�ݺ�ߣshows by User: faigg

�ݺ�ߣshows by User: faigg / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: faigg / Wed, 26 Oct 2016 06:13:13 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: faigg Context Semantic Analysis: a knowledge-based technique for computing inter-document similarity /slideshow/context-semantic-analysis-a-knowledgebased-technique-for-computing-interdocument-similarity/67659236 20161017csasisap-161026061314
Presented at SISAP 2016 (http://sisap.org/2016/index.html) Paper: https://goo.gl/xAcyTq Abstract: We propose a novel knowledge-based technique for inter-document similarity, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature but CSA differs from them because it is designed to be portable to any RDF knowledge base. Our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a vector able to represent the context of a document. We show how such a Semantic Context Vector can be effectively exploited to compute inter-document similarity. Experimental results show that our general technique outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones of specialized methods.]]>
Presented at SISAP 2016 (http://sisap.org/2016/index.html) Paper: https://goo.gl/xAcyTq Abstract: We propose a novel knowledge-based technique for inter-document similarity, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature but CSA differs from them because it is designed to be portable to any RDF knowledge base. Our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a vector able to represent the context of a document. We show how such a Semantic Context Vector can be effectively exploited to compute inter-document similarity. Experimental results show that our general technique outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones of specialized methods.]]> Wed, 26 Oct 2016 06:13:13 GMT /slideshow/context-semantic-analysis-a-knowledgebased-technique-for-computing-interdocument-similarity/67659236 faigg@slideshare.net(faigg) Context Semantic Analysis: a knowledge-based technique for computing inter-document similarity faigg Presented at SISAP 2016 (http://sisap.org/2016/index.html) Paper: https://goo.gl/xAcyTq Abstract: We propose a novel knowledge-based technique for inter-document similarity, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature but CSA differs from them because it is designed to be portable to any RDF knowledge base. Our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a vector able to represent the context of a document. We show how such a Semantic Context Vector can be effectively exploited to compute inter-document similarity. Experimental results show that our general technique outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones of specialized methods. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20161017csasisap-161026061314-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented at SISAP 2016 (http://sisap.org/2016/index.html) Paper: https://goo.gl/xAcyTq Abstract: We propose a novel knowledge-based technique for inter-document similarity, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature but CSA differs from them because it is designed to be portable to any RDF knowledge base. Our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a vector able to represent the context of a document. We show how such a Semantic Context Vector can be effectively exploited to compute inter-document similarity. Experimental results show that our general technique outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones of specialized methods.

Context Semantic Analysis: a knowledge-based technique for computing inter-document similarity from Fabio Benedetti

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0 Visual Querying LOD sources with LODeX /slideshow/visual-querying-lod-sources-with-lodex/53756381 20151001kkaplodex-151009220631-lva1-app6892
paper: http://dl.acm.org/citation.cfm?id=2815849&CFID=533841763&CFTOKEN=85077894 Abstract: The Linked Open Data (LOD) Cloud has more than tripled its sources in just three years (from 295 sources in 2011 to 1014 in 2014). While the LOD data are being produced at a increasing rate, LOD tools lack in producing an high level representation of datasets and in supporting users in the exploration and querying of a source. To overcome the above problems and significantly increase the number of consumers of LOD data, we devised a new method and a tool, called LODeX, that promotes the understanding, navigation and querying of LOD sources both for experts and for beginners. It also provides a standardized and homogeneous summary of LOD sources and supports user in the creation of visual queries on previously unknown datasets. We have extensively evaluated the portability and usability of the tool. LODeX have been tested on the entire set of datasets available at Data Hub, i.e. 302 sources. In this paper, we showcase the usability evaluation of the different features of the tool (the Schema Summary representation and the visual query building) obtained on 27 users (comprising both Semantic Web experts and beginners).]]>
paper: http://dl.acm.org/citation.cfm?id=2815849&CFID=533841763&CFTOKEN=85077894 Abstract: The Linked Open Data (LOD) Cloud has more than tripled its sources in just three years (from 295 sources in 2011 to 1014 in 2014). While the LOD data are being produced at a increasing rate, LOD tools lack in producing an high level representation of datasets and in supporting users in the exploration and querying of a source. To overcome the above problems and significantly increase the number of consumers of LOD data, we devised a new method and a tool, called LODeX, that promotes the understanding, navigation and querying of LOD sources both for experts and for beginners. It also provides a standardized and homogeneous summary of LOD sources and supports user in the creation of visual queries on previously unknown datasets. We have extensively evaluated the portability and usability of the tool. LODeX have been tested on the entire set of datasets available at Data Hub, i.e. 302 sources. In this paper, we showcase the usability evaluation of the different features of the tool (the Schema Summary representation and the visual query building) obtained on 27 users (comprising both Semantic Web experts and beginners).]]> Fri, 09 Oct 2015 22:06:31 GMT /slideshow/visual-querying-lod-sources-with-lodex/53756381 faigg@slideshare.net(faigg) Visual Querying LOD sources with LODeX faigg paper: http://dl.acm.org/citation.cfm?id=2815849&CFID=533841763&CFTOKEN=85077894 Abstract: The Linked Open Data (LOD) Cloud has more than tripled its sources in just three years (from 295 sources in 2011 to 1014 in 2014). While the LOD data are being produced at a increasing rate, LOD tools lack in producing an high level representation of datasets and in supporting users in the exploration and querying of a source. To overcome the above problems and significantly increase the number of consumers of LOD data, we devised a new method and a tool, called LODeX, that promotes the understanding, navigation and querying of LOD sources both for experts and for beginners. It also provides a standardized and homogeneous summary of LOD sources and supports user in the creation of visual queries on previously unknown datasets. We have extensively evaluated the portability and usability of the tool. LODeX have been tested on the entire set of datasets available at Data Hub, i.e. 302 sources. In this paper, we showcase the usability evaluation of the different features of the tool (the Schema Summary representation and the visual query building) obtained on 27 users (comprising both Semantic Web experts and beginners). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20151001kkaplodex-151009220631-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> paper: http://dl.acm.org/citation.cfm?id=2815849&CFID=533841763&CFTOKEN=85077894 Abstract: The Linked Open Data (LOD) Cloud has more than tripled its sources in just three years (from 295 sources in 2011 to 1014 in 2014). While the LOD data are being produced at a increasing rate, LOD tools lack in producing an high level representation of datasets and in supporting users in the exploration and querying of a source. To overcome the above problems and significantly increase the number of consumers of LOD data, we devised a new method and a tool, called LODeX, that promotes the understanding, navigation and querying of LOD sources both for experts and for beginners. It also provides a standardized and homogeneous summary of LOD sources and supports user in the creation of visual queries on previously unknown datasets. We have extensively evaluated the portability and usability of the tool. LODeX have been tested on the entire set of datasets available at Data Hub, i.e. 302 sources. In this paper, we showcase the usability evaluation of the different features of the tool (the Schema Summary representation and the visual query building) obtained on 27 users (comprising both Semantic Web experts and beginners).

Visual Querying LOD sources with LODeX from Fabio Benedetti

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0 LODeX: Schema Summarization and automatic SPARQL query generation for Linked Open Data sources /faigg/lodex-schema-summarization-and-automatic-sparql-query-generation-for-linked-open-data-sources 20150112dday2015fabio-150112071339-conversion-gate01
Presentation of my research activity held in the 2014.]]>
Presentation of my research activity held in the 2014.]]> Mon, 12 Jan 2015 07:13:39 GMT /faigg/lodex-schema-summarization-and-automatic-sparql-query-generation-for-linked-open-data-sources faigg@slideshare.net(faigg) LODeX: Schema Summarization and automatic SPARQL query generation for Linked Open Data sources faigg Presentation of my research activity held in the 2014. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20150112dday2015fabio-150112071339-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation of my research activity held in the 2014.

LODeX: Schema Summarization and automatic SPARQL query generation for Linked Open Data sources from Fabio Benedetti

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0 Online Index Extraction from Linked Open Data Sources /slideshow/online-index-extraction-from-linked-open-data-sources/40455440 ld4iefabio2-141019104825-conversion-gate01
This presentation has been held by me at the Workshop titled Linked Data for Information Extraction 2014 (LD4IE) held at the International Semantic Web Conference 2014. The related paper is titled "Online Index Extraction from Linked Open Data Sources" and here is the link: http://ceur-ws.org/Vol-1267/LD4IE2014_Benedetti.pdf]]>
This presentation has been held by me at the Workshop titled Linked Data for Information Extraction 2014 (LD4IE) held at the International Semantic Web Conference 2014. The related paper is titled "Online Index Extraction from Linked Open Data Sources" and here is the link: http://ceur-ws.org/Vol-1267/LD4IE2014_Benedetti.pdf]]> Sun, 19 Oct 2014 10:48:25 GMT /slideshow/online-index-extraction-from-linked-open-data-sources/40455440 faigg@slideshare.net(faigg) Online Index Extraction from Linked Open Data Sources faigg This presentation has been held by me at the Workshop titled Linked Data for Information Extraction 2014 (LD4IE) held at the International Semantic Web Conference 2014. The related paper is titled "Online Index Extraction from Linked Open Data Sources" and here is the link: http://ceur-ws.org/Vol-1267/LD4IE2014_Benedetti.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ld4iefabio2-141019104825-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation has been held by me at the Workshop titled Linked Data for Information Extraction 2014 (LD4IE) held at the International Semantic Web Conference 2014. The related paper is titled "Online Index Extraction from Linked Open Data Sources" and here is the link: http://ceur-ws.org/Vol-1267/LD4IE2014_Benedetti.pdf

Online Index Extraction from Linked Open Data Sources from Fabio Benedetti

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0 Tutorial of Sentiment Analysis /slideshow/tutotial-of-sentiment-analysis/28598177 20131001bigdataanalysistweetsentiment-131125063458-phpapp02
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]]> Mon, 25 Nov 2013 06:34:58 GMT /slideshow/tutotial-of-sentiment-analysis/28598177 faigg@slideshare.net(faigg) Tutorial of Sentiment Analysis faigg <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20131001bigdataanalysistweetsentiment-131125063458-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Tutorial of Sentiment Analysis from Fabio Benedetti

]]> 36250 16 https://cdn.slidesharecdn.com/ss_thumbnails/20131001bigdataanalysistweetsentiment-131125063458-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 https://cdn.slidesharecdn.com/profile-photo-faigg-48x48.jpg?cb=1557907749 I am Fabio Benedetti, a PhD student of the Doctoral School in ICT of Modena and Reggio Emilia. My research topic regards the study of ontologies in the eHealth domain and now my research effort is focused on the development of LODeX: a tool that produces a highly representative summary of a LOD source. Starting from the URL of a SPARQL Endpoint, the tool launches a set of predefined SPARQL queries and automatically generates a summary of the source. Before starting the PhD school I did an internship at Datariver with the goal of reengineering a framework for clinical data management. In July 2012 I obtained the Master Degree in Computer Science and Engineering from the University of Mode... https://cdn.slidesharecdn.com/ss_thumbnails/20161017csasisap-161026061314-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/context-semantic-analysis-a-knowledgebased-technique-for-computing-interdocument-similarity/67659236 Context Semantic Analy... https://cdn.slidesharecdn.com/ss_thumbnails/20151001kkaplodex-151009220631-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/visual-querying-lod-sources-with-lodex/53756381 Visual Querying LOD s... https://cdn.slidesharecdn.com/ss_thumbnails/20150112dday2015fabio-150112071339-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds faigg/lodex-schema-summarization-and-automatic-sparql-query-generation-for-linked-open-data-sources LODeX: Schema Summariz...