M. Marneffe, Christopher D. Manning, Christopher Potts
{"title":"Veridicality and Utterance Understanding","authors":"M. Marneffe, Christopher D. Manning, Christopher Potts","doi":"10.1109/ICSC.2011.10","DOIUrl":"https://doi.org/10.1109/ICSC.2011.10","url":null,"abstract":"Natural language understanding depends heavily on assessing veridicality -- whether the speaker intends to convey that events mentioned are actual, non-actual, or uncertain. However, this property is little used in relation and event extraction systems, and the work that has been done has generally assumed that it can be captured by lexical semantic properties. Here, we show that context and world knowledge play a significant role in shaping veridicality. We extend the Fact Bank corpus, which contains semantically driven veridicality annotations, with pragmatically informed ones. Our annotations are more complex than the lexical assumption predicts but systematic enough to be included in computational work on textual understanding. They also indicate that veridicality judgments are not always categorical, and should therefore be modeled as distributions. We build a classifier to automatically assign event veridicality distributions based on our new annotations. The classifier relies not only on lexical features like hedges or negations, but also structural features and approximations of world knowledge, thereby providing a nuanced picture of the diverse factors that shape veridicality.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"2 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":"129731729","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}
{"title":"Multiresolution Semantic Visualization of Network Traffic","authors":"Alefiya Hussain, A. Viswanathan","doi":"10.1109/ICSC.2011.80","DOIUrl":"https://doi.org/10.1109/ICSC.2011.80","url":null,"abstract":"Multi resolution semantic analysis of data involves inferring increasing levels of meaning from data. Due to the large volume and complexity of network data, multi resolution visualizations allow the user to rapidly focus on meaningful and relevant information. Current tools and techniques for visual analysis of network data are limited in their ability to operate at semantically relevant resolutions and impose great cognitive burden on users to manually infer semantics from low-level details. Extending our previous work that allows users to define semantics as abstract models, we apply these models to construct multi resolution visualizations of network traffic data. Our methodology for visual exploration allows the user to rapidly analyze and understand network traces, by providing intuitive and interactive representations of the network. We demonstrate the effectiveness of our approach by applying it to analyzing network trace data from a cyber security incident involving DNS cache poisoning.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"90 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":"114564473","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}
{"title":"A Modular Architecture for Adaptive ChatBots","authors":"G. Pilato, A. Augello, S. Gaglio","doi":"10.1109/ICSC.2011.68","DOIUrl":"https://doi.org/10.1109/ICSC.2011.68","url":null,"abstract":"We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"14 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":"126598175","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}
{"title":"Detecting Abnormal Semantic Web Data Using Semantic Dependency","authors":"Yang Yu, Yingjie Li, J. Heflin","doi":"10.1109/ICSC.2011.81","DOIUrl":"https://doi.org/10.1109/ICSC.2011.81","url":null,"abstract":"Data quality is a critical problem for the Semantic Web. We propose that the degree to which a triple deviates from similar triples can be an important heuristic for identifying errors. Inspired by data dependency, which has shown promise in database data quality research, we introduce Semantic Dependency to assess quality of Semantic Web data. The system first builds a summary graph for finding candidate semantic dependencies. Each semantic dependency has a probability according to its instantiations and is subsequently adjusted based on the inconsistencies among them. Then triples can get a posterior probability of normality based on what semantic dependencies can support each of them. Repeating the iteration above, the proposed approach detects abnormal Semantic Web data. Experiments have shown that the system is efficient on data set with 10M triples and has more than a ten percent F-score improvement over our previous system.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"1 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":"117324445","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}
Kevin R. Page, A. Frazer, B. J. Nagel, D. D. Roure, K. Martinez
{"title":"Semantic Access to Sensor Observations through Web APIs","authors":"Kevin R. Page, A. Frazer, B. J. Nagel, D. D. Roure, K. Martinez","doi":"10.1109/ICSC.2011.86","DOIUrl":"https://doi.org/10.1109/ICSC.2011.86","url":null,"abstract":"Sensor networks are often deployed with the purpose of providing data to large-scale information management and GIS systems, or to collect measurements for specific scientific experiments. The benefits of such use are clear and widely accepted. The reuse of observations in low-cost, lightweight, web applications and mashups is a further compelling use case for sensor networks, but requires provision of data through simple mechanisms, readily accessible, that are quick to develop with. To enable the latter while maintaining support for larger applications and, to increase information utility, links to and from other datasets, we propose a domain-driven approach that embodies REST and Linked Data principles using a common semantic framework that underpins a separation of concerns between domain models, sensor observation infrastructure, and Application Programming Interfaces (APIs) while maintaining information consistency. We describe a reusable, reconfigurable, web service that realises this design and can be deployed to provide access to multiple sources of sensor information, including databases and streaming data, with flexible semantic configuration of the API and domain mapping.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"21 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":"132832049","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}
{"title":"Creating a Standardized Markup Language for Semantic Networks","authors":"Brian Harrington, Pia-Ramona Wojtinnek","doi":"10.1109/ICSC.2011.82","DOIUrl":"https://doi.org/10.1109/ICSC.2011.82","url":null,"abstract":"This paper describes the creation of, and serves as a request for comment on the SemML language for markup of semantic relational information. The major goal of the SemML project is to create a language that can act as an inter-lingua for a variety of semantic computing applications. In this paper, we discuss the structure of the language, and the ways it which it allows for recursively defined complex concepts, maintains source links which allows for management of multiple world views, and deals with other issues such as temporal data and factual versus non-factual information.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"6 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":"132906383","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}
Claire Bonial, William J. Corvey, Martha Palmer, V. Petukhova, H. Bunt
{"title":"A Hierarchical Unification of LIRICS and VerbNet Semantic Roles","authors":"Claire Bonial, William J. Corvey, Martha Palmer, V. Petukhova, H. Bunt","doi":"10.1109/ICSC.2011.57","DOIUrl":"https://doi.org/10.1109/ICSC.2011.57","url":null,"abstract":"This research compares several of the thematic roles of Verb Net (VN) to those of the Linguistic Infrastructure for Interoperable Resources and Systems (LIRICS). The purpose of this comparison is to develop a standard set of thematic roles that would be suited to a variety of natural language processing (NLP) applications. We draw from both resources to construct a unified set of semantic roles that will replace existing VN semantic roles. Through the process of comparison, we find that a hierarchical organization of coarse-grained, intermediate and fine-grained roles facilitates mapping between semantic resources of differing granularity and allows for flexibility in how VN can be used for diverse NLP applications, thus, we propose a hierarchical taxonomy of the unified role set. The comparison and subsequent development of the hierarchy reveals a level of granularity shared by both resources, which could be further developed into a standard set of thematic roles for the International Organization for Standardization (ISO).","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"14 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":"131756528","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}
Christopher Ochs, Tian Tian, J. Geller, Soon Ae Chun
{"title":"Google Knows Who is Famous Today -- Building an Ontology from Search Engine Knowledge and DBpedia","authors":"Christopher Ochs, Tian Tian, J. Geller, Soon Ae Chun","doi":"10.1109/ICSC.2011.50","DOIUrl":"https://doi.org/10.1109/ICSC.2011.50","url":null,"abstract":"Modern search engines provide users with suggested query completions. These search suggestions are often ambiguous in nature and could refer to any number of homonyms. Previously we used a static ontology built from data in Wikipedia to address this issue. Here, we present a method for dynamically building an ontology of \"famous people\" based on mining the suggested completions of a search engine that are produced in response to partial user search queries. This is combined with data from DBpedia. We use this ontology to provide disambiguated search suggestions within a dynamic version of our previous Ontology-Supported Web Search system.","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":"131844728","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}
{"title":"Privacy-Preserving Trust-Based Recommendations on Vertically Distributed Data","authors":"C. Kaleli, H. Polat","doi":"10.1109/ICSC.2011.43","DOIUrl":"https://doi.org/10.1109/ICSC.2011.43","url":null,"abstract":"Providing recommendations on trusts between entities is receiving increasing attention lately. Customers may prefer different online vendors for shopping. Thus, their preferences about various products might be distributed among multiple parties. To provide more accurate and reliable referrals, such companies might decide to collaborate. Due to privacy, legal, and financial reasons, however, they do not want to work jointly. In this paper, we propose a method for providing trust-based predictions on vertically distributed data while preserving data owners' confidentiality. We analyze our scheme in terms of privacy and performance. We also perform experiments for accuracy analysis. Our analyses show that our scheme is secure and able to provide accurate and reliable predictions efficiently.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"47 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":"114228483","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}
{"title":"A Latent Semantic Analysis-Based Approach to Geographic Feature Categorization from Text","authors":"Yuxia Huang","doi":"10.1109/ICSC.2011.15","DOIUrl":"https://doi.org/10.1109/ICSC.2011.15","url":null,"abstract":"Geographic feature categorization from text addresses the need for querying and finding geographic features from text documents. Although many text classification techniques have been developed, there are limitations to apply to geographic features due to the uniqueness of the geography features. In this paper we propose a method to classify geographic features based on latent semantic analysis and domain knowledge. The empirical experiment indicates that the proposed method achieves satisfactory categorizing effectiveness.","PeriodicalId":408382,"journal":{"name":"2011 IEEE Fifth International Conference on Semantic Computing","volume":"62 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":"114933117","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}