2013 IEEE Seventh International Conference on Semantic Computing最新文献_第2页

Proposed Testing Infrastructure for Automation of the GPU Chip Validation: Leading to Painless Driver Development 提议的GPU芯片验证自动化测试基础设施:导致无痛的驱动程序开发

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.77

Akash Kulkarni

引用次数: 0

Extracting Cybersecurity Related Linked Data from Text 从文本中提取网络安全相关关联数据

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.50

Arnav Joshi, R. Lal, Timothy W. Finin, A. Joshi

引用次数: 133

Mapping Natural Language Questions to SPARQL Queries for Job Search 将自然语言问题映射到SPARQL查询用于求职

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.35

Naila Karim, K. Latif, N. Ahmed, Mishal Fatima, Atif Mumtaz

引用次数: 3

Semantic Computing and Drug Discovery - A Preliminary Report 语义计算和药物发现-初步报告

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.86

Charles C. N. Wang, D. Hecht, P. Sheu, J. Tsai

引用次数: 6

Semantic Entity Search Diversification 语义实体搜索多样化

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.16

Tuukka Ruotsalo, Matias Frosterus

引用次数: 3

Semantic Medical Prescriptions -- Towards Intelligent and Interoperable Medical Prescriptions 语义医学处方——走向智能和互操作的医学处方

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.66

A. Khalili, B. Sedaghati

{"title":"Semantic Medical Prescriptions -- Towards Intelligent and Interoperable Medical Prescriptions","authors":"A. Khalili, B. Sedaghati","doi":"10.1109/ICSC.2013.66","DOIUrl":"https://doi.org/10.1109/ICSC.2013.66","url":null,"abstract":"Medication errors are the most common type of medical errors in health-care domain. The use of electronic prescribing systems (e-prescribing) have resulted in significant reductions in such errors. However, dealing with the heterogeneity of available information sources is still one of the main challenges of e-prescription systems. There already exists different sources of information addressing different aspects of pharmaceutical research (e.g. chemical, pharmacological and pharmaceutical drug data, clinical trials, approved prescription drugs, drugs activity against drug targets. etc.). Handling these dynamic pieces of information within current e-prescription systems without bridging the existing pharmaceutical information islands is a cumbersome task. In this paper we present semantic medical prescriptions which are intelligent e-prescription documents enriched by dynamic drug-related meta-data thereby know about their content and the possible interactions. Semantic prescriptions provide an interoperable interface which helps patients, physicians, pharmacists, researchers, pharmaceutical and insurance companies to collaboratively improve the quality of pharmaceutical services by facilitating the process of shared decision making. In order to showcase the applicability of semantic prescriptions we present an application called Pharmer. Pharmer employs datasets such as DBpedia, Drug Bank, Daily Med and RxNorm to automatically detect the drugs in the prescriptions and to collect multidimensional data on them. We evaluate the feasibility of the Pharmer by conducting a usability evaluation and report on the quantitative and qualitative results of our survey.","PeriodicalId":189682,"journal":{"name":"2013 IEEE Seventh International Conference on Semantic Computing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125074544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Open Information Extraction via Contextual Sentence Decomposition 基于上下文句子分解的开放信息提取

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.36

H. Bast, Elmar Haussmann

引用次数: 64

Getting Creative with Semantic Similarity 利用语义相似度获得创造性

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.63

Ching-Yun Chang, S. Clark, Brian Harrington

引用次数: 5

Semantic Retrieval for Videos in Non-static Background Using Motion Saliency and Global Features 基于运动显著性和全局特征的非静态背景下视频语义检索

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.57

Dianting Liu, M. Shyu

{"title":"Semantic Retrieval for Videos in Non-static Background Using Motion Saliency and Global Features","authors":"Dianting Liu, M. Shyu","doi":"10.1109/ICSC.2013.57","DOIUrl":"https://doi.org/10.1109/ICSC.2013.57","url":null,"abstract":"In this paper, a video semantic retrieval framework is proposed based on a novel unsupervised motion region detection algorithm which works reasonably well with dynamic background and camera motion. The proposed framework is inspired by biological mechanisms of human vision which make motion salience (defined as attention due to motion) is more \"attractive\" than some other low-level visual features to people while watching videos. Under this biological observation, motion vectors in frame sequences are calculated using the optical flow algorithm to estimate the movement of a block from one frame to another. Next, a center-surround coherency evaluation model is proposed to compute the local motion saliency in a completely unsupervised manner. The integral density algorithm is employed to search the globally optimal solution of the minimum coherency region as the motion region which is then integrated into the video semantic retrieval framework to enhance the performance of video semantic analysis and understanding. Our proposed framework is evaluated using video sequences in non-static background, and the promising experimental results reveal that the semantic retrieval performance can be improved by integrating the global texture and local motion information.","PeriodicalId":189682,"journal":{"name":"2013 IEEE Seventh International Conference on Semantic Computing","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114279563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Analysis of Big Data Technologies and Method - Query Large Web Public RDF Datasets on Amazon Cloud Using Hadoop and Open Source Parsers 大数据技术和方法分析-使用Hadoop和开源解析器在Amazon Cloud上查询大型Web公共RDF数据集

2013 IEEE Seventh International Conference on Semantic Computing Pub Date : 2013-09-16 DOI: 10.1109/ICSC.2013.49

Ted Garcia, Taehyung Wang

{"title":"Analysis of Big Data Technologies and Method - Query Large Web Public RDF Datasets on Amazon Cloud Using Hadoop and Open Source Parsers","authors":"Ted Garcia, Taehyung Wang","doi":"10.1109/ICSC.2013.49","DOIUrl":"https://doi.org/10.1109/ICSC.2013.49","url":null,"abstract":"Extremely large datasets found in Big Data projects are difficult to work with using conventional databases, statistical software, and visualization tools. Massively parallel software, such as Hadoop, running on tens, hundreds, or even thousands of servers is more suitable for Big Data challenges. Additionally, in order to achieve the highest performance when querying large datasets, it is necessary to work these datasets at rest without preprocessing or moving them into a repository. Therefore, this work will analyze tools and techniques to overcome working with large datasets at rest. Parsing and querying will be done on the raw dataset - the untouched Web Data Commons RDF files. Web Data Commons comprises five billion pages of web pages crawled from the Internet. This work will analyze available tools and appropriate methods to assist the Big Data developer in working with these extremely large, semantic RDF datasets. Hadoop, open source parsers, and Amazon Cloud services will be used to data mine these files. In order to assist in further discovery, recommendations for future research will be included.","PeriodicalId":189682,"journal":{"name":"2013 IEEE Seventh International Conference on Semantic Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124625938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13