Abstract-Context-aware user interfaces facilitate the user interaction by suggesting or pre lling data derived from the user's current context. This raises the problem of determining which context information can be used as input for which interaction element in the user interface. This task is especially challenging as the texts that describe the elements, e.g. their labels, often differ in the terminology used. In this paper, we present a novel mapping process for that purpose which (i) combines the advantages of string-based and semantic similarity measures to bridge the vocabulary gap between context and UI element, and which (ii) is able to automatically extend its vocabulary by observing the user's interactions. We show that these two features dramatically increase the quality of the resulting mapping. Unlike previous approaches, the proposed mapping process does not require any training or manually tagged data. Further, it does not only use the label to describe the context and UI elements, but additional texts like their tooltips.  Keywords-Intelligent Nowadays applications get more and more complex. To facilitate the interaction with an application, we need user interfaces (UIs) that provide proactive assistance, for example by suggesting which values to enter in a form. The importance of these proactive suggestions, especially for mobile usage, is stressed by Rukzio [1]. He found that users are four times faster on a smart phone when they just have to correct pre lled form entries compared to entering the information from scratch. Which data should be suggested thereby strongly depends on the user's context. This context information ranges from physical data (like the user's location) to "virtual" data (like calendar entries or data the user entered). For example, data that is entered by the user is especially useful for tasks where the same information needs to be provided in several forms, e.g. when planing a trip which includes renting a car, booking a ight, etc. UIs that are aware of the user's current context, and use it to provide context-aware suggestions are called context-aware UIs. An example screenshot of such a context-aware UI is shown in Figure 1 . It suggests the user which data to enter in the input elds by querying the user's calendar and his current location. In many cases, the terminology used in the UI differs from the one used to describe the context. For example, the location information in a calendar entry has the label "location" whereas the location information for the UI in Figure 1 is labeled with "to". Thus, we need a mapping process that is able to bridge the existing vocabulary gap. Thereby, we can neither rely on pre-tagged data, as we cannot tag all possible domains the user might encounter in advance, nor can we collect suf cient training data. Building on our preliminary study in [2], we present a novel approach for mapping context information to UI elements that combines string-based and semantic similarity measures, and also learns from observing the user's interactions. In the remainder of this paper, we rst brie y describe how UI elements and context elements are represented and how context information is gathered. In Section IV, we present our novel mapping process, and describe how the resulting mapping between context and UI elements can be applied by a context-aware UI. Finally, we report on the evaluation of our mapping process. The task of mapping context information to input elements is strongly related to ontology mapping [3] and database schema matching [4] , where concepts have to be mapped to ontology entries or column names, respectively. However, in these areas most approaches bene t from additional information like constraints or instances that are more distinctive than the input element's label alone or they rely on a large amount of training data. Both is not available for context-aware UIs. Also related is the research on the deep web 1 , as it is concerned with mapping textual representations of several web forms. For example, Wu et al. [5] use cosine similarity for determining the similarity of label and name attributes. However, it also relies on a large corpus of training data. 1 Deep web refers to all information in the web that cannot be accessed via conventional search engines following hyperlinks.
Hartmann REF explored the feasibility of building an auto form fill solution that is context aware, using a mapping between a context store and the user interface of the form to get more accurate labels.
Context-Aware Form Filling for Web Applications
2009 IEEE International Conference on Semantic Computing
2009 IEEE International Conference on Semantic Computing
Computer Science