Proceedings. IEEE International Conference on Semantic Computing最新文献

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Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals. 针对酒精使用障碍治疗转诊的大二部网络数据开发高性能网络计算。
Proceedings. IEEE International Conference on Semantic Computing Pub Date : 2025-02-01 Epub Date: 2025-06-19 DOI: 10.1109/icsc64641.2025.00044
Muhammad Tuan Amith, Sharon Andrews, Angela Heads, Bruno Kluwe-Schiavon, Atchyutha Choday, Ramya Poonam, Sai Venkat Ballem, Cui Tao, Jane Hamilton
{"title":"Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals.","authors":"Muhammad Tuan Amith, Sharon Andrews, Angela Heads, Bruno Kluwe-Schiavon, Atchyutha Choday, Ramya Poonam, Sai Venkat Ballem, Cui Tao, Jane Hamilton","doi":"10.1109/icsc64641.2025.00044","DOIUrl":"10.1109/icsc64641.2025.00044","url":null,"abstract":"<p><p>Electronic health care records offer big data to mine and analyze towards improving public health outcomes. The information extracted, specifically social network data, could help us understand the primary care referrals for patients experiencing alcohol use disorder and wield that knowledge to better inform the engagement of this patient population. <i>Network exposure</i> and <i>affiliation exposure</i> models are two metrics that can be utilized to analyze the influence of social networks. We developed a core software library that address the scalability issue of our previous work. Our library computed high volume, randomly generated network graphs that range from 500-10,000 nodes (~126,000-40 million edges). This C library can be integrated with our previous work to handle high volume network data. Future plans include providing support for variant network exposure models and interfaces towards big network data analytics.</p>","PeriodicalId":89468,"journal":{"name":"Proceedings. IEEE International Conference on Semantic Computing","volume":"2025 ","pages":"253-258"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Speeding up Batch Alignment of Large Ontologies Using MapReduce. 使用MapReduce加速大型本体的批量对齐。
Proceedings. IEEE International Conference on Semantic Computing Pub Date : 2013-09-01 DOI: 10.1109/ICSC.2013.28
Uthayasanker Thayasivam, Prashant Doshi
{"title":"Speeding up Batch Alignment of Large Ontologies Using MapReduce.","authors":"Uthayasanker Thayasivam,&nbsp;Prashant Doshi","doi":"10.1109/ICSC.2013.28","DOIUrl":"https://doi.org/10.1109/ICSC.2013.28","url":null,"abstract":"<p><p>Real-world ontologies tend to be very large with several containing thousands of entities. Increasingly, ontologies are hosted in repositories, which often compute the alignment between the ontologies. As new ontologies are submitted or ontologies are updated, their alignment with others must be quickly computed. Therefore, aligning several pairs of ontologies quickly becomes a challenge for these repositories. We project this problem as one of batch alignment and show how it may be approached using the distributed computing paradigm of MapReduce. Our approach allows any alignment algorithm to be utilized on a MapReduce architecture. Experiments using four representative alignment algorithms demonstrate flexible and significant speedup of batch alignment of large ontology pairs using MapReduce.</p>","PeriodicalId":89468,"journal":{"name":"Proceedings. IEEE International Conference on Semantic Computing","volume":"2013 ","pages":"110-113"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICSC.2013.28","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32818116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Adopting Graph Traversal Techniques for Context-Driven Value Sets Extraction from Biomedical Knowledge Sources. 基于图遍历技术的生物医学知识上下文驱动值集提取。
Proceedings. IEEE International Conference on Semantic Computing Pub Date : 2008-08-12 DOI: 10.1109/ICSC.2008.76
Jyotishman Pathak, Guoqian Jiang, Sridhar O Dwarkanath, James D Buntrock, Christopher G Chute
{"title":"Adopting Graph Traversal Techniques for Context-Driven Value Sets Extraction from Biomedical Knowledge Sources.","authors":"Jyotishman Pathak, Guoqian Jiang, Sridhar O Dwarkanath, James D Buntrock, Christopher G Chute","doi":"10.1109/ICSC.2008.76","DOIUrl":"10.1109/ICSC.2008.76","url":null,"abstract":"<p><p>The ability to model, share and re-use value sets across multiple medical information systems is an important requirement. However, generating value sets semi-automatically from a terminology service is still an unresolved issue, in part due to the lack of linkage to clinical context patterns that provide the constraints in defining a concept domain and invocation of value sets extraction. Towards this goal, we develop and evaluate an approach for context-driven automatic value sets extraction based on a formal terminology model. The crux of the technique is to identify and define the context patterns from various domains of discourse and leverage them for value set extraction using two complementary ideas based on (i) local terms provided by the subject matter experts (extensional) and (ii) semantic definition of the concepts in coding schemes (intensional). We develop algorithms based on well-studied graph traversal and ontology segmentation techniques for both the approaches and implement a prototype demonstrating their applicability on use cases from, SNOMED CT rendered, in the LexGrid terminology model. We also present preliminary evaluation of our approach and report investigation results done by subject matter experts at the Mayo Clinic.</p>","PeriodicalId":89468,"journal":{"name":"Proceedings. IEEE International Conference on Semantic Computing","volume":"2008 ","pages":"460-467"},"PeriodicalIF":0.0,"publicationDate":"2008-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3101576/pdf/nihms233931.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29901334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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