Hierarchical Interest Graphs from Tweets
Project URL: http://wiki.knoesis.org/index.php/Hierarchical_Interest_Graph
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming.
In this project, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as Hierarchical Interest Graph. To create such graphs, we utilize user's Twitter data to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then use an adaptation of spreading activation theory to assign user interest score (or weights) to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of user's explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source.
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming.
In this project, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as Hierarchical Interest Graph. To create such graphs, we utilize user's Twitter data to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then use an adaptation of spreading activation theory to assign user interest score (or weights) to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of user's explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source.
BLOOMS
Project URL: https://github.com/jainprateek/BLOOMS
BLOOMS is an ontology alignment system based on the idea of bootstrapping information already present on the LOD cloud. It was developed particularly for Linked Open Data schema alignment.
To obtain more information about BLOOMS, please have a look at our papers Ontology Alignment for Linked Open Data. (Full research paper at ISWC2010)
BLOOMS is an ontology alignment system based on the idea of bootstrapping information already present on the LOD cloud. It was developed particularly for Linked Open Data schema alignment.
To obtain more information about BLOOMS, please have a look at our papers Ontology Alignment for Linked Open Data. (Full research paper at ISWC2010)