• Journal of Industrial Technology
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• v.25 no.A
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• pp.123-131
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• 2005

Semantic object model has widely been recognized as an alternative data modeling approach to entity-relationship model for database system design. In this study, we have presented a semantic object model for intermediary type shopping mall consisting of multiple buyers and sellers. Essential processes and information with regard to the customer management, product management, price estimation, product order etc. have been considered for this study. Upon careful examination and analysis of them, a detailed semantic objects and attributes have been drawn and structured into semantic object diagrams. The final objects were converted into an entity-relationship diagram so that intuitive comparison could be made for relational database design. The results in this study may form a conceptual framework for both academic concerns and more complicated system applications.

• Genomics & Informatics
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• v.17 no.2
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• pp.20.1-20.5
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• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 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.

• Genomics & Informatics
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• v.18 no.2
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• pp.15.1-15.6
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• 2020

Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched text and will miss important information or identify spurious text that will frustrate users. Most tools do not capture non-contiguous entities which are separate spans of text that together refer to an entity, e.g., the entity "type 1 diabetes" in the phrase "type 1 and type 2 diabetes." This type is commonly found in biomedical texts, especially in lists, where multiple biomedical entities are named in shortened form to avoid repeating words. Most text annotation systems, that enable users to view and edit entity annotations, do not support non-contiguous entities. Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. This enables users to add new subspans to existing entities by selecting additional text. We integrate this new functionality with TextAE's existing editing functionality to allow easy changes to entity annotation and editing of relation annotations involving non-contiguous entities, with importing and exporting to the PubAnnotation format. Finally, we roughly quantify the problem across the entire accessible biomedical literature to highlight that there are a substantial number of non-contiguous entities that appear in lists that would be missed by most text mining systems.

• Journal of Chest Surgery
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• v.26 no.9
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• pp.722-725
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• 1993

Multiple primary lung cancer is a rare disease entity and its clinical characteristics, treatment, and prognosis are poorly described. But the multiple primary lung cancer have a more favorable prognosis than locally recurrent or metastastic disease. Therefore, appropriate identification of multiple primary lung cancer will be very important. We have experienced a case of stage I multiple primary lung cancer in a 76-year-old male with two large mass in the right lower lobe without metastasis in the mediastinal lymph nodes with right mid and lower lobectomy. The microscopic pictures revealed adenocarcinoma in the one & small cell carcinoma in another. The post-operative courses were in uneventful for 4 months & but he was treated with chemotherapies, 2 times for complete remission of small cell carcinoma to now after discharge.

• Journal of Korean Neurosurgical Society
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• v.30 no.6
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• pp.786-789
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• 2001

Alveolar Soft Part Sarcoma(ASPS) is a rare entity that invariably ends in death from the disseminated disease. Although the most common site of metastasis is the lung, the central nervous system is also the third common site. Its histogenesis remains to be unknown and the gold standard treatment is radically surgical removal of the mass. However, adjuvant chemotherapy and radiotherapy are known to be less effective. The authors present a 24-year-old man who was admitted with headache and neck discomfort. Magnetic resonance( MR) imaging scans demonstrated multiple masses in the left frontal lobe, parietal lobe, and right cerebellum. The patient underwent surgery to remove multiple masses in the staged fashion. The postoperative course was uneventful, but the patient committed suicide 5 months later. The authors reviewed the pertinent literature and discussed this clinical entity with references.

• International Journal of Contents
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• v.8 no.4
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• pp.21-29
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• 2012

It is not easy for users to effectively have information that is semantically related but scattered on the Web. To obtain qualitatively improved information in web pages, it is necessary to integrate information that is heterogeneous but semantically related. In this study, we propose a method that provides XML-based metadata to users through integration of multiple heterogeneous Web pages. The metadata generated from the proposed system is obtained by integrating different heterogeneous information into a single page, using entity identification based on ontology. A wheelchair information integration system for disabled people is implemented to verify the efficiency of the proposed method. The implemented system provides an integrated web page from multiple web pages as a type of XML document.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.143-152
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• 2004

Recently, software programming method based on EJB for object oriented software design and implement has been used frequently. Usually, case that use permanent data that use Database in EJB base application is most. Part connected with Database-Access that take charge in Entity Bean class of server side creation program. In this paper using J2EE relationship DAO pattern class each separate. This is no much difference with existent pattern method, but in same pattern common classes are designed so that composition may be possible. And as well as use Entity Bean class that created each DBMS classes are different, is doing Rata Source so that connection work is available without alteration or creation of additional program in several DBMS environments.

• CELLMED
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• v.5 no.4
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• pp.27.1-27.6
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• 2015

Literatures and experimental studies have shown that Prunella has an effect on anti-hypertension, however, its components are complicated, so that it is still difficult to clear the specific roles of its various components in blood pressure regulation in. So we decide to systematically study the anti-hypertension mechanism of Prunella. We integrated multiple databases and constructed molecular interaction network between the chemical constituents of Prunella Vulgaris and hypertension based on entity grammar systems model. The network has 262 nodes and 802 edges. Then we infer the interactions between chemical compositions and disease targets to clarify the anti-hypertension mechanism. Finally, we found Prunella could influence hypertension by regulating apoptosis, cell proliferation, blood vessel development and vasoconstriction, etc. Thus this study provides reference for drug development and compatibility, and also gives guidance for health care at a certain extent.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.4
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• pp.277-284
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• 2012

Named entity recognition (NER) technique can play a crucial role in extracting information from the web. While NER systems with relatively high performances have been developed based on careful manipulation of terms with a statistical model, term mismatches often degrade the performance of such systems because the strings of all the candidate entities are not known a priori. Despite the importance of lexical-level term mismatches for NER systems, however, most NER approaches developed to date utilize only the term string itself and simple term-level features, and do not exploit the semantic features of terms which can handle the variations of terms effectively. As a solution to this problem, here we propose to match the semantic concepts of term units in restaurant named entities (NEs), where these units are automatically generated from multiple resolutions of a semantic tree. As a test experiment, we applied our restaurant NER scheme to 49,153 nouns in Korean restaurant web pages. Our scheme achieved an average accuracy of 87.89% when applied to test data, which was considerably better than the 78.70% accuracy obtained using the baseline system.