Email-based activity management systems promise to give users better tools for managing increasing volumes of email, by organizing email according to a user’s activities. Current activity management systems do not automatically classify incoming messages by the activity to which they belong, instead relying on simple heuristics (such as message threads), or asking the user to manually classify incoming messages as belonging to an activity. This paper presents several algorithms for automatically recognizing emails as part of an ongoing activity. Our baseline methods are the use of message reply-to threads to determine activity membership and a naïve Bayes classifier. Our SimSubset and SimOverlap algorithms compare the people involved in an activity against the recipients of each incoming message. Our SimContent algorithm uses IRR (a variant of latent semantic indexing) to classify emails into activities using similarity based on message contents. An empirical evaluation shows that each of these methods provide a significant improvement to the baseline methods. In addition, we show that a combined approach that votes the predictions of the individual methods performs better than each individual method alone.

In email conversational analysis, it is often useful to trace the the intents behind each message exchange. In this paper, we consider classification of email messages as to whether or not they contain certain intents or email-acts, such as “propose a meeting ” or “commit to a task”. We demonstrate that exploiting the contextual information in the messages can noticeably improve email-act classification. More specifically, we describe a combination of n-gram sequence features with careful message preprocessing that is highly effective for this task. Compared to a previous study (Cohen et al., 2004), this representation reduces the classification error rates by 26.4 % on average. Finally, we introduce Ciranda: a new open source toolkit for email speech act prediction. 1

In this paper, we introduce a formal email workflow model based on traditional email, which enables the user to define and execute ad-hoc workflows in an intuitive way. This model paves the way for semantic annotation of implicit, well-defined workflows, thus making them explicit and exposing the missing information in a machine processable way. Grounding this work within the Social Semantic Desktop [1] via appropriate ontologies means that this information can be exploited for the benefit of the user. This will have a direct impact on their personal information management- given email is not just a major channel of data exchange between desktops, but it also serves as a virtual working environment where people collaborate. Thus the presented workflow model will have a concrete manifestation in the creation, organization and exchange of semantic desktop data.

Our use of ordinary desktop applications (such as email, Web, calendars) is often a manifestation of the activities with which we are engaged (Moran, Cozzi, & Farrell 2005). Planning a conference trip involves sending travel expense forms, and visits to airline and hotel sites. Renovating a kitchen involves sketches, product specifications, emails with the architect and spreadsheets for tracking expenses. Every enterprise has (often implicit) processes for managing customer queries, requesting maintenance, hiring a new employee, purchasing equipment, and so on. Unfortunately, ordinary desktop applications do not know anything about these activities. Within an enterprise, many activities have been formalized into business workflows such as hiring or ordering equipment. However, the way people interact with these workflows is often through email and

Abstract—Despite persisting in popularity email is still plagued with information overload, hindering the workflow of data the user has to handle. While the revolutionization of the Web by the Semantic Web is underway, we aspire to use the same technology to enhance electronic mail with useful semantics. In so doing we will tackle one of the largest flaws of the email communication genre- the lack of shared expectations about the form and content of the interaction. This can be attributed to the lack of explicit semantics covering the context and content of exchanged email messages. Earlier research showed that email content can be captured by applying speech act theory. We will refine and extend this work to develop an ontology for email speech acts and outline non-deterministic models to support the user in deciding the best course of action upon sending or receiving an email.

Email is the most common collaborative tool in use today. Although originally designed as an asynchronous communication tool, it is being used increasingly for information management, coordination and collaboration tasks. For effective collaborative work, email must be designed that meets users ’ needs and their experience. The traditional approach to designing interfaces has been increasingly criticised because of the gaps between research results and practical design, especially concerning requirements. Requirements elicitation is a key to the success of the development of all email applications. Activity theory incorporates the notions of intentionality, history, mediation, motivation, understanding, culture and community into design. In particular, it provides a framework in which the critical issue of context can be taken into account. This paper describes the use of activity theory for the requirements analysis of a collaborative email system for a manufacturing company, XBC Ltd.

Abstract. Despite persisting in popularity, email is still plagued with information overload, hindering the workflow of data handled by the user. Just as Semantic Web technologies promise to revolutionize the Web, we aspire to use the same technology to enhance electronic mail with useful semantics. Thus we will tackle one of the largest flaws of the email communication genre- the lack of shared expectations about the form and content of the interaction, which can be attributed to the lack of explicit semantics covering context and content of exchanged messages. Earlier research showed that email content can be captured by applying speech act theory. We will refine and extend this work to develop an email speech act ontology and outline a non-deterministic model that predicts the user’s best course of action upon sending or receiving an email.

Knowledge workers must manage large numbers of simultaneous, ongoing projects that collectively involve huge numbers of resources (documents, emails, web pages, calendar items, etc). An activity database that captures the relationships among projects, resources, and time can drive a variety of tools that save time and increase productivity. To maximize net time savings, we would prefer to build such a database automatically, or with as little user effort as possible. In this paper, we present several sets of features and algorithms for predicting the project associated with each action a user performs on the desktop. Key to our methods is salience, the notion that more recent activity is more informative. By developing novel features that represent salience, we were able to learn models that outperform both a simple benchmark and an expert system tuned specifically for this task on real-world data from five users.

In this paper we provide a summary of work that has been pursued in the area of Semantic Email, with a particular focus on our work in the area. The aim of this paper is to provide a status quo for this topic, as well as to generate ideas and discussions that could evolve the topic and take it to new heights. We finish off by outlining future directions for evaluation, improvement as well as extension of our current technologies. The motivation for our work arises from a vision of a framework that supports the exchange of semanticallyannotated email in order to expose implicit knowledge about the following to machines:

Abstract. Semanta is a system supporting Semantic Email, implemented as an add-in to two popular Mail User Agents, using existing email transport technology and integrated with the Social Semantic Desktop. It enables machines to support email users with correctly interpreting, handling and keeping track of action items within email messages, visualizing email workflows, and extracting tasks and appointments from email messages.