This paper presents a platform for multifaceted product search using Semantic Web technology. Online shops can use a ping service to submit their RDFa annotated Web pages for processing. The platform is able to process these RDFa annotated (X)HTML pages and aggregate product information coming from different Web stores. We propose solutions for the identification of products and the mapping of the categories in this process. Furthermore, when a loose vocabulary such as the Google RDFa vocabulary is used, the platform deals with the issue of heterogeneous information (e.g., currencies, rating scales, etc.).

In a relatively short period of time, social media have acquired a prominent role in media and daily life. Although this development brought about several academic endeavors, the literature concerning the analysis of social media data to investigate one's customer base appears to be limited. In this paper, we show how data from the social network site Facebook can be operationalized to gain insight into the individuals connected to a company's Facebook site. In particular, we propose a data collection framework to obtain individual specific data and propose methodology to explore user profiles and identify segments based on these profiles. The proposed data collection framework can be used as an identification step in an analytical customer relationship management implementation that specifically focuses on potential customers. We illustrate our methodology by applying it to the Facebook page of an internationally well-known professional football (soccer) club. In our analysis, we identify four clusters of users that differ with respect to their indicated "liking" profiles.

Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, in this paper, we propose the Semantic Tag Clustering Search, a framework able to cope with these needs. The framework consists of three parts: removing syntactic variations, creating semantic clusters, and utilizing the obtained clusters to improve search and exploration of tag spaces. For removing syntactic variations, we use the normalized Levenshtein distance, and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we improve an existing non-hierarchical clustering technique. Using our framework, we are able to find more clusters and achieve a higher precision than the original method. The advantages of a cluster-based approach for searching and browsing through tag spaces have been exploited in XploreFlickr.com, the implementation of our framework.

Due to the increasing popularity of tagging, it is important to overcome challenges resulting from the free nature of tagging, such as the use of synonyms, homonyms, syntactic variations, etc. The Semantic Tag Clustering Search (STCS) framework deals with these challenges by detecting syntactic variations of tags and by clustering semantically related tags. We evaluate our framework using Flickr data from 2009 and compare the STCS framework to two previously introduced tag clustering techniques. We conclude that our framework performs significantly better in terms of cluster precision compared to one method and has a better average precision compared to the other method.

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Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, we propose the Semantic Tag Clustering Search, a framework which is able to cope with these needs. The framework consists of two parts: removing syntactic variations and creating semantic clusters. For removing syntactic variations, we use the normalized Levenshtein distance and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we improve an existing non-hierarchical clustering technique. Using our framework, we are able to find more clusters and achieve a higher precision than the original method.