Development of Distributed Complex Network for Representation of Knowledge Base

Development of Distributed Complex Network for Representation of Knowledge Base

Literatures analysis on standardized training in new recruited nurses

Objective To analyse the literatures characteristics about the recruited accepting and to compare the differences between the Standardized Training Program of New Nurses, so as to provide suggestions for nursing managers. Methods The literatures were recruited by putting the new nurses, recruited nurses, junior nurses and standardized training, training as the key words in CNKI, WANFANG to retrieve relevant literatures from January 2011 to December 2015 and were analysed by the content analysis method and bibliometrics method. Results A total of 126 literatures were included, in which core periodicals, qualitative reasearches, tertiary hospitals accounted for 18.25%, 13.49%, 74.6%, respectively. The content characteristics results are below: the content is incomplete; the rates of reaching basic theory knowledge standard, 27 items of clinical nursing operations standard, special theory knowledge and operations standard are 39.68%, 0, 45.24% respectively; the lecture and practice course achieved 100% compliance rate; the percent of group discussion, clinical ward round, simulation were 13.49%, 23.02%, 19.84% respectively; the case nursing was 9 for compliance rate; the examination index was short for patient and case nursing; the graduation examination acquired 100% compliance rate; the percent of process evaluations was 21.43%. The time was messy and the requirements were lack of integrity. Conclusions There are some differences between the current situations and the Guideline of New Nurses Being Trained. The content, the methods, the time, the indexes of checking should be unified according to Guideline, to promote the homogenizing outcome. Key words: New recruited nurse; Standardized training; Literature analysis