U-Sem in ImREAL
[ Augmented User and Context Modeling Infrastructure ]

1. Overview

1.1 U-Sem in ImREAL

1.2 Research Approach

The research approach followed in the science and engineering of the augmented learner and context modeling infrastructure is separated in two main stages:

• Year 1: In year one, the focus is on (1) the setup of the technical infrastructure for the learner and context modeling infrastructure to be available for the other components and services in the project, and (2) the development and validation of relevant techniques for learner and context modeling. For the development and validation of the modeling techniques, first the state of the art in user modeling is used to create techniques for capturing augmented, real-world knowledge into user models.
• Year 2/3: In years two and three, the research and development is aimed at the experimentation with the techniques from the first stage on real-world knowledge to determine the best possible augmentation for adaptive applications, such as the ImREAL simulators.

2. Architecture

U-Sem is a framework and service infrastructure for enriching and mining usage and user data such as learner and context data in ImREAL. U-Sem allows developers to create and design services for enriching and analyzing user-related data and makes these services available to client applications. The above figure depicts the architecture of the U-Sem modeling service. The green boxes are those components that are already implemented and available within ImREAL.

The architecture of the ImREAL infrastructure follows the state of the art model for semantic-based user model augmentation, depicted in the above figure. The bottom layer shows the preprocessing of user data such as learner and context data to align it with the demands from the target application. At the middle layer the actual user modeling and analysis takes place. At the top layer the model and analysis are exploited for the specific application, such as adapting an application, e.g. a simulator. For the application in ImREAL, the following are key components of U-Sem:

• Semantic Enrichment, Linkage and Alignment: plug-ins that enrich the semantic meaning of user-related data. Given observations about the user (e.g. usage data such as click-through data or other sorts of log data), user profile information or domain knowledge (e.g. descriptions of resources a user interacted with), these components clarify the semantics of that data so that it can be interpreted by the user modeling components. Alignment components aim for resolving problems caused by heterogeneous schemata or ambiguity of terms.
• Analysis and User Modeling: plug-ins that analyze the (enriched) user-related data for inferring user profiles. U-Sem allows for a variety of user modeling modules ranging from plug-ins that infer profile attributes such as interests, knowledge, skills or demographic characteristics for individual users to plug-ins that rather follow a stereotype-based user modeling approach and therefore analyze data related a community of users.
• Orchestration Logic: engines that allow orchestrating a set of plug-ins from the layer below in order to provide workflows/orchestrations that provide certain user modeling functionality. The orchestration logic allows for executing enrichment and user modeling pipelines which are composed of plug-ins of the semantic enrichment and user modeling layer. Orchestrations of plug-ins are made available as service to U-Sem clients via the U-Sem endpoints.
• Endpoints: interfaces that allow client applications to store and query for user-related data. Dialog-based support allows for negotiation between U-Sem and U-Sem clients where U- Sem may augment profile information sent by a client until it meets the requirements of the client.
• U-Sem Application Logic: controls the U-Sem application flow (based on incoming requests), i.e. it connects the endpoints with the orchestration logic and plug-ins and provides functionality related to access control or plug-in management.
• U-Sem Clients: client applications that connect to U-Sem via the endpoints. U-Sem clients are software components as well, i.e. communication with the actual end-users for which U-Sem may infer interests, knowledge skills, etc. is thus encapsulated via client applications such as ImREAL simulators or the Dialog-based learner modeling agent.

3. Applications and Usage Showcases

3.1 Applications in Demos

• Reflection Trigger (ETU). More details: ../reflectionTrigger/
• Job Interview Learner Profiling (IMA). More details: ../jobInterviewProfiling/

3.2 Interest Profiling based on Social Data

The suite of services for Twitter-based user modeling (TweetUM) allows for the generation of contextual user interest profiles from a user's Twitter stream: http://wis.ewi.tudelft.nl/imreal/u-sem/tweetum//

3.3 Location Profiling

U-Sem includes services that are based on location profiling and allow us to generate a user's whereabouts in the past based on the photos the user has uploaded to the photo sharing platform Flickr. [ more details ]

3.4 Language Profiling

U-Sem includes a language profiling service that identifies the languages that a user can understand based on the user's tweets. [ more details ]

3.5 Orchestrating U-Sem Functionality

U-Sem allows designers to easily create customized user model augmentation services using RDF Gears.

4. Services

• Twitter-based User Modeling: a suite of U-Sem plug-ins that allow for understanding the semantics of short text snippets (tweets) published by a learner and deducing interest profiles from tweeting activities. [ more details ]
• Knowledge Profiling: the knowledge profiling components deduce a learner's knowledge about different concepts by analyzing the learner's social activities. [ more details ]
• Location Detection: given activities a learner performs in ImRAL and on the Web, the location detection component allows to create a location profile for a learner. [ more details ]
• Faceted Search: this U-Sem service provides functionality to perform faceted search on the data generated by the learners. It particularly features also functionality to filter tweets that have been published by the learner. [ more details ]
• Multilingual Ontology Matching: content and user data may come in different languages and schemata. This components allows to align such multilingual ontological data. [ more details ]
• Domain-aware Ontology Matching: for matching heterogeneous data and schemata that originates from different applications and domains, U-Sem provides domain-aware mapping services. [ more details ]
• Deriving Group Profiles from YouTube: a suite of services that mine the user-created content on the video social sharing site YouTube. [ more details ]
• Language Detection: learners may speak different languages. This components infers the language skills of a given learner by analyzing her data traces. [ more details ]
• Interactive User Modeling Dialogue: facilitates an interactive dialogue with the learner using semantically augmented content. [ more details ]
• RDF Gears: the core orchestration engine of U-Sem is called RDF Gears. It allows designers and developers to orchestrate the functionality that is provided by the components above and to create customized augmented user modeling services. [ more details ]

5. Publications

5.1 Project Deliverables

1. Fabian Abel, Ahmad Ammari, Ilknur Celik, Vania Dimitrova, Claudia Hauff, Laura Hollink, Geert-Jan Houben, Dhaval Thakker: Deliverable 4.1: Functional Specification of Learner and Context Modeling Services. May 2011
2. Fabian Abel, Ahmad Ammari, Ilknur Celik, Vania Dimitrova, Claudia Hauff, Laura Hollink, Geert-Jan Houben, Dhaval Thakker: Deliverable 4.2: First Version of Demonstrator of Context and Learner Modelling Services. October 2011

5.2 Scientific Publications

1. Fabian Abel, Eelco Herder, Geert-Jan Houben, Nicola Henze, Daniel Krause. Cross-system User Modeling and Personalization on the Social Web. In P. Brusilovski, D. Chin (eds.): User Modeling and User-Adapted Interaction (UMUAI), Special Issue on Personalization in Social Web Systems, 2011 [bib] (to appear)
2. Eric Feliksik. A data integration framework for the Semantic Web. Master thesis, TU Delft, 2011. [pdf]
3. Dennis Spohr, Laura Hollink, Philipp Cimiano. Multilingual and Cross-Lingual Ontology Matching and its Application to Financial Accounting Standards. In Proceedings of 10th International Semantic Web Conference (ISWC), Bonn, Germany, October 2011.
4. Fabian Abel, Ilknur Celik, Geert-Jan Houben, Patrick Siehndel. Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter. In Proceedings of 10th International Semantic Web Conference (ISWC), Bonn, Germany, October 2011 [bib, pdf]
5. Kristian Slabbekoorn, Laura Hollink, Geert-Jan Houben. Domain-aware Matching of Events to DBpedia. In DeRiVE workshop on Detection, Representation, and Exploitation of Events in the Semantic Web at ISWC, Bonn, Germany, 2011.
6. Qi Gao, Fabian Abel, Geert-Jan Houben, Ke Tao. Interweaving Trend and User Modeling for Personalized News Recommendation. In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence (WI), Lyon, France, August 2011 [bib, pdf]
7. Claudia Hauff and Geert-Jan Houben. Deriving Knowledge Profiles from Twitter. In Proceedings of 6th European conference on Technology enhanced learning: towards ubiquitous learning (EC-TEL), Palermo, Italy, September 2011 [pdf]
8. Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao. Analyzing User Modeling on Twitter for Personalized News Recommendations. In Proceedings of International Conference on User Modeling, Adaptation and Personalization (UMAP), Girona, Spain, July 2011 [bib, pdf] (won best paper award at UMAP 2011)
9. Ilknur Celik, Fabian Abel, Patrick Siehndel. Adaptive Faceted Search on Twitter. In Proceedings of International Workshop on Semantic Adaptive Social Web (SASWeb), in connection with UMAP, Girona, Spain, July 2011 [bib, pdf]
10. Fabian Abel, Samur Aurojo, Qi Gao, Geert-Jan Houben. Analyzing Cross-System User Modeling on the Social Web. In Proceedings of Eleventh International Conference on Web Engineering (ICWE), Paphos, Cyprus, June 2011 [bib, pdf]
11. Ilknur Celik, Fabian Abel, Geert-Jan Houben. Learning Semantic Relationships between Entities in Twitter. In Proceedings of Eleventh International Conference on Web Engineering (ICWE), Paphos, Cyprus, June 2011 [bib, pdf]
12. Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao. Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web. In Proceedings of Proceedings of ACM International Conference on Web Science (WebSci), Koblenz, Germany June 2011 [bib, pdf]
13. Ilknur Celik, Fabian Abel, Patrick Siehndel. Towards a Framework for Adaptive Faceted Search on Twitter. In Proceedings of International Workshop on Dynamic and Adaptive Hypertext (DAH), in connection with ACM Hypertext, Eindhoven, The Netherlands, June 2011 [bib, pdf]
14. Fabian Abel, Qi Gao, Geert-Jan Houben, Ke Tao. Semantic Enrichment of Twitter Posts for User Profile Construction. In Proceedings of 8th Extended Semantic Web Conference (ESWC), Heraklion, Crete, Greece, May 2011 [bib, pdf]
15. Ke Tao, Fabian Abel, Qi Gao, Geert-Jan Houben. TUMS: Twitter-based User Modeling Service. In Proceedings of the International Workshop on User Profile Data on the Social Semantic Web (UWeb), ESWC, Heraklion, Crete, Greece, May 2011 [bib, pdf]
16. Fabian Abel, Ilknur Celik, Claudia Hauff, Laura Hollink, Geert-Jan Houben. U-Sem: Semantic Enrichment, User Modeling and Mining Usage Data on the Social Web. In Proceedings of International Workshop on Usage Analysis and the Web of Data (USEWOD), co-located with WWW '11, Hyderabad, India, March 2011 [bib, pdf]

Web Information Systems, Delft University of Technology, Mekelweg 4, 2628 CD Delft, the Netherlands