2011 IEEE Fifth International Conference on Semantic Computing最新文献_第2页

A Multimedia Semantic Recommender System for Cultural Heritage Applications 一种面向文物应用的多媒体语义推荐系统

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.47

Massimiliano Albanese, A. d’Acierno, V. Moscato, Fabio Persia, A. Picariello

引用次数: 55

RESP: A Computer Aided OWL REasoner Selection Process 一个计算机辅助OWL推理器选择过程

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.17

W. Tai, J. Keeney, D. O’Sullivan

引用次数: 1

Content-Based Geospatial Schema Matching Using Semi-supervised Geosemantic Clustering and Hierarchy 基于内容的半监督地理语义聚类和层次的地理空间模式匹配

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.18

J. Partyka, L. Khan

{"title":"Content-Based Geospatial Schema Matching Using Semi-supervised Geosemantic Clustering and Hierarchy","authors":"J. Partyka, L. Khan","doi":"10.1109/ICSC.2011.18","DOIUrl":"https://doi.org/10.1109/ICSC.2011.18","url":null,"abstract":"The problem of semantic similarity across heterogeneous geospatial data sources continues to attract interest. Semantic similarity across data sources typically involves 1:1 matching of attributes and their instances between tables. Using clustering methods, three distinct challenges remain unaddressed. First, many clustering algorithms rely only on one instance property. Second, a consistent score for an attribute match is not produced. Finally, hierarchical relationships between the data are not considered. To address these, we introduce GeoSim, a tool for determining the semantic similarity between geospatial schemas. GeoSim consists of GeoSimG and GeoSimH. GeoSimG derives clusters from attribute instances based on their geographic and semantic properties. It examines attribute instances in the clusters to calculate a consistent semantic similarity score through entropy-based distribution (EBD). GeoSimH also captures hierarchical relationships between compared tables and attributes. Results from experiments involving multi-jurisdictional geospatial datasets show that GeoSim outperforms several popular semantic similarity approaches.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131786873","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: 3

Improving Interactive Video Retrieval by Exploiting Automatically-Extracted Video Structural Semantics 利用自动提取视频结构语义改进交互式视频检索

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.29

V. Mezaris, P. Sidiropoulos, Y. Kompatsiaris

引用次数: 5

SIM, a Semantic Instrumentation and Monitoring Solution for Large Scale Reasoning Systems 大规模推理系统的语义检测与监控解决方案

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICCP.2011.6047861

I. Toma, Mihai Chezan, R. Brehar, S. Nedevschi, D. Fensel

{"title":"SIM, a Semantic Instrumentation and Monitoring Solution for Large Scale Reasoning Systems","authors":"I. Toma, Mihai Chezan, R. Brehar, S. Nedevschi, D. Fensel","doi":"10.1109/ICCP.2011.6047861","DOIUrl":"https://doi.org/10.1109/ICCP.2011.6047861","url":null,"abstract":"One central task to the idea of Semantic Web is reasoning over semantic descriptions of web pages and information items available on the Web. A flagship project that is advancing the state of the art in reasoning with Web scale data is the Large Knowledge Collider (LarKC). Having a plug gable architecture, LarKC enables the interested users to test their reasoning approaches with very little overhead. In this context, instrumenting and monitoring of the large scale reasoning systems and their components becomes essential for verifying and assuring high performance, adaptability and well functioning. These aspects are in the end vital for any reasoning experiment. We introduce SIM, Semantic Instrumentation and Monitoring, a semantic-based instrumentation and monitoring solution. SIM enables the instrumentation and monitoring of LarKC applications in particular and any large scale reasoning system in general. It offers the means for developers to specify the metrics of interest, to instrument the code, to collect and observe how well the system and its components are performing. We identify a large set of relevant metrics for monitoring and provide ontological models for them. Finally we discuss the architecture and the role of each component and tool which is part of SIM.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"600 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116290652","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: 3

Multilingual Verification of the Annotation Scheme ISO-Space ISO-Space标注方案的多语种验证

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.71

Kiyong Lee, A. Fang, J. Pustejovsky

引用次数: 5

Boosting Collaborative Ontology Building with Key-Concept Extraction 基于关键概念提取的协同本体构建

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.21

Sara Tonelli, M. Rospocher, E. Pianta, L. Serafini

引用次数: 27

Automated analysis of semantic-aware access control policies: a logic-based approach 语义感知访问控制策略的自动分析:基于逻辑的方法

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.74

A. Armando, R. Carbone, Silvio Ranise

引用次数: 2

Visibly Pushdown Languages for a GUI Parsing Application with Probabilistic Lexer 带有概率词法分析器的GUI解析应用程序的可见下推语言

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.64

D. Lehavi, Omer Barkol, Sagi Schein

引用次数: 1

Utilizing Video Ontology for Fast and Accurate Query-by-Example Retrieval 利用视频本体实现快速准确的逐例查询检索

2011 IEEE Fifth International Conference on Semantic Computing Pub Date : 2011-09-18 DOI: 10.1109/ICSC.2011.88

Kimiaki Shirahama, K. Uehara

{"title":"Utilizing Video Ontology for Fast and Accurate Query-by-Example Retrieval","authors":"Kimiaki Shirahama, K. Uehara","doi":"10.1109/ICSC.2011.88","DOIUrl":"https://doi.org/10.1109/ICSC.2011.88","url":null,"abstract":"In this paper, we develop a video retrieval method based on Query-By-Example (QBE) approach where example shots are provided to represent a query, and used to construct a retrieval model. One drawback of QBE is that a user can only provide a small number of example shots, while each shot is generally represented by a high-dimensional feature. This causes that the retrieval model tends to be over fit to feature dimensions which are specific to example shots, but are ineffective for retrieving relevant shots. As a result, many clearly irrelevant shots are retrieved. To overcome this, we construct a {it video ontology} as knowledge base for QBE. Our video ontology is used to select concepts related to a query. Then, irrelevant shots are filtered by referring to recognition results of objects corresponding to selected concepts. Also, counter-example shots are not provided in QBE, although they are useful for constructing an accurate retrieval model. We introduce a method which selects counter-example shots among shots without user supervision. In this method, our video ontology is used to exclude shots relevant to the query from candidates of counter-example shots. Specifically, we filter shots where object recognition results for concepts related to the query are similar to those of example shots. The effectiveness of our video ontology is tested on TRECVID 2009 video data.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122538482","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: 1