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Stretching the Life of Twitter Classifiers with Time-Stamped Semantic GraphsStretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs
Stretching the Life of Twitter Classifiers with Time-Stamped Semantic GraphsAmparo Elizabeth Cano Basave
Presented at Hypertext'13.
Topic classification (TC) of short text messages o↵ers an ef- fective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolu- tion). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the top- ics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detec- tion (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.Harnessing Linked Knowledge Sources for Topic Classification in Social Media
Harnessing Linked Knowledge Sources for Topic Classification in Social MediaAmparo Elizabeth Cano Basave
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Stretching the Life of Twitter Classifiers with Time-Stamped Semantic GraphsStretching the Life of Twitter Classifiers with Time-Stamped Semantic Graphs
Stretching the Life of Twitter Classifiers with Time-Stamped Semantic GraphsAmparo Elizabeth Cano Basave
Presented at Hypertext'13.
Topic classification (TC) of short text messages o↵ers an ef- fective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolu- tion). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the top- ics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detec- tion (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.Harnessing Linked Knowledge Sources for Topic Classification in Social Media
Harnessing Linked Knowledge Sources for Topic Classification in Social MediaAmparo Elizabeth Cano Basave
We present a methodology for asserting rRepresenting, Proving and Sharing Trustworthiness of Web Resources Using Vera...
Representing, Proving and Sharing Trustworthiness of Web Resources Using Vera...Amparo Elizabeth Cano Basave
Sensor Networks meets Social WebSensing
Presence
(PreSense)
Ontology
–
User
Modelling
in
the
Semantic ...
Sensing
Presence
(PreSense)
Ontology
–
User
Modelling
in
the
Semantic ...Amparo Elizabeth Cano Basave
Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collec- tive perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profil- ing geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social aware- ness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.Volatile Classification of Point of Interests based on Social Activity Streams
Volatile Classification of Point of Interests based on Social Activity StreamsAmparo Elizabeth Cano Basave
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We present a methodology for asserting rRepresenting, Proving and Sharing Trustworthiness of Web Resources Using Vera...
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Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collec- tive perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profil- ing geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social aware- ness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.Volatile Classification of Point of Interests based on Social Activity Streams
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