ºÝºÝߣshows by User: MauroDragoni1 / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: MauroDragoni1 / Mon, 28 Oct 2019 21:09:17 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: MauroDragoni1 Keynote given at ISWC 2019 Semantic Management for Healthcare Workshop /slideshow/keynote-given-at-iswc-2019-semantic-management-for-healthcare-workshop/187890006 20191026swhkeynotev2noanimations-191028210917
Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity. In this talk I will present the HORUS.AI platform: an AI-based platform built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The platform collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g. facts of the world) and interacts with users by using a goal-based metaphor. Interactive dialogues are used for proposing set of challenges to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. HORUS.AI is constituted by two main layers: the Knowledge and the Dialog-Based Persuasive layers. The Knowledge Layer contains the knowledge bases modeling the specific domains for which users are monitored (e.g. diet), the rules provided by domain-experts, and the RDF-based reasoner that combines the modeled knowledge with the users’ generated data. The results produced by reasoning operations are coded into motivational strategies and messages by the Dialog-based Persuasive Layer. The Dialog-based Persuasive Layer creates and manages dialogues and generates motivational messages based on the information provided by the Knowledge Layer and learned from previous users’ behavior. This way, messages are tailored to specific users. These two layers are supported by an Input/Output Layer exploited for directly communicating with users (i.e. dedicated mobile application or social media channels) by providing summaries of the acquired data, the chat containing the interactions between the users and the system, and graphical items showing the users’ statuses with respect to their goals. HORUS.AI has been validated within the context of different territorial labs and projects and the observed results demonstrated the suitability of HORUS.AI in real-world scenarios.]]>
Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity. In this talk I will present the HORUS.AI platform: an AI-based platform built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The platform collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g. facts of the world) and interacts with users by using a goal-based metaphor. Interactive dialogues are used for proposing set of challenges to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. HORUS.AI is constituted by two main layers: the Knowledge and the Dialog-Based Persuasive layers. The Knowledge Layer contains the knowledge bases modeling the specific domains for which users are monitored (e.g. diet), the rules provided by domain-experts, and the RDF-based reasoner that combines the modeled knowledge with the users’ generated data. The results produced by reasoning operations are coded into motivational strategies and messages by the Dialog-based Persuasive Layer. The Dialog-based Persuasive Layer creates and manages dialogues and generates motivational messages based on the information provided by the Knowledge Layer and learned from previous users’ behavior. This way, messages are tailored to specific users. These two layers are supported by an Input/Output Layer exploited for directly communicating with users (i.e. dedicated mobile application or social media channels) by providing summaries of the acquired data, the chat containing the interactions between the users and the system, and graphical items showing the users’ statuses with respect to their goals. HORUS.AI has been validated within the context of different territorial labs and projects and the observed results demonstrated the suitability of HORUS.AI in real-world scenarios.]]> Mon, 28 Oct 2019 21:09:17 GMT /slideshow/keynote-given-at-iswc-2019-semantic-management-for-healthcare-workshop/187890006 MauroDragoni1@slideshare.net(MauroDragoni1) Keynote given at ISWC 2019 Semantic Management for Healthcare Workshop MauroDragoni1 Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity. In this talk I will present the HORUS.AI platform: an AI-based platform built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The platform collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g. facts of the world) and interacts with users by using a goal-based metaphor. Interactive dialogues are used for proposing set of challenges to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. HORUS.AI is constituted by two main layers: the Knowledge and the Dialog-Based Persuasive layers. The Knowledge Layer contains the knowledge bases modeling the specific domains for which users are monitored (e.g. diet), the rules provided by domain-experts, and the RDF-based reasoner that combines the modeled knowledge with the users’ generated data. The results produced by reasoning operations are coded into motivational strategies and messages by the Dialog-based Persuasive Layer. The Dialog-based Persuasive Layer creates and manages dialogues and generates motivational messages based on the information provided by the Knowledge Layer and learned from previous users’ behavior. This way, messages are tailored to specific users. These two layers are supported by an Input/Output Layer exploited for directly communicating with users (i.e. dedicated mobile application or social media channels) by providing summaries of the acquired data, the chat containing the interactions between the users and the system, and graphical items showing the users’ statuses with respect to their goals. HORUS.AI has been validated within the context of different territorial labs and projects and the observed results demonstrated the suitability of HORUS.AI in real-world scenarios. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20191026swhkeynotev2noanimations-191028210917-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity. In this talk I will present the HORUS.AI platform: an AI-based platform built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The platform collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g. facts of the world) and interacts with users by using a goal-based metaphor. Interactive dialogues are used for proposing set of challenges to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals. HORUS.AI is constituted by two main layers: the Knowledge and the Dialog-Based Persuasive layers. The Knowledge Layer contains the knowledge bases modeling the specific domains for which users are monitored (e.g. diet), the rules provided by domain-experts, and the RDF-based reasoner that combines the modeled knowledge with the users’ generated data. The results produced by reasoning operations are coded into motivational strategies and messages by the Dialog-based Persuasive Layer. The Dialog-based Persuasive Layer creates and manages dialogues and generates motivational messages based on the information provided by the Knowledge Layer and learned from previous users’ behavior. This way, messages are tailored to specific users. These two layers are supported by an Input/Output Layer exploited for directly communicating with users (i.e. dedicated mobile application or social media channels) by providing summaries of the acquired data, the chat containing the interactions between the users and the system, and graphical items showing the users’ statuses with respect to their goals. HORUS.AI has been validated within the context of different territorial labs and projects and the observed results demonstrated the suitability of HORUS.AI in real-world scenarios.
Keynote given at ISWC 2019 Semantic Management for Healthcare Workshop from Mauro Dragoni
]]> 451 1 https://cdn.slidesharecdn.com/ss_thumbnails/20191026swhkeynotev2noanimations-191028210917-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Translating Ontologies in Real-World Settings /slideshow/translating-ontologies-in-realworld-settings/67434748 20161021translatingontologiesinrealworldsettingsv3-161020023234
To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. The main challenge in translating ontologies is to find the right term with respect to the domain modeled by ontology itself. Machine translation services may help in this task; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications must implement a support system addressing this task for relieve experts work in validating all translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit semantic information of the concept's context for improving the quality of label translations. The system has been tested both within the Organic.Lingua project by translating the modeled ontology in three languages and on other multilingual ontologies in order to evaluate the effectiveness of the system in other contexts. The results have been compared with the translations provided by the Microsoft Translator API and the improvements demonstrated the viability of the proposed approach.]]>
To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. The main challenge in translating ontologies is to find the right term with respect to the domain modeled by ontology itself. Machine translation services may help in this task; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications must implement a support system addressing this task for relieve experts work in validating all translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit semantic information of the concept's context for improving the quality of label translations. The system has been tested both within the Organic.Lingua project by translating the modeled ontology in three languages and on other multilingual ontologies in order to evaluate the effectiveness of the system in other contexts. The results have been compared with the translations provided by the Microsoft Translator API and the improvements demonstrated the viability of the proposed approach.]]> Thu, 20 Oct 2016 02:32:34 GMT /slideshow/translating-ontologies-in-realworld-settings/67434748 MauroDragoni1@slideshare.net(MauroDragoni1) Translating Ontologies in Real-World Settings MauroDragoni1 To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. The main challenge in translating ontologies is to find the right term with respect to the domain modeled by ontology itself. Machine translation services may help in this task; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications must implement a support system addressing this task for relieve experts work in validating all translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit semantic information of the concept's context for improving the quality of label translations. The system has been tested both within the Organic.Lingua project by translating the modeled ontology in three languages and on other multilingual ontologies in order to evaluate the effectiveness of the system in other contexts. The results have been compared with the translations provided by the Microsoft Translator API and the improvements demonstrated the viability of the proposed approach. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20161021translatingontologiesinrealworldsettingsv3-161020023234-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. The main challenge in translating ontologies is to find the right term with respect to the domain modeled by ontology itself. Machine translation services may help in this task; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications must implement a support system addressing this task for relieve experts work in validating all translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit semantic information of the concept&#39;s context for improving the quality of label translations. The system has been tested both within the Organic.Lingua project by translating the modeled ontology in three languages and on other multilingual ontologies in order to evaluate the effectiveness of the system in other contexts. The results have been compared with the translations provided by the Microsoft Translator API and the improvements demonstrated the viability of the proposed approach.
Translating Ontologies in Real-World Settings from Mauro Dragoni
]]> 1558 6 https://cdn.slidesharecdn.com/ss_thumbnails/20161021translatingontologiesinrealworldsettingsv3-161020023234-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval /slideshow/keystone-summer-schoolmaurodragoniontologiesforir/50846440 keystonesummerschoolmaurodragoniontologiesforir-150723132850-lva1-app6891
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed]]>
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed]]> Thu, 23 Jul 2015 13:28:50 GMT /slideshow/keystone-summer-schoolmaurodragoniontologiesforir/50846440 MauroDragoni1@slideshare.net(MauroDragoni1) Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval MauroDragoni1 The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/keystonesummerschoolmaurodragoniontologiesforir-150723132850-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval from Mauro Dragoni
]]> 1668 7 https://cdn.slidesharecdn.com/ss_thumbnails/keystonesummerschoolmaurodragoniontologiesforir-150723132850-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Exploiting Multilinguality For Creating Mappings Between Thesauri /slideshow/2015-04-14fbksacslides/47147133 20150414fbksac-slides-150418124552-conversion-gate02
The definition of mappings between multilingual thesauri is a recent research topic concerning the application of the traditional schema mapping algorithms in conjunction with the use of multilingual resources. In this paper, we present a multilingual mapping approach aiming at defining matches between terms belonging to multilingual thesauri. The paper presents the approach as a variant of the schema mapping problem and discusses its evaluation on (i) domain-specific use cases and (ii) on a standard benchmark, namely MultiFarm benchmark, used for measuring the effectiveness of multilingual ontology mapping systems.]]>
The definition of mappings between multilingual thesauri is a recent research topic concerning the application of the traditional schema mapping algorithms in conjunction with the use of multilingual resources. In this paper, we present a multilingual mapping approach aiming at defining matches between terms belonging to multilingual thesauri. The paper presents the approach as a variant of the schema mapping problem and discusses its evaluation on (i) domain-specific use cases and (ii) on a standard benchmark, namely MultiFarm benchmark, used for measuring the effectiveness of multilingual ontology mapping systems.]]> Sat, 18 Apr 2015 12:45:52 GMT /slideshow/2015-04-14fbksacslides/47147133 MauroDragoni1@slideshare.net(MauroDragoni1) Exploiting Multilinguality For Creating Mappings Between Thesauri MauroDragoni1 The definition of mappings between multilingual thesauri is a recent research topic concerning the application of the traditional schema mapping algorithms in conjunction with the use of multilingual resources. In this paper, we present a multilingual mapping approach aiming at defining matches between terms belonging to multilingual thesauri. The paper presents the approach as a variant of the schema mapping problem and discusses its evaluation on (i) domain-specific use cases and (ii) on a standard benchmark, namely MultiFarm benchmark, used for measuring the effectiveness of multilingual ontology mapping systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20150414fbksac-slides-150418124552-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The definition of mappings between multilingual thesauri is a recent research topic concerning the application of the traditional schema mapping algorithms in conjunction with the use of multilingual resources. In this paper, we present a multilingual mapping approach aiming at defining matches between terms belonging to multilingual thesauri. The paper presents the approach as a variant of the schema mapping problem and discusses its evaluation on (i) domain-specific use cases and (ii) on a standard benchmark, namely MultiFarm benchmark, used for measuring the effectiveness of multilingual ontology mapping systems.
Exploiting Multilinguality For Creating Mappings Between Thesauri from Mauro Dragoni
]]> 452 6 https://cdn.slidesharecdn.com/ss_thumbnails/20150414fbksac-slides-150418124552-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Semantic-based Process Analysis /slideshow/semanticbased-process-analysis/40640547 20141023iswc20143storev2noanimations-141023092623-conversion-gate02
The widespread adoption of Information Technology systems and their capability to trace data about process executions has made available Information Technology data for the analysis of process executions. Meanwhile, at business level, static and procedural knowledge, which can be exploited to analyze and rea- son on data, is often available. In this paper we aim at providing an approach that, combining static and procedural aspects, business and data levels and exploiting semantic-based techniques allows business analysts to infer knowledge and use it to analyze system executions. The proposed solution has been implemented using current scalable Semantic Web technologies, that offer the possibility to keep the advantages of semantic-based reasoning with non-trivial quantities of data.]]>
The widespread adoption of Information Technology systems and their capability to trace data about process executions has made available Information Technology data for the analysis of process executions. Meanwhile, at business level, static and procedural knowledge, which can be exploited to analyze and rea- son on data, is often available. In this paper we aim at providing an approach that, combining static and procedural aspects, business and data levels and exploiting semantic-based techniques allows business analysts to infer knowledge and use it to analyze system executions. The proposed solution has been implemented using current scalable Semantic Web technologies, that offer the possibility to keep the advantages of semantic-based reasoning with non-trivial quantities of data.]]> Thu, 23 Oct 2014 09:26:23 GMT /slideshow/semanticbased-process-analysis/40640547 MauroDragoni1@slideshare.net(MauroDragoni1) Semantic-based Process Analysis MauroDragoni1 The widespread adoption of Information Technology systems and their capability to trace data about process executions has made available Information Technology data for the analysis of process executions. Meanwhile, at business level, static and procedural knowledge, which can be exploited to analyze and rea- son on data, is often available. In this paper we aim at providing an approach that, combining static and procedural aspects, business and data levels and exploiting semantic-based techniques allows business analysts to infer knowledge and use it to analyze system executions. The proposed solution has been implemented using current scalable Semantic Web technologies, that offer the possibility to keep the advantages of semantic-based reasoning with non-trivial quantities of data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20141023iswc20143storev2noanimations-141023092623-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The widespread adoption of Information Technology systems and their capability to trace data about process executions has made available Information Technology data for the analysis of process executions. Meanwhile, at business level, static and procedural knowledge, which can be exploited to analyze and rea- son on data, is often available. In this paper we aim at providing an approach that, combining static and procedural aspects, business and data levels and exploiting semantic-based techniques allows business analysts to infer knowledge and use it to analyze system executions. The proposed solution has been implemented using current scalable Semantic Web technologies, that offer the possibility to keep the advantages of semantic-based reasoning with non-trivial quantities of data.
Semantic-based Process Analysis from Mauro Dragoni
]]> 1362 3 https://cdn.slidesharecdn.com/ss_thumbnails/20141023iswc20143storev2noanimations-141023092623-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Authoring OWL 2 ontologies with the TEX-OWL syntax /slideshow/2014-10-18owled2014texowlv1noanimations/40448975 20141018owled2014texowlv1noanimations-141019043257-conversion-gate01
This work describes a new syntax that can be used to write OWL 2 ontologies. The syntax, which is known as TEX-OWL, was developed to address the need for an easy-to-read and easy-to-write plain text syntax. TEX-OWL is inspired by LaTeX syntax, and covers all construct of OWL 2. We designed TEX-OWL to be less verbose than the other OWL syntaxes, and easy-to-use especially for quickly developing small-size ontologies with just a text editor. The important features of the syntax are discussed in this work, and a reference implementation of a Java-based parser and writer is described.]]>
This work describes a new syntax that can be used to write OWL 2 ontologies. The syntax, which is known as TEX-OWL, was developed to address the need for an easy-to-read and easy-to-write plain text syntax. TEX-OWL is inspired by LaTeX syntax, and covers all construct of OWL 2. We designed TEX-OWL to be less verbose than the other OWL syntaxes, and easy-to-use especially for quickly developing small-size ontologies with just a text editor. The important features of the syntax are discussed in this work, and a reference implementation of a Java-based parser and writer is described.]]> Sun, 19 Oct 2014 04:32:57 GMT /slideshow/2014-10-18owled2014texowlv1noanimations/40448975 MauroDragoni1@slideshare.net(MauroDragoni1) Authoring OWL 2 ontologies with the TEX-OWL syntax MauroDragoni1 This work describes a new syntax that can be used to write OWL 2 ontologies. The syntax, which is known as TEX-OWL, was developed to address the need for an easy-to-read and easy-to-write plain text syntax. TEX-OWL is inspired by LaTeX syntax, and covers all construct of OWL 2. We designed TEX-OWL to be less verbose than the other OWL syntaxes, and easy-to-use especially for quickly developing small-size ontologies with just a text editor. The important features of the syntax are discussed in this work, and a reference implementation of a Java-based parser and writer is described. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20141018owled2014texowlv1noanimations-141019043257-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This work describes a new syntax that can be used to write OWL 2 ontologies. The syntax, which is known as TEX-OWL, was developed to address the need for an easy-to-read and easy-to-write plain text syntax. TEX-OWL is inspired by LaTeX syntax, and covers all construct of OWL 2. We designed TEX-OWL to be less verbose than the other OWL syntaxes, and easy-to-use especially for quickly developing small-size ontologies with just a text editor. The important features of the syntax are discussed in this work, and a reference implementation of a Java-based parser and writer is described.
Authoring OWL 2 ontologies with the TEX-OWL syntax from Mauro Dragoni
]]> 1031 3 https://cdn.slidesharecdn.com/ss_thumbnails/20141018owled2014texowlv1noanimations-141019043257-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Fuzzy Approach For Multi-Domain Sentiment Analysis /slideshow/2014-06-19inriaseminarioslidesnoanimations/36057744 20140619inriaseminarioslidesnoanimations-140619051556-phpapp02
An emerging field within Sentiment Analysis concerns the investigation about how sentiment polarities towards concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset by demonstrating its viability in real-world cases.]]>
An emerging field within Sentiment Analysis concerns the investigation about how sentiment polarities towards concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset by demonstrating its viability in real-world cases.]]> Thu, 19 Jun 2014 05:15:56 GMT /slideshow/2014-06-19inriaseminarioslidesnoanimations/36057744 MauroDragoni1@slideshare.net(MauroDragoni1) A Fuzzy Approach For Multi-Domain Sentiment Analysis MauroDragoni1 An emerging field within Sentiment Analysis concerns the investigation about how sentiment polarities towards concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset by demonstrating its viability in real-world cases. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20140619inriaseminarioslidesnoanimations-140619051556-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An emerging field within Sentiment Analysis concerns the investigation about how sentiment polarities towards concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset by demonstrating its viability in real-world cases.
A Fuzzy Approach For Multi-Domain Sentiment Analysis from Mauro Dragoni
]]> 958 6 https://cdn.slidesharecdn.com/ss_thumbnails/20140619inriaseminarioslidesnoanimations-140619051556-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Using Semantic and Domain-based Information in CLIR Systems /slideshow/using-semantic-and-domainbased-information-in-clir-systems/35167970 20140527clireswc2014-140527084322-phpapp01
Cross-Language Information Retrieval (CLIR) systems extend classic information retrieval mechanisms for allowing users to query across languages, i.e., to retrieve documents written in languages different from the language used for query formulation. In this paper, we present a CLIR system exploiting multilingual ontologies for enriching documents representation with multilingual semantic information during the indexing phase and for mapping query fragments to concepts during the retrieval phase. This system has been applied on a domain-specific document collection and the contribution of the ontologies to the CLIR system has been evaluated in conjunction with the use of both Microsoft Bing and Google Translate translation services. Results demonstrate that the use of domain-specific resources leads to a significant improvement of CLIR system performance.]]>
Cross-Language Information Retrieval (CLIR) systems extend classic information retrieval mechanisms for allowing users to query across languages, i.e., to retrieve documents written in languages different from the language used for query formulation. In this paper, we present a CLIR system exploiting multilingual ontologies for enriching documents representation with multilingual semantic information during the indexing phase and for mapping query fragments to concepts during the retrieval phase. This system has been applied on a domain-specific document collection and the contribution of the ontologies to the CLIR system has been evaluated in conjunction with the use of both Microsoft Bing and Google Translate translation services. Results demonstrate that the use of domain-specific resources leads to a significant improvement of CLIR system performance.]]> Tue, 27 May 2014 08:43:22 GMT /slideshow/using-semantic-and-domainbased-information-in-clir-systems/35167970 MauroDragoni1@slideshare.net(MauroDragoni1) Using Semantic and Domain-based Information in CLIR Systems MauroDragoni1 Cross-Language Information Retrieval (CLIR) systems extend classic information retrieval mechanisms for allowing users to query across languages, i.e., to retrieve documents written in languages different from the language used for query formulation. In this paper, we present a CLIR system exploiting multilingual ontologies for enriching documents representation with multilingual semantic information during the indexing phase and for mapping query fragments to concepts during the retrieval phase. This system has been applied on a domain-specific document collection and the contribution of the ontologies to the CLIR system has been evaluated in conjunction with the use of both Microsoft Bing and Google Translate translation services. Results demonstrate that the use of domain-specific resources leads to a significant improvement of CLIR system performance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20140527clireswc2014-140527084322-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Cross-Language Information Retrieval (CLIR) systems extend classic information retrieval mechanisms for allowing users to query across languages, i.e., to retrieve documents written in languages different from the language used for query formulation. In this paper, we present a CLIR system exploiting multilingual ontologies for enriching documents representation with multilingual semantic information during the indexing phase and for mapping query fragments to concepts during the retrieval phase. This system has been applied on a domain-specific document collection and the contribution of the ontologies to the CLIR system has been evaluated in conjunction with the use of both Microsoft Bing and Google Translate translation services. Results demonstrate that the use of domain-specific resources leads to a significant improvement of CLIR system performance.
Using Semantic and Domain-based Information in CLIR Systems from Mauro Dragoni
]]> 349 3 https://cdn.slidesharecdn.com/ss_thumbnails/20140527clireswc2014-140527084322-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Multilingual Knowledge Organization Systems Management: Best Practices /MauroDragoni1/2014-03-27mokiorganiclinguamultilingualbestpracticeworkshop 20140327mokiorganiclinguabestpracticeworkshop-140327084021-phpapp02
This presentation addresses the most well-known challenges in managing multilingual knowledge organization systems. Such challenges are presented and it is discussed how they have been addressed with the implementation of a collaborative tool called MoKi.]]>
This presentation addresses the most well-known challenges in managing multilingual knowledge organization systems. Such challenges are presented and it is discussed how they have been addressed with the implementation of a collaborative tool called MoKi.]]> Thu, 27 Mar 2014 08:40:21 GMT /MauroDragoni1/2014-03-27mokiorganiclinguamultilingualbestpracticeworkshop MauroDragoni1@slideshare.net(MauroDragoni1) Multilingual Knowledge Organization Systems Management: Best Practices MauroDragoni1 This presentation addresses the most well-known challenges in managing multilingual knowledge organization systems. Such challenges are presented and it is discussed how they have been addressed with the implementation of a collaborative tool called MoKi. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20140327mokiorganiclinguabestpracticeworkshop-140327084021-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation addresses the most well-known challenges in managing multilingual knowledge organization systems. Such challenges are presented and it is discussed how they have been addressed with the implementation of a collaborative tool called MoKi.
Multilingual Knowledge Organization Systems Management: Best Practices from Mauro Dragoni
]]> 570 3 https://cdn.slidesharecdn.com/ss_thumbnails/20140327mokiorganiclinguabestpracticeworkshop-140327084021-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Collaborative Modeling of Processes and Ontologies with MoKi /slideshow/mo-ki-smwconfall2013berlinv2-28360194/28360194 mokismwconfall2013berlinv2-131118033639-phpapp02
The objective of this framework is to sustain and encourage the collaboration between different kind of experts for modeling domains and for providing a semantic representation of the it. Examples of experts are the Domain Experts (i.e. those that knows the domain but usually lacks the modelling skills), and the Knowledge Engineers (those that have the skills but have not a clear understanding of the domain). During this talk, I will present the last version of MoKi, the wiki-based tool designed for supporting such a framework and I will show how this tool has been customized and extended in several projects in order to face the different challenges raised by the usage of semantic representations in different domains.]]>
The objective of this framework is to sustain and encourage the collaboration between different kind of experts for modeling domains and for providing a semantic representation of the it. Examples of experts are the Domain Experts (i.e. those that knows the domain but usually lacks the modelling skills), and the Knowledge Engineers (those that have the skills but have not a clear understanding of the domain). During this talk, I will present the last version of MoKi, the wiki-based tool designed for supporting such a framework and I will show how this tool has been customized and extended in several projects in order to face the different challenges raised by the usage of semantic representations in different domains.]]> Mon, 18 Nov 2013 03:36:39 GMT /slideshow/mo-ki-smwconfall2013berlinv2-28360194/28360194 MauroDragoni1@slideshare.net(MauroDragoni1) Collaborative Modeling of Processes and Ontologies with MoKi MauroDragoni1 The objective of this framework is to sustain and encourage the collaboration between different kind of experts for modeling domains and for providing a semantic representation of the it. Examples of experts are the Domain Experts (i.e. those that knows the domain but usually lacks the modelling skills), and the Knowledge Engineers (those that have the skills but have not a clear understanding of the domain). During this talk, I will present the last version of MoKi, the wiki-based tool designed for supporting such a framework and I will show how this tool has been customized and extended in several projects in order to face the different challenges raised by the usage of semantic representations in different domains. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mokismwconfall2013berlinv2-131118033639-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The objective of this framework is to sustain and encourage the collaboration between different kind of experts for modeling domains and for providing a semantic representation of the it. Examples of experts are the Domain Experts (i.e. those that knows the domain but usually lacks the modelling skills), and the Knowledge Engineers (those that have the skills but have not a clear understanding of the domain). During this talk, I will present the last version of MoKi, the wiki-based tool designed for supporting such a framework and I will show how this tool has been customized and extended in several projects in order to face the different challenges raised by the usage of semantic representations in different domains.
Collaborative Modeling of Processes and Ontologies with MoKi from Mauro Dragoni
]]> 750 6 https://cdn.slidesharecdn.com/ss_thumbnails/mokismwconfall2013berlinv2-131118033639-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-MauroDragoni1-48x48.jpg?cb=1708426453 EXCELLENT SKILL IN: Java, PHP, MySQL, XML, SQL, C++, C#, Visual Basic, SOA, Lucene, SOLR, Web Services, Excel (VBA), Access, JavaScript. PUBLICATIONS: https://dkm.fbk.eu/index.php/Mauro_Dragoni THE MAIN TOPICS OF MY RESEARCH ARE: Computational Intelligence, Information Retrieval, Ontologies, Semantic Web, and Business Process Modeling and Analysis. www.maurodragoni.com https://cdn.slidesharecdn.com/ss_thumbnails/20191026swhkeynotev2noanimations-191028210917-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/keynote-given-at-iswc-2019-semantic-management-for-healthcare-workshop/187890006 Keynote given at ISWC ... https://cdn.slidesharecdn.com/ss_thumbnails/20161021translatingontologiesinrealworldsettingsv3-161020023234-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/translating-ontologies-in-realworld-settings/67434748 Translating Ontologies... https://cdn.slidesharecdn.com/ss_thumbnails/keystonesummerschoolmaurodragoniontologiesforir-150723132850-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/keystone-summer-schoolmaurodragoniontologiesforir/50846440 Keystone Summer School...