With the rapid growth in users on social networks, there is a corresponding increase in user-gene... more With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes.
The 2013 IEEE/WIC/ACM International Conference on Web Intelligence (to appear), 2013
Extracting and representing user interests on the Social Web is becoming an essential part of the... more Extracting and representing user interests on the Social Web is becoming an essential part of the Web for personalisation and recommendations. Such personalisation is required in order to provide an adaptive Web to users, where content fits their preferences, background and current interests, making the Web more social and relevant. Current techniques analyse user activities on social media systems and collect structured or unstructured sets of entities representing users’ interests. These sets of entities, or user profiles of interest, are often missing the semantics of the entities in terms of: (i) popularity and temporal dynamics of the interests on the Social Web and (ii) abstractness of the entities in the real world. State of the art techniques to compute these values are using specific knowledge bases or taxonomies and need to analyse the dynamics of the entities over a period of time. Hence, we propose a real-time, computationally inexpensive, domain independent model for concepts of interest composed of: popularity, temporal dynamics and specificity. We describe and evaluate a novel algorithm for computing specificity leveraging the semantics of Linked Data and evaluate the impact of our model on user profiles of interests.
The creation of accurate user profiles of interest across heterogeneous websites is a fundamental... more The creation of accurate user profiles of interest across heterogeneous websites is a fundamental step for personalisation, recommendations and analysis of social networks. The opportunities offered by the Web of Data and Semantic Web technologies introduce new interesting challenges. In particular, the main benefits for user profiling techniques are given by the extensive amount of already available and structured information and the solution to the "cold start" problem. On the other hand it is difficult to manage a massive "open corpus" such as the Web of Data and select only the relevant features and sources from an heterogeneous collection of datasets. Hence we propose semantic technologies for interlinking social websites and provenance management on the Web of Data to retrieve accurate information about data producers. The goal is to build comprehensive user profiles based on qualitative and quantitative measures about user activities across social sites.
User profiling techniques have mostly focused on retrieving and representing a user's knowledge, ... more User profiling techniques have mostly focused on retrieving and representing a user's knowledge, context and interests in order to provide recommendations, personalise search, and build user-adaptive systems. However, building a user profile on a single social network limits the quality and completeness of the profile, especially when interoperability of the profile is key and its reuse on different sites is necessary for providing other types of personalisation. Indeed recent studies have shown that users on the Social Web often use different social networking sites for diverse, and sometimes non-overlapping, purposes and interests. In this paper, we describe our methodology for the automatic creation and aggregation of interoperable and multi-domain user profiles using semantic technologies. Moreover, we propose a user study on different user profiling techniques for social networking websites in general, and for Twitter and Facebook in particular. In this regard, based on the results of our user evaluation, we investigate (i) the accuracy of different methodologies for profiling, (ii) the effect of time decay functions on ranking user interests, and (iii) the benefits of merging different user models using semantic technologies.
In the past few years, the growing number of personal information shared on the Web (through Web ... more In the past few years, the growing number of personal information shared on the Web (through Web 2.0 applications) increased awareness regarding privacy and personal data. Recent studies showed that privacy in Social Networks is a major concern when user profiles are publicly shared, revealing that most users are aware of privacy settings. Most Social Networks provide privacy settings restricting access to private data to those who are in the user’s friends lists (i.e. their “social graph”) such as Facebook’s privacy preferences. Yet, the studies show that users require more complex privacy settings as current systems do not meet their requirements. Hence, we propose a platform-independent system that allows end-users to set fine-grained privacy preferences for the creation of privacy-aware faceted user profiles on the Social Web.
With the rapid growth in users on social networks, there is
a corresponding increase in user-gene... more With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes.
DBpedia is one of the largest datasets in the linked Open Data cloud. Its centrality and its cros... more DBpedia is one of the largest datasets in the linked Open Data cloud. Its centrality and its cross-domain nature makes it one of the most important and most referred to knowledge bases on the Web of Data, generally used as a reference for data interlinking. Yet, in spite of its authoritative aspect, there is no work so far tackling the provenance aspect of DBpedia statements. By being extracted from Wikipedia, an open and collaborative encyclopedia, delivering provenance information about it would help to ensure trustworthiness of its data, a major need for people using DBpedia data for building applications. To overcome this problem, we propose an approach for modelling and managing provenance on DBpedia using Wikipedia edits, and making this information available on the Web of Data. In this paper, we describe the framework that we implemented to do so, consisting in (1) a lightweight modelling solution to semantically represent provenance of both DBpedia resources and Wikipedia content, along with mappings to popular ontologies such as the W7 - what, when, where, how, who, which, and why - and OPM - open provenance model - models, (2) an information extraction process and a provenance-computation system combining Wikipedia articles' history with DBpedia information, (3) a set of scripts to make provenance information about DBpedia statements directly available when browsing this source, as well as being publicly exposed in RDF for letting software agents consume it.
Wikis are often considered as being a wide source of information. However, identifying provenance... more Wikis are often considered as being a wide source of information. However, identifying provenance information about their content is crucial, whether it is for computing trust in public wiki pages or to identify experts in corporate wikis. In this paper, we address this issue by providing a lightweight ontology for provenance management in wikis, based on the W7 model. Furthermore, we showcase the use of our model in a framework that computes provenance information in Wikipedia, also using DBpedia to compute provenance and contribution information per category, and not only per page.
With the rapid growth in users on social networks, there is a corresponding increase in user-gene... more With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes.
The 2013 IEEE/WIC/ACM International Conference on Web Intelligence (to appear), 2013
Extracting and representing user interests on the Social Web is becoming an essential part of the... more Extracting and representing user interests on the Social Web is becoming an essential part of the Web for personalisation and recommendations. Such personalisation is required in order to provide an adaptive Web to users, where content fits their preferences, background and current interests, making the Web more social and relevant. Current techniques analyse user activities on social media systems and collect structured or unstructured sets of entities representing users’ interests. These sets of entities, or user profiles of interest, are often missing the semantics of the entities in terms of: (i) popularity and temporal dynamics of the interests on the Social Web and (ii) abstractness of the entities in the real world. State of the art techniques to compute these values are using specific knowledge bases or taxonomies and need to analyse the dynamics of the entities over a period of time. Hence, we propose a real-time, computationally inexpensive, domain independent model for concepts of interest composed of: popularity, temporal dynamics and specificity. We describe and evaluate a novel algorithm for computing specificity leveraging the semantics of Linked Data and evaluate the impact of our model on user profiles of interests.
The creation of accurate user profiles of interest across heterogeneous websites is a fundamental... more The creation of accurate user profiles of interest across heterogeneous websites is a fundamental step for personalisation, recommendations and analysis of social networks. The opportunities offered by the Web of Data and Semantic Web technologies introduce new interesting challenges. In particular, the main benefits for user profiling techniques are given by the extensive amount of already available and structured information and the solution to the "cold start" problem. On the other hand it is difficult to manage a massive "open corpus" such as the Web of Data and select only the relevant features and sources from an heterogeneous collection of datasets. Hence we propose semantic technologies for interlinking social websites and provenance management on the Web of Data to retrieve accurate information about data producers. The goal is to build comprehensive user profiles based on qualitative and quantitative measures about user activities across social sites.
User profiling techniques have mostly focused on retrieving and representing a user's knowledge, ... more User profiling techniques have mostly focused on retrieving and representing a user's knowledge, context and interests in order to provide recommendations, personalise search, and build user-adaptive systems. However, building a user profile on a single social network limits the quality and completeness of the profile, especially when interoperability of the profile is key and its reuse on different sites is necessary for providing other types of personalisation. Indeed recent studies have shown that users on the Social Web often use different social networking sites for diverse, and sometimes non-overlapping, purposes and interests. In this paper, we describe our methodology for the automatic creation and aggregation of interoperable and multi-domain user profiles using semantic technologies. Moreover, we propose a user study on different user profiling techniques for social networking websites in general, and for Twitter and Facebook in particular. In this regard, based on the results of our user evaluation, we investigate (i) the accuracy of different methodologies for profiling, (ii) the effect of time decay functions on ranking user interests, and (iii) the benefits of merging different user models using semantic technologies.
In the past few years, the growing number of personal information shared on the Web (through Web ... more In the past few years, the growing number of personal information shared on the Web (through Web 2.0 applications) increased awareness regarding privacy and personal data. Recent studies showed that privacy in Social Networks is a major concern when user profiles are publicly shared, revealing that most users are aware of privacy settings. Most Social Networks provide privacy settings restricting access to private data to those who are in the user’s friends lists (i.e. their “social graph”) such as Facebook’s privacy preferences. Yet, the studies show that users require more complex privacy settings as current systems do not meet their requirements. Hence, we propose a platform-independent system that allows end-users to set fine-grained privacy preferences for the creation of privacy-aware faceted user profiles on the Social Web.
With the rapid growth in users on social networks, there is
a corresponding increase in user-gene... more With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes.
DBpedia is one of the largest datasets in the linked Open Data cloud. Its centrality and its cros... more DBpedia is one of the largest datasets in the linked Open Data cloud. Its centrality and its cross-domain nature makes it one of the most important and most referred to knowledge bases on the Web of Data, generally used as a reference for data interlinking. Yet, in spite of its authoritative aspect, there is no work so far tackling the provenance aspect of DBpedia statements. By being extracted from Wikipedia, an open and collaborative encyclopedia, delivering provenance information about it would help to ensure trustworthiness of its data, a major need for people using DBpedia data for building applications. To overcome this problem, we propose an approach for modelling and managing provenance on DBpedia using Wikipedia edits, and making this information available on the Web of Data. In this paper, we describe the framework that we implemented to do so, consisting in (1) a lightweight modelling solution to semantically represent provenance of both DBpedia resources and Wikipedia content, along with mappings to popular ontologies such as the W7 - what, when, where, how, who, which, and why - and OPM - open provenance model - models, (2) an information extraction process and a provenance-computation system combining Wikipedia articles' history with DBpedia information, (3) a set of scripts to make provenance information about DBpedia statements directly available when browsing this source, as well as being publicly exposed in RDF for letting software agents consume it.
Wikis are often considered as being a wide source of information. However, identifying provenance... more Wikis are often considered as being a wide source of information. However, identifying provenance information about their content is crucial, whether it is for computing trust in public wiki pages or to identify experts in corporate wikis. In this paper, we address this issue by providing a lightweight ontology for provenance management in wikis, based on the W7 model. Furthermore, we showcase the use of our model in a framework that computes provenance information in Wikipedia, also using DBpedia to compute provenance and contribution information per category, and not only per page.
Uploads
Papers by Fabrizio Orlandi
a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes.
In this paper, we describe the framework that we implemented to do so, consisting in (1) a lightweight modelling solution to semantically represent provenance of both DBpedia resources and Wikipedia content, along with mappings to popular ontologies such as the W7 - what, when, where, how, who, which, and why - and OPM - open provenance model - models, (2) an information extraction process and a provenance-computation system combining Wikipedia articles' history with DBpedia information, (3) a set of scripts to make provenance information about DBpedia statements directly available when browsing this source, as well as being publicly exposed in RDF for letting software agents consume it.
their content is crucial, whether it is for computing trust in public wiki pages or to identify experts in corporate wikis. In this paper, we address this issue by providing a lightweight ontology for provenance management in wikis, based on the W7 model. Furthermore, we showcase the use of our model in a framework
that computes provenance information in Wikipedia, also using DBpedia to compute provenance and contribution information per category, and not only per page.
a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes.
In this paper, we describe the framework that we implemented to do so, consisting in (1) a lightweight modelling solution to semantically represent provenance of both DBpedia resources and Wikipedia content, along with mappings to popular ontologies such as the W7 - what, when, where, how, who, which, and why - and OPM - open provenance model - models, (2) an information extraction process and a provenance-computation system combining Wikipedia articles' history with DBpedia information, (3) a set of scripts to make provenance information about DBpedia statements directly available when browsing this source, as well as being publicly exposed in RDF for letting software agents consume it.
their content is crucial, whether it is for computing trust in public wiki pages or to identify experts in corporate wikis. In this paper, we address this issue by providing a lightweight ontology for provenance management in wikis, based on the W7 model. Furthermore, we showcase the use of our model in a framework
that computes provenance information in Wikipedia, also using DBpedia to compute provenance and contribution information per category, and not only per page.