• Title/Summary/Keyword: Entity

Search Result 2,088, Processing Time 0.032 seconds

A Modeling of XML Document Preserving Object-Oriented Concepts

  • Kim, Chang Suk;Kim, Dae Su;Son, Dong Cheul
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.4 no.2
    • /
    • pp.129-134
    • /
    • 2004
  • XML is the new universal format for structured documents and data on the World Wide Web. As the Web becomes a major means of disseminating and sharing information and as the amount of XML data increases substantially, there are increased needs to manage and design such XML document in a novel yet efficient way. Moreover a demand of XML Schema(W3C XML Schema Spec.) that verifies XML document becomes increasing recently. However, XML Schema has a weak point for design because of its complication despite of various data and abundant expressiveness. Thus, it is difficult to design a complex document reflecting the usability, global and local facility and ability of expansion. This paper shows a simple way of modeling for XML document using a fundamental means for database design, the Entity-Relationship model. The design from the Entity-Relationship model to XML Schema can not be directly on account of discordance between the two models. So we present some algorithms to generate XML Schema from the Entity-Relationship model. The algorithms produce XML Schema codes using a hierarchical view representation. An important objective of this modeling is to preserve XML Schema's object-oriented concepts such as reusability, global and local ability. In addition to, implementation procedure and evaluation of the proposed design method are described.

A study on the improvement of BCM industry through legal systems (BCM(재해경감활동관리)산업 활성화를 위한 법·제도 개선 방안 연구)

  • Han, Jong-U
    • Disaster and Security
    • /
    • v.5 no.1
    • /
    • pp.93-100
    • /
    • 2015
  • Although many years passed since 'The Legislative bill on the support of voluntary activities of enterprises for disaster reduction'(hereinafter referred to as 'enterprise disaster reduction act') has been first enacted in 2007, BCMS is still not activated in our society. In contrast, after 911 Terror, importance of BCM is getting magnified and standardization research & institutionalization i s a lso proceeding i all over world. Lately, Disaster preventing activities is urgently needed like the sinking of 'Sewol ferry'. So the purpose of this paper is proposed for establishment of 'BCMS' and activation of the certificate system for Best-Run Business by analyzing the problem of 'enterprise disaster reduction act' and weak of activation as following. First, propel changing the policy of self-regulated participation to mandatory about the certificate system for Best-Run Business from public entity to government ministry and it is able to activate by propelling demo business of the certificate system for Best-Run Business. Second, public entity that has been given the certificate system for Best-Run Business by affiliating with Disaster Management Assessment of government management can be exempted from Disaster Management Assessment or those entity can arrange for connectivity acquisition method of 'Excellent rate'. Third, to publicize the activation of the law mentioned above, makes public entity r ecognizable by incorporating 'BCMS' into National safety management plan and establishment of National critical infrastructures security plan. Fourth, it should be reviewed to improving the related act regarding to inclusion of public organizations as well as private enterprises.

  • PDF

Generation of 3D Model and Drawing of Rotor Using 2D Entity Groups with Attributes (속성이 부여된 2차원 엔터티 그룹을 이용한 로터의 3차원 모델 및 도면 생성)

  • Kim, Yeoung-Il
    • Journal of the Korean Society of Manufacturing Process Engineers
    • /
    • v.18 no.8
    • /
    • pp.91-97
    • /
    • 2019
  • A method for generating 3D solid models and drawings for a rotor in the steam turbine is proposed. One of the most important design steps is generating the drawing for manufacturing it. This step is a very routine and time-consuming job because each drawing is composed of several kinds of views and many dimensions. To achieve automation for this activity, rotor profiles are composed of 2D entity groups with attributes. Based on this, the improved design process is developed as follows. First, the rotor profiles can be selected by searching for 2D entity groups using the related attributes. Second, the profiles are connected sequentially so that an entire rotor profile is determined. The completed profile is used to generate 2D drawings automatically, especially views, dimensions, and 3D models. The proposed method is implemented using a commercial CAD/CAM system, Unigraphics, and API functions written in C-language and applied to the rotor of steam turbines. Some illustrative examples are provided to show the effectiveness of the proposed method.

Conceptual Data Modeling: Entity-Relationship Models as Thinging Machines

  • Al-Fedaghi, Sabah
    • International Journal of Computer Science & Network Security
    • /
    • v.21 no.9
    • /
    • pp.247-260
    • /
    • 2021
  • Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims to provide richer expressiveness and incorporate a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate the viewing of connections and ideas on a database. The described structure of the data is often represented in an entity–relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes, and relationships. This paper attempts to examine the basic ER modeling notions in order to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships, and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

Improving methods for normalizing biomedical text entities with concepts from an ontology with (almost) no training data at BLAH5 the CONTES

  • Ferre, Arnaud;Ba, Mouhamadou;Bossy, Robert
    • Genomics & Informatics
    • /
    • v.17 no.2
    • /
    • pp.20.1-20.5
    • /
    • 2019
  • Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.

A review of Chinese named entity recognition

  • Cheng, Jieren;Liu, Jingxin;Xu, Xinbin;Xia, Dongwan;Liu, Le;Sheng, Victor S.
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.15 no.6
    • /
    • pp.2012-2030
    • /
    • 2021
  • Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition

  • Liu, Jingxin;Cheng, Jieren;Peng, Xin;Zhao, Zeli;Tang, Xiangyan;Sheng, Victor S.
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.16 no.6
    • /
    • pp.1833-1848
    • /
    • 2022
  • Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

Entity Matching Method Using Semantic Similarity and Graph Convolutional Network Techniques (의미적 유사성과 그래프 컨볼루션 네트워크 기법을 활용한 엔티티 매칭 방법)

  • Duan, Hongzhou;Lee, Yongju
    • The Journal of the Korea institute of electronic communication sciences
    • /
    • v.17 no.5
    • /
    • pp.801-808
    • /
    • 2022
  • Research on how to embed knowledge in large-scale Linked Data and apply neural network models for entity matching is relatively scarce. The most fundamental problem with this is that different labels lead to lexical heterogeneity. In this paper, we propose an extended GCN (Graph Convolutional Network) model that combines re-align structure to solve this lexical heterogeneity problem. The proposed model improved the performance by 53% and 40%, respectively, compared to the existing embedded-based MTransE and BootEA models, and improved the performance by 5.1% compared to the GCN-based RDGCN model.

OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition

  • Larmande, Pierre;Liu, Yusha;Yao, Xinzhi;Xia, Jingbo
    • Genomics & Informatics
    • /
    • v.19 no.3
    • /
    • pp.27.1-27.4
    • /
    • 2021
  • Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pretrained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.

Improving classification of low-resource COVID-19 literature by using Named Entity Recognition

  • Lithgow-Serrano, Oscar;Cornelius, Joseph;Kanjirangat, Vani;Mendez-Cruz, Carlos-Francisco;Rinaldi, Fabio
    • Genomics & Informatics
    • /
    • v.19 no.3
    • /
    • pp.22.1-22.5
    • /
    • 2021
  • Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.