We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from a text document and the ontology sub-graphs resulting from the projection of the defined contexts. The domain ontology effectively becomes the classifier, where classification topics are expressed using the defined ontological contexts. In contrast to the traditional supervised categorization methods, the proposed method does not require a training set of documents. More importantly, our approach allows dynamically changing the classification topics without retraining of the classifier. In our experiments, we used the English language Wikipedia converted to an RDF ontology to categorize a corpus of current Web news documents into selection of topics of interest. The high accuracy achieved in our tests demonstrates the effectiveness of the proposed method, as well as the applicability of Wikipedia for semantic text categorization purposes.
{"title":"Ontology-Based Text Classification into Dynamically Defined Topics","authors":"M. Allahyari, K. Kochut, Maciej Janik","doi":"10.1109/ICSC.2014.51","DOIUrl":"https://doi.org/10.1109/ICSC.2014.51","url":null,"abstract":"We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from a text document and the ontology sub-graphs resulting from the projection of the defined contexts. The domain ontology effectively becomes the classifier, where classification topics are expressed using the defined ontological contexts. In contrast to the traditional supervised categorization methods, the proposed method does not require a training set of documents. More importantly, our approach allows dynamically changing the classification topics without retraining of the classifier. In our experiments, we used the English language Wikipedia converted to an RDF ontology to categorize a corpus of current Web news documents into selection of topics of interest. The high accuracy achieved in our tests demonstrates the effectiveness of the proposed method, as well as the applicability of Wikipedia for semantic text categorization purposes.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"28 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":"134387607","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}
Healthcare datasets are increasingly characterized by large volume, high rate of generation and need for real time analysis (velocity), and variety. These datasets are often termed biomedical big data and include multi-modal electrophysiological signals and electronic health records. In this talk, we focus on the computational challenges associated with signal data management and the role of semantic computing in addressing these challenges. We describe a cloud computing platform called Cloud wave that has been developed to effectively manage electrophysiological big data for epilepsy clinical research and patient care.
{"title":"Biomedical Big Data for Clinical Research and Patient Care: Role of Semantic Computing","authors":"S. Sahoo","doi":"10.1109/ICSC.2014.68","DOIUrl":"https://doi.org/10.1109/ICSC.2014.68","url":null,"abstract":"Healthcare datasets are increasingly characterized by large volume, high rate of generation and need for real time analysis (velocity), and variety. These datasets are often termed biomedical big data and include multi-modal electrophysiological signals and electronic health records. In this talk, we focus on the computational challenges associated with signal data management and the role of semantic computing in addressing these challenges. We describe a cloud computing platform called Cloud wave that has been developed to effectively manage electrophysiological big data for epilepsy clinical research and patient care.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"34 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":"133591926","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}
F. Amato, Aniello De Santo, V. Moscato, Fabio Persia, A. Picariello
Detection of human behavior in On-line Social Networks (OSNs) has become more and more important for a wide range of applications, such as security, marketing, parent controls and so on, opening a wide range of novel research areas, which have not been fully addressed yet. In this paper, we present a two-stage method for anomaly detection in humans' behavior while they are using a social network. First, we use Markov chains to automatically learn from the social network graph a number of models of human behaviors (normal behaviors), the second stage applies an activity detection framework based on the concept of possible words to detect all unexplained activities with respect to the normal behaviors. Some preliminary experiments using Facebook data show the approach efficiency and effectiveness.
在线社交网络(online Social Networks, OSNs)中人类行为的检测在安全、营销、家长控制等广泛应用中变得越来越重要，开辟了许多尚未完全解决的新研究领域。在本文中，我们提出了一种两阶段的方法来检测人类使用社交网络时的行为异常。首先，我们使用马尔可夫链从社交网络图中自动学习人类行为(正常行为)的一些模型，第二阶段应用基于可能词概念的活动检测框架来检测相对于正常行为的所有未解释的活动。一些使用Facebook数据的初步实验显示了该方法的效率和有效性。
{"title":"Detecting Unexplained Human Behaviors in Social Networks","authors":"F. Amato, Aniello De Santo, V. Moscato, Fabio Persia, A. Picariello","doi":"10.1109/ICSC.2014.21","DOIUrl":"https://doi.org/10.1109/ICSC.2014.21","url":null,"abstract":"Detection of human behavior in On-line Social Networks (OSNs) has become more and more important for a wide range of applications, such as security, marketing, parent controls and so on, opening a wide range of novel research areas, which have not been fully addressed yet. In this paper, we present a two-stage method for anomaly detection in humans' behavior while they are using a social network. First, we use Markov chains to automatically learn from the social network graph a number of models of human behaviors (normal behaviors), the second stage applies an activity detection framework based on the concept of possible words to detect all unexplained activities with respect to the normal behaviors. Some preliminary experiments using Facebook data show the approach efficiency and effectiveness.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"63 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":"132316845","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}
Seung-Hwa Chung, W. Tai, D. O’Sullivan, Aidan Boran
To address the problem of semantic heterogeneity, there has been a large body of research directed to the study of semantic mapping technologies. Although various semantic mapping technologies have been investigated, facilitating the process for domain experts to perform a semantic data integration task is not an easy task. This is because one is required not only to possess domain expertise but also to have a good understanding of knowledge engineering. This work proposes an abstract semantic mapping representation using UML for undertaking ontology mapping. The aim is to enable domain experts (particularly system engineers) to undertake mappings using the proposed UML representation that they are familiar with, while ensuring accuracy and ease of use of the automatically generated mappings. The proposed UML representation is evaluated through usability experiments (undertaken by system engineers) using a developed tool that was developed to implement the propose approach. The results show that the participants could correctly undertake the mapping task using the proposed UML representation and that the tool generated correct and executable mappings.
{"title":"A Semantic Mapping Representation and Generation Tool Using UML for System Engineers","authors":"Seung-Hwa Chung, W. Tai, D. O’Sullivan, Aidan Boran","doi":"10.1109/ICSC.2014.16","DOIUrl":"https://doi.org/10.1109/ICSC.2014.16","url":null,"abstract":"To address the problem of semantic heterogeneity, there has been a large body of research directed to the study of semantic mapping technologies. Although various semantic mapping technologies have been investigated, facilitating the process for domain experts to perform a semantic data integration task is not an easy task. This is because one is required not only to possess domain expertise but also to have a good understanding of knowledge engineering. This work proposes an abstract semantic mapping representation using UML for undertaking ontology mapping. The aim is to enable domain experts (particularly system engineers) to undertake mappings using the proposed UML representation that they are familiar with, while ensuring accuracy and ease of use of the automatically generated mappings. The proposed UML representation is evaluated through usability experiments (undertaken by system engineers) using a developed tool that was developed to implement the propose approach. The results show that the participants could correctly undertake the mapping task using the proposed UML representation and that the tool generated correct and executable mappings.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"260 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":"122897175","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 paper addresses the problem of modeling the relationship between phrases in English using a similarity graph. The mathematical model stores data about the strength of the relationship between phrases expressed as a decimal number. Both structured data from Wikipedia, such as that the Wikipedia page with title "Dog" belongs to the Wikipedia category "Domesticated animals", and textual descriptions, such as that the Wikipedia page with title "Dog" contains the word "wolf" thirty one times are used in creating the graph. The quality of the graph data is validated by comparing the similarity of pairs of phrases using our software that uses the graph with results of studies that were performed with human subjects. To the best of our knowledge, our software produces better correlation with the results of both the Miller and Charles study and the WordSimilarity-353 study than any other published research.
{"title":"Creating a Phrase Similarity Graph from Wikipedia","authors":"L. Stanchev","doi":"10.1109/ICSC.2014.22","DOIUrl":"https://doi.org/10.1109/ICSC.2014.22","url":null,"abstract":"The paper addresses the problem of modeling the relationship between phrases in English using a similarity graph. The mathematical model stores data about the strength of the relationship between phrases expressed as a decimal number. Both structured data from Wikipedia, such as that the Wikipedia page with title \"Dog\" belongs to the Wikipedia category \"Domesticated animals\", and textual descriptions, such as that the Wikipedia page with title \"Dog\" contains the word \"wolf\" thirty one times are used in creating the graph. The quality of the graph data is validated by comparing the similarity of pairs of phrases using our software that uses the graph with results of studies that were performed with human subjects. To the best of our knowledge, our software produces better correlation with the results of both the Miller and Charles study and the WordSimilarity-353 study than any other published research.","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":"131701537","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}
Pieter Colpaert, Laurens De Vocht, S. Verstockt, Anastasia Dimou, Raf Buyle, E. Mannens, R. Walle
To inform citizens when they can use government services, governments publish the services' opening hours on their website. When opening hours would be published in a machine interpretable manner, software agents would be able to answer queries about when it is possible to contact a certain service. We introduce an ontology for describing opening hours and use this ontology to create an input form. Furthermore, we explain a logic which can reply to queries for government services which are open or closed. The data is modeled according to this ontology. The principles discussed and applied in this paper are the first steps towards a design pattern for the governance of Open Government Data.
{"title":"Ontology Based Improvement of Opening Hours in E-governments","authors":"Pieter Colpaert, Laurens De Vocht, S. Verstockt, Anastasia Dimou, Raf Buyle, E. Mannens, R. Walle","doi":"10.1109/ICSC.2014.37","DOIUrl":"https://doi.org/10.1109/ICSC.2014.37","url":null,"abstract":"To inform citizens when they can use government services, governments publish the services' opening hours on their website. When opening hours would be published in a machine interpretable manner, software agents would be able to answer queries about when it is possible to contact a certain service. We introduce an ontology for describing opening hours and use this ontology to create an input form. Furthermore, we explain a logic which can reply to queries for government services which are open or closed. The data is modeled according to this ontology. The principles discussed and applied in this paper are the first steps towards a design pattern for the governance of Open Government Data.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"65 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":"128447983","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}
Pub Date : 2014-06-16DOI: 10.1142/S1793351X14400169
M. Ruta, F. Scioscia, Maria di Summa, S. Ieva, E. Sciascio, M. Sacco
Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A general data model and the corresponding ontology provide the formal underpinning for automatic posture and gesture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking, exploiting non-standard inference services, allows to: (i) detect postures via on-the-fly comparison of the retrieved annotations with standard posture descriptions stored as instances of a proper Knowledge Base, (ii) compare subsequent postures in order to recognize gestures. The framework has been implemented in a prototypical tool and experimental tests have been carried out on a reference dataset. Preliminary results indicate the feasibility of the proposed approach.
{"title":"Semantic Matchmaking for Kinect-Based Posture and Gesture Recognition","authors":"M. Ruta, F. Scioscia, Maria di Summa, S. Ieva, E. Sciascio, M. Sacco","doi":"10.1142/S1793351X14400169","DOIUrl":"https://doi.org/10.1142/S1793351X14400169","url":null,"abstract":"Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A general data model and the corresponding ontology provide the formal underpinning for automatic posture and gesture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking, exploiting non-standard inference services, allows to: (i) detect postures via on-the-fly comparison of the retrieved annotations with standard posture descriptions stored as instances of a proper Knowledge Base, (ii) compare subsequent postures in order to recognize gestures. The framework has been implemented in a prototypical tool and experimental tests have been carried out on a reference dataset. Preliminary results indicate the feasibility of the proposed approach.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"7 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":"116774995","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 rate at which biomedical literature is being published is quickly outpacing our ability to effectively leverage this information for evidence-based medicine. While papers are readily searchable through databases such as Pub Med, clinicians are often left with the time-consuming task of finding, assessing, interpreting, and applying this information. Tools that structure evidence from published papers using a standardized data model and provide an intuitive query interface for exploring documented biomedical entities would be valuable in utilizing this information as part of the clinical decision making process. This talk presents efforts towards developing computational tools and a representation for modeling and relating evidence from multiple clinical trial reports for lung cancer. Challenges related to representing this information in a machine-interpretable manner, assessing study quality, and handling conflicting evidence are described. I discuss the development of two tools: 1) an annotator tool used to extract information from papers, mapping it to concepts in an ontology-based representation and 2) a visualization that summarizes information about a single paper based on information captured in the model. Using lung cancer as a driving example, I demonstrate how these tools help users apply information reported in literature towards individually tailored medicine.
{"title":"Representing Evidence from Biomedical Literature for Clinical Decision Support: Challenges on Semantic Computing and Biomedicine","authors":"William Hsu","doi":"10.1109/ICSC.2014.67","DOIUrl":"https://doi.org/10.1109/ICSC.2014.67","url":null,"abstract":"The rate at which biomedical literature is being published is quickly outpacing our ability to effectively leverage this information for evidence-based medicine. While papers are readily searchable through databases such as Pub Med, clinicians are often left with the time-consuming task of finding, assessing, interpreting, and applying this information. Tools that structure evidence from published papers using a standardized data model and provide an intuitive query interface for exploring documented biomedical entities would be valuable in utilizing this information as part of the clinical decision making process. This talk presents efforts towards developing computational tools and a representation for modeling and relating evidence from multiple clinical trial reports for lung cancer. Challenges related to representing this information in a machine-interpretable manner, assessing study quality, and handling conflicting evidence are described. I discuss the development of two tools: 1) an annotator tool used to extract information from papers, mapping it to concepts in an ontology-based representation and 2) a visualization that summarizes information about a single paper based on information captured in the model. Using lung cancer as a driving example, I demonstrate how these tools help users apply information reported in literature towards individually tailored medicine.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114213374","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}
Anastasia Dimou, M. V. Sande, Jason Slepicka, Pedro A. Szekely, E. Mannens, Craig A. Knoblock, R. Walle
Incorporating structured data in the Linked Data cloud is still complicated, despite the numerous existing tools. In particular, hierarchical structured data (e.g., JSON) are underrepresented, due to their processing complexity. A uniform mapping formalization for data in different formats, which would enable reuse and exchange between tools and applied data, is missing. This paper describes a novel approach of mapping heterogeneous and hierarchical data sources into RDF using the RML mapping language, an extension over R2RML (the W3C standard for mapping relational databases into RDF). To facilitate those mappings, we present a toolset for producing RML mapping files using the Karma data modelling tool, and for consuming them using a prototype RML processor. A use case shows how RML facilitates the mapping rules' definition and execution to map several heterogeneous sources.
{"title":"Mapping Hierarchical Sources into RDF Using the RML Mapping Language","authors":"Anastasia Dimou, M. V. Sande, Jason Slepicka, Pedro A. Szekely, E. Mannens, Craig A. Knoblock, R. Walle","doi":"10.1109/ICSC.2014.25","DOIUrl":"https://doi.org/10.1109/ICSC.2014.25","url":null,"abstract":"Incorporating structured data in the Linked Data cloud is still complicated, despite the numerous existing tools. In particular, hierarchical structured data (e.g., JSON) are underrepresented, due to their processing complexity. A uniform mapping formalization for data in different formats, which would enable reuse and exchange between tools and applied data, is missing. This paper describes a novel approach of mapping heterogeneous and hierarchical data sources into RDF using the RML mapping language, an extension over R2RML (the W3C standard for mapping relational databases into RDF). To facilitate those mappings, we present a toolset for producing RML mapping files using the Karma data modelling tool, and for consuming them using a prototype RML processor. A use case shows how RML facilitates the mapping rules' definition and execution to map several heterogeneous sources.","PeriodicalId":175352,"journal":{"name":"2014 IEEE International Conference on Semantic Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129810630","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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