The emerging Internet of Things technologies enable enterprises to collect a variety of real-time data from the physical world, making a case for accessing, combining, interpreting, and distributing such data in real-time too. Enterprise Information Integration (EII) aims at providing tools for integrating data from multiple sources without having to first load all the data into a central warehouse, and, in so, for accessing live data. In this paper, we introduce a practical semantic EII solution, which, in addition to addressing the data virtualization and federation problems of EII, also provides data abstraction and data pipeline capabilities. This solution has been implemented as a software platform as well as applied in an operational enterprise system in the parking domain.
新兴的物联网技术使企业能够从物理世界中收集各种实时数据，并为实时访问、组合、解释和分发这些数据提供了理由。企业信息集成(Enterprise Information Integration, EII)旨在提供工具，用于集成来自多个数据源的数据，而无需首先将所有数据加载到中央仓库中，从而访问实时数据。在本文中，我们介绍了一个实用的语义EII解决方案，该解决方案除了解决EII的数据虚拟化和联邦问题外，还提供了数据抽象和数据管道功能。该解决方案已作为软件平台实现，并应用于停车场领域的运营企业系统中。
{"title":"A Semantic Approach to Enterprise Information Integration","authors":"A. Katasonov, A. Lattunen","doi":"10.1109/ICSC.2014.23","DOIUrl":"https://doi.org/10.1109/ICSC.2014.23","url":null,"abstract":"The emerging Internet of Things technologies enable enterprises to collect a variety of real-time data from the physical world, making a case for accessing, combining, interpreting, and distributing such data in real-time too. Enterprise Information Integration (EII) aims at providing tools for integrating data from multiple sources without having to first load all the data into a central warehouse, and, in so, for accessing live data. In this paper, we introduce a practical semantic EII solution, which, in addition to addressing the data virtualization and federation problems of EII, also provides data abstraction and data pipeline capabilities. This solution has been implemented as a software platform as well as applied in an operational enterprise system in the parking domain.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123937724","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}
Sebastian Pospiech, Sven Mielke, R. Mertens, K. Jagannath, Michael Städler
The business world has become more dynamic than ever before. Global competition and today's rapid pace of development in many fields has led to shorter time-to-market intervals, as well as more complex products and services. These developments do often imply impromptu changes to existing business processes. These dynamics are aggravated when unforeseen paths have to be taken like it is often the case when problems are solved in customer support situations. This leads to undocumented business processes which pose a serious problem for management. In order to cope with this problem the discipline of Process Mining has emerged. In classical Process Mining, event logs generated for example by workflow management systems are used to create a process model. In order for classical Process Mining to work, the process therefore has to be implemented in such a system, it just lacks documentation. The above mentioned impromptu changes and impromptu processes do, however, lack any such documentation. In many cases event logs do not exist, at least not in the strict sense of the definition. Instead, traces left by a process might include unstructured data, such as emails or notes in a human readable format. In this paper we will demonstrate how it is possible to search and locate processes that exist in a company, but that are neither documented, nor implemented in any business process management system. The idea is to use all data stores in a company to find a trace of a process instance and to reconstruct and visualize it. The trace of this single instance is then generalized to a process template that covers all instances of that process. This generalization step generates a description that can manually be adapted in order to fit all process instances. While retrieving instances from structured data can be described by simple queries, retrieving process steps from unstructured data often requires more elaborate approaches. Hence, we have modified a search-engine to combine a simple word-search with ad-hoc ontologies that allow for defining synonym relations on a query-by-query basis.
{"title":"Exploration and Analysis of Undocumented Processes Using Heterogeneous and Unstructured Business Data","authors":"Sebastian Pospiech, Sven Mielke, R. Mertens, K. Jagannath, Michael Städler","doi":"10.1109/ICSC.2014.24","DOIUrl":"https://doi.org/10.1109/ICSC.2014.24","url":null,"abstract":"The business world has become more dynamic than ever before. Global competition and today's rapid pace of development in many fields has led to shorter time-to-market intervals, as well as more complex products and services. These developments do often imply impromptu changes to existing business processes. These dynamics are aggravated when unforeseen paths have to be taken like it is often the case when problems are solved in customer support situations. This leads to undocumented business processes which pose a serious problem for management. In order to cope with this problem the discipline of Process Mining has emerged. In classical Process Mining, event logs generated for example by workflow management systems are used to create a process model. In order for classical Process Mining to work, the process therefore has to be implemented in such a system, it just lacks documentation. The above mentioned impromptu changes and impromptu processes do, however, lack any such documentation. In many cases event logs do not exist, at least not in the strict sense of the definition. Instead, traces left by a process might include unstructured data, such as emails or notes in a human readable format. In this paper we will demonstrate how it is possible to search and locate processes that exist in a company, but that are neither documented, nor implemented in any business process management system. The idea is to use all data stores in a company to find a trace of a process instance and to reconstruct and visualize it. The trace of this single instance is then generalized to a process template that covers all instances of that process. This generalization step generates a description that can manually be adapted in order to fit all process instances. While retrieving instances from structured data can be described by simple queries, retrieving process steps from unstructured data often requires more elaborate approaches. Hence, we have modified a search-engine to combine a simple word-search with ad-hoc ontologies that allow for defining synonym relations on a query-by-query basis.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"15 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131300084","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}
A well-known drawback in building machine learning semantic relation detectors for natural language is the lack of a large number of qualified training instances for the target relations in multiple languages. Even when good results are achieved, the datasets used by the state-of-the-art approaches are rarely published. In order to address these problems, this work presents an automatic approach to build multilingual semantic relation detectors through distant supervision combining two of the largest resources of structured and unstructured content available on the Web, DBpedia and Wikipedia. We map the DBpedia ontology back to the Wikipedia text to extract more than 100.000 training instances for more than 90 DBpedia relations for English and Portuguese languages without human intervention. First, we mine the Wikipedia articles to find candidate instances for relations described in the DBpedia ontology. Second, we preprocess and normalize the data filtering out irrelevant instances. Finally, we use the normalized data to construct regularized logistic regression detectors that achieve more than 80% of F-Measure for both English and Portuguese languages. In this paper, we also compare the impact of different types of features on the accuracy of the trained detector, demonstrating significant performance improvements when combining lexical, syntactic and semantic features. Both the datasets and the code used in this research are available online.
{"title":"Building Distant Supervised Relation Extractors","authors":"Thiago Nunes, D. Schwabe","doi":"10.1109/ICSC.2014.15","DOIUrl":"https://doi.org/10.1109/ICSC.2014.15","url":null,"abstract":"A well-known drawback in building machine learning semantic relation detectors for natural language is the lack of a large number of qualified training instances for the target relations in multiple languages. Even when good results are achieved, the datasets used by the state-of-the-art approaches are rarely published. In order to address these problems, this work presents an automatic approach to build multilingual semantic relation detectors through distant supervision combining two of the largest resources of structured and unstructured content available on the Web, DBpedia and Wikipedia. We map the DBpedia ontology back to the Wikipedia text to extract more than 100.000 training instances for more than 90 DBpedia relations for English and Portuguese languages without human intervention. First, we mine the Wikipedia articles to find candidate instances for relations described in the DBpedia ontology. Second, we preprocess and normalize the data filtering out irrelevant instances. Finally, we use the normalized data to construct regularized logistic regression detectors that achieve more than 80% of F-Measure for both English and Portuguese languages. In this paper, we also compare the impact of different types of features on the accuracy of the trained detector, demonstrating significant performance improvements when combining lexical, syntactic and semantic features. Both the datasets and the code used in this research are available online.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116914540","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}
Process constraint modeling and development, focusing on how to enforce the conformity of process constraints throughout its lifecycle, including design, deployment and runtime execution, remains a big challenge in the research area of model-driven development, especially when such constraints are considered in the composite Web services and workflow applications. By extending our previous work in process constraint ontology and process constraint language with the capability of exception definition and handling, we propose a semantic end-to-end process constraint modeling framework. In addition to constraint modeling at the design time, both static and dynamic verification in constraint's lifecycle are covered. While the former concentrates on syntactic, semantic and service specification verifications during design and deployment phases, the latter focuses on constraint verification during runtime, with the help of the underlying monitoring module. The scenarios of emergency reaction and nepotism in interview are considered and we illustrate how ontology and semantic reasoning are utilized in a constraint's whole lifecycle.
{"title":"A Semantic End-to-End Process Constraint Modeling Framework","authors":"Shasha Liu, K. Kochut","doi":"10.1109/ICSC.2014.32","DOIUrl":"https://doi.org/10.1109/ICSC.2014.32","url":null,"abstract":"Process constraint modeling and development, focusing on how to enforce the conformity of process constraints throughout its lifecycle, including design, deployment and runtime execution, remains a big challenge in the research area of model-driven development, especially when such constraints are considered in the composite Web services and workflow applications. By extending our previous work in process constraint ontology and process constraint language with the capability of exception definition and handling, we propose a semantic end-to-end process constraint modeling framework. In addition to constraint modeling at the design time, both static and dynamic verification in constraint's lifecycle are covered. While the former concentrates on syntactic, semantic and service specification verifications during design and deployment phases, the latter focuses on constraint verification during runtime, with the help of the underlying monitoring module. The scenarios of emergency reaction and nepotism in interview are considered and we illustrate how ontology and semantic reasoning are utilized in a constraint's whole lifecycle.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123729227","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}
T. Sipes, H. Karimabadi, Steve B. Jiang, K. Moore, Nan Li, Joseph R. Barr
The work presented here resulted in a valuable innovative technology tool for automatic detection of catastrophic errors in cancer radiotherapy, adding an important safeguard for patient safety. We designed a tool for Dynamic Modeling and Prediction of Radiotherapy Treatment Deviations from Intended Plans (Smart Tool) to automatically detect and highlight potential errors in a radiotherapy treatment plan, based on the data from several thousand prostate cancer treatments at Moore Cancer Research Center at University of California San Diego. Smart Tool determines if the treatment parameters are valid, against a previously built Predictive Model of a Medical Error (PMME). Smart Tool has the following main features: 1) It communicates with a radiotherapy treatment management system, checking all the treatment parameters in the background prior to execution, and after the human expert QA is completed, 2) The anomalous treatment parameters, if any, are detected using an innovative intelligent algorithm in a completely automatic and unsupervised manner, 3) It is a self-learning and constantly evolving system, the model is dynamically updated with the new treatment data, 4) It incorporates expert knowledge through the feedback loop of the dynamic process which updates the model with any new false positives (FP) and false negatives (FN), 4) When an outlier treatment parameter is detected, Smart Tool works by preventing the plan execution and highlighting the parameter for human intervention, 5) It is aimed at catastrophic errors, not small errors.
{"title":"Anomaly Detection in Time Series Radiotherapy Treatment Data","authors":"T. Sipes, H. Karimabadi, Steve B. Jiang, K. Moore, Nan Li, Joseph R. Barr","doi":"10.1109/ICSC.2014.64","DOIUrl":"https://doi.org/10.1109/ICSC.2014.64","url":null,"abstract":"The work presented here resulted in a valuable innovative technology tool for automatic detection of catastrophic errors in cancer radiotherapy, adding an important safeguard for patient safety. We designed a tool for Dynamic Modeling and Prediction of Radiotherapy Treatment Deviations from Intended Plans (Smart Tool) to automatically detect and highlight potential errors in a radiotherapy treatment plan, based on the data from several thousand prostate cancer treatments at Moore Cancer Research Center at University of California San Diego. Smart Tool determines if the treatment parameters are valid, against a previously built Predictive Model of a Medical Error (PMME). Smart Tool has the following main features: 1) It communicates with a radiotherapy treatment management system, checking all the treatment parameters in the background prior to execution, and after the human expert QA is completed, 2) The anomalous treatment parameters, if any, are detected using an innovative intelligent algorithm in a completely automatic and unsupervised manner, 3) It is a self-learning and constantly evolving system, the model is dynamically updated with the new treatment data, 4) It incorporates expert knowledge through the feedback loop of the dynamic process which updates the model with any new false positives (FP) and false negatives (FN), 4) When an outlier treatment parameter is detected, Smart Tool works by preventing the plan execution and highlighting the parameter for human intervention, 5) It is aimed at catastrophic errors, not small errors.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125500046","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}
The elasticity characteristic of cloud computing enables clients to acquire and release resources on demand. This characteristic reduces clients' cost by making them pay for the resources they actually have used. On the other hand, clients are obligated to maintain Service Level Agreement (SLA) with their users. One approach to deal with this cost-performance trade-off is employing an auto-scaling system which automatically adjusts application's resources based on its load. In this paper we have proposed an auto-scaling system based on Hidden Markov Model (HMM). We have conducted an experiment on Amazon EC2 infrastructure to evaluate our model. Our results show HMM can generate correct scaling actions in 97% of time. CPU utilization, throughput, and response time are being considered as performance metrics in our experiment.
{"title":"Cloud Resource Auto-scaling System Based on Hidden Markov Model (HMM)","authors":"A. Nikravesh, S. Ajila, Chung-Horng Lung","doi":"10.1109/ICSC.2014.43","DOIUrl":"https://doi.org/10.1109/ICSC.2014.43","url":null,"abstract":"The elasticity characteristic of cloud computing enables clients to acquire and release resources on demand. This characteristic reduces clients' cost by making them pay for the resources they actually have used. On the other hand, clients are obligated to maintain Service Level Agreement (SLA) with their users. One approach to deal with this cost-performance trade-off is employing an auto-scaling system which automatically adjusts application's resources based on its load. In this paper we have proposed an auto-scaling system based on Hidden Markov Model (HMM). We have conducted an experiment on Amazon EC2 infrastructure to evaluate our model. Our results show HMM can generate correct scaling actions in 97% of time. CPU utilization, throughput, and response time are being considered as performance metrics in our experiment.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116753300","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}
Semantic web works on producing machine readable data. So semantic web aims to overcome the amount of data that is consisted. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that is consisted as a result of the existing pages on the web. Semantic search engines are extensions to traditional search engines and improved version. This paper summarizes semantic web, traditional and semantic search engine concepts and infrastructure. Also semantic search approaches and differences from traditional approach are detailed. A summary of the literature is provided by touching on the trends on this area. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging.
{"title":"A Review on Semantic Web and Recent Trends in Its Applications","authors":"Oguzhan Menemencioglu, I. M. Orak","doi":"10.1109/ICSC.2014.57","DOIUrl":"https://doi.org/10.1109/ICSC.2014.57","url":null,"abstract":"Semantic web works on producing machine readable data. So semantic web aims to overcome the amount of data that is consisted. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that is consisted as a result of the existing pages on the web. Semantic search engines are extensions to traditional search engines and improved version. This paper summarizes semantic web, traditional and semantic search engine concepts and infrastructure. Also semantic search approaches and differences from traditional approach are detailed. A summary of the literature is provided by touching on the trends on this area. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117249263","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}
Detection of cyber-attacks is a major responsibility for network managers and security specialists. Most existing Network Intrusion Detection systems rely on inspecting individual packets, an increasingly resource consuming task in today's high speed networks due to the overhead associated with accessing packet content. An alternative approach is to detect attack patterns by investigating IP flows. Since analyzing raw data extracted from IP flows lacks the semantic information needed to discover attacks, a novel approach is introduced that utilizes contextual information to semantically reveal cyber-attacks from IP flows. Time, location, and other contextual information mined from network flow data is utilized to create semantic links among alerts raised in response to suspicious flows. The semantic links are identified through an inference process on probabilistic semantic link networks (SLNs). The resulting links are used at run-time to retrieve relevant suspicious activities that represent possible steps in multi-step attacks.
{"title":"Context Infusion in Semantic Link Networks to Detect Cyber-attacks: A Flow-Based Detection Approach","authors":"Ahmed Aleroud, George Karabatis","doi":"10.1109/ICSC.2014.29","DOIUrl":"https://doi.org/10.1109/ICSC.2014.29","url":null,"abstract":"Detection of cyber-attacks is a major responsibility for network managers and security specialists. Most existing Network Intrusion Detection systems rely on inspecting individual packets, an increasingly resource consuming task in today's high speed networks due to the overhead associated with accessing packet content. An alternative approach is to detect attack patterns by investigating IP flows. Since analyzing raw data extracted from IP flows lacks the semantic information needed to discover attacks, a novel approach is introduced that utilizes contextual information to semantically reveal cyber-attacks from IP flows. Time, location, and other contextual information mined from network flow data is utilized to create semantic links among alerts raised in response to suspicious flows. The semantic links are identified through an inference process on probabilistic semantic link networks (SLNs). The resulting links are used at run-time to retrieve relevant suspicious activities that represent possible steps in multi-step attacks.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122936255","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}
We propose a novel algorithm, QuIET, for binary classification of texts. The method automatically generates a set of span queries from a set of annotated documents and uses the query set to categorize unlabeled texts. QuIET generates models that are human understandable. We describe the method and evaluate it empirically against Support Vector Machines, demonstrating a comparable performance for a known curated dataset and a superior performance for some categories of noisy local businesses data. We also describe an active learning approach that is applicable to QuIET and can boost its performance.
{"title":"QuIET: A Text Classification Technique Using Automatically Generated Span Queries","authors":"Vassilis Polychronopoulos, N. Pendar, S. Jeffery","doi":"10.1109/ICSC.2014.18","DOIUrl":"https://doi.org/10.1109/ICSC.2014.18","url":null,"abstract":"We propose a novel algorithm, QuIET, for binary classification of texts. The method automatically generates a set of span queries from a set of annotated documents and uses the query set to categorize unlabeled texts. QuIET generates models that are human understandable. We describe the method and evaluate it empirically against Support Vector Machines, demonstrating a comparable performance for a known curated dataset and a superior performance for some categories of noisy local businesses data. We also describe an active learning approach that is applicable to QuIET and can boost its performance.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125804736","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}
Structure-similarity method for attributed generalized trees is proposed. (Meta)data is expressed as a generalized tree, in which inner-vertex labels (as types) and edge labels (as attributes) embody semantic information, while edge weights express assessments regarding the (percentage-)relative importance of the attributes, a kind of pragmatic information added by domain experts. The generalized trees are uniformly represented and interchanged using a weighted extension of Object Oriented RuleML. The recursive similarity algorithm performs a top-down traversal of structures and computes the global similarity of two structures bottom-up considering vertex labels, edge labels, and edge-weight similarities. In order to compare generalized trees having different sizes, the effect of a missing sub-structure on the overall similarity is computed using a simplicity measure. The proposed similarity approach is applied in the retrieval of Electronic Medical Records (EMRs).
{"title":"Structure Similarity of Attributed Generalized Trees","authors":"Mahsa Kiani, V. Bhavsar, H. Boley","doi":"10.1109/ICSC.2014.33","DOIUrl":"https://doi.org/10.1109/ICSC.2014.33","url":null,"abstract":"Structure-similarity method for attributed generalized trees is proposed. (Meta)data is expressed as a generalized tree, in which inner-vertex labels (as types) and edge labels (as attributes) embody semantic information, while edge weights express assessments regarding the (percentage-)relative importance of the attributes, a kind of pragmatic information added by domain experts. The generalized trees are uniformly represented and interchanged using a weighted extension of Object Oriented RuleML. The recursive similarity algorithm performs a top-down traversal of structures and computes the global similarity of two structures bottom-up considering vertex labels, edge labels, and edge-weight similarities. In order to compare generalized trees having different sizes, the effect of a missing sub-structure on the overall similarity is computed using a simplicity measure. The proposed similarity approach is applied in the retrieval of Electronic Medical Records (EMRs).","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133531333","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}
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