1. Modelling Point of Interest through Social Awareness Streams A.E. Cano, G. Burel, A.-S. Dadzie, F. Ciravegna The Oak Group, Department of Computer Science, The University of Sheffield
2. Social Stream Social Awareness Streams Collection of semi-public, natural language message produced by different users and characterisedby their brevity
4. Social Stream Social Awareness Streams and POIs Can social awareness streams convey information for modellingdynamic features of a point of interest?
18. Topica is a mashup developed to support especially those end users who may have little to no knowledge about where to find information on nearby POI, but who are able to trigger a search based on their interests. Topica mashes up services coming from: OpenCalais, Zemanta, DbpediaandDbpedia Spotlight for providing an even richer annotation and to widen recall during information retrieval. Google Maps Topica provides a temporal-facet to the featuring of a POI. Summary Summary
19. Try It Try It! http://ext.dcs.shef.ac.uk/~u0080/Topica/ http://nebula.dcs.shef.ac.uk/sparks/topica Prism Framework http://evhart.online.fr/prism/
20. Related Work Related Work Dbedia Mobile [1] Location-aware SW client, which based on the current geo coordinates renders a map with near-by locations from the DBpedia data set. Stevie Mobile [2] Allows users to share and browse temporal infor- mation about POIs events based on the location broadcast by end users’ GPS (Global Positioning System) on a map visualisation. Tintarev et al [3] Demonstrate the added benefit in personalising recommendations of popular POIsDbedia Mobile for tourists.
21. References References [1] C.Becker and C.Bizer. Exploring the geospatial semantic web with DBpediaMobile. Journal of Web Semantics, 7(4):278–286, 2009. [2] M. Braun, D. Schmeiß, A. Scherp, and S. Staab. Stevie–collaborative creation and exchange of events and pois on a mobile phone. pages 35–40, 2010. [3] N. Tintarev, A. Flores, and X. Amatriain. Off the beaten track: a mobile field study exploring the long tail of tourist recommendations. In Proceedings of the 12th international conference on Human computer interaction with mobile devices and services, MobileHCI ’10, pages 209–218, New York, NY, USA, 2010. ACM.
Editor's Notes
New emerging patterns of communications used in social platform like Facebook and Twitter. These communication patterns are characterised by high social connectivity and by their ability to communicate trends. (add references). The messages generated in these platforms are referred as Social Awareness Streams.
The rise of mobile-based social media services has contributed to the alteration of the way people perceive and engage with their social context and environments. We are interested in exploring how people model the places where they are we things that they say about them..
The our mashup “Topica”, we explore whether Social awareness streams can convey information for modellingdynamic features of a point of interest?
A point of interest is a specific point location that someone may find useful or interesting.. It is usually expressed by a set of static information like:Address, Geo coordinates, or information that is rarely changing like opening hours or the numbers of stars rating the place… However there is also hidden features which can change on time and can chaeacterise dynamically a a point of interest.For example, in the case of Kiriakos restaurant these hidden features include:
That his snails dish is one of the best in crete
Or that during this week they are offering complementary raki and glyka for dessert.
The approach taken in by Topicasonsisted on the following.. . For a given geocoordinated bounded area, we extracted POI through the Facebook Places API.
2) Then considering the POI location information provided by Facebook, we automatically alligned a POI with a Facebook Page
The aggregation of comments obtained from the page were used to modell the POI.
For analysing each comment we used Dbpeida Spotlight, Open Calais, and Zemanta,..
These services helped us to identify entities within the comment.. Like for this comment John Rose.. Palesting.. Sheffield University..
For each entity identified by these services we queried dbpedia for obtaining the entitie’s categories and broader categories
So for example, for the entity JohnRosecweategories and broader categories.. Doing the same for all the POI related comments and weighting this categories according a tr-idf function lead to a characterisation of the POI by category.
For modelling the relation between the messages’ entities and a POI we introduce the Linked POI ontology. The idea behind the ontology is to model a POI by aggregating the categories of the entities found on a message regarding the POI.
Topica provides a visualisation for this POIs and allows you to browse the categories that collectively fetatures a POIThe systems is design to work by batches.. In order to have real-time data featuring the POIs..The
Topica provides a visualisation for this POIs and allows you to browse the categories that collectively fetatures a POIThe systems is design to work by batches.. In order to have real-time data featuring the POIs..The
Topica was developed to support especially those end users who may have little to no knowledge about where to find information on nearby physical entities, but who are able to trigger search based on their interests. Topica cater to the modern user’s expectations of ubiquitous technology, by exploiting the collective knowledge of crowds to satisfy overlapping information requirements. Further work includes the syntactical analysis of comments in order to filter spamming comments, and to obtain entities that are more relevant to the POI. Topica currently uses fixed snapshots in time (period of Dec 2010 - Jan 2011); we aim however to move to a dynamic batch update, to ensure that the end user retrieves the latest information about POIs. Future work also includes the modelling of relevance decay functions for the latent features of a POI.
Topica was developed to support especially those end users who may have little to no knowledge about where to find information on nearby physical entities, but who are able to trigger search based on their interests. Topica cater to the modern user’s expectations of ubiquitous technology, by exploiting the collective knowledge of crowds to satisfy overlapping information requirements. Further work includes the syntactical analysis of comments in order to filter spamming comments, and to obtain entities that are more relevant to the POI. Topica currently uses fixed snapshots in time (period of Dec 2010 - Jan 2011); we aim however to move to a dynamic batch update, to ensure that the end user retrieves the latest information about POIs. Future work also includes the modelling of relevance decay functions for the latent features of a POI.
Searching for information about POIs and events in a user’s environment is an oft-performed activity.Our approach improves on existing POI retrieval apps in that Topica: does not require end users to explicitly contribute information about events - the mashup extracts these from users’ social interaction data; As in [3], Topica enriches POIs, but goes beyond by adding DBPedia resources extracted from POI related comments. By using services e.g. OpenCalais and Zemanta, we provide even richer annotation and widenrecall during information retrieval. Extracts temporal features that characterise a POI
