Simone Braun

Applying quantified self approaches to support reflective learning

This paper presents a framework for technical support of reflective learning, derived from a unification of reflective learning theory with a conceptual framework of Quantified Self tools -- tools for collecting personally relevant information for gaining self-knowledge. Reflective learning means returning to and evaluating past experiences in order to promote continuous learning and improve future experiences. Whilst the reflective learning theories do not sufficiently consider technical support, Quantified Self (QS) approaches are rather experimental and the many emergent tools are disconnected from the goals and benefits of their use. This paper brings these two strands into one unified framework that shows how QS approaches can support reflective learning processes on the one hand and how reflective learning can inform the design of new QS tools for informal learning purposes on the other hand.

Context-Aware workplace learning support: concept, experiences, and remaining challenges

Workplace learning offers the unique possibility of the immediacy of purpose and real-world context. In order to leverage on this, we have developed a context-aware method to support workplace learning. In this paper, we want to describe the concept of context-steered learning, both from a content-driven and communication-driven perspective, and present corresponding system functionality primitives.

People Tagging and Ontology Maturing: Toward Collaborative Competence Management

Competence management has received increasing attention as an implementation of a systematic approach to human resources management, e.g., part total of quality management approaches such as European Foundation of Quality Management (EFQM). Furthermore, trends such as recurring shortage of skilled workers and the anticipated demographic changes have led to an increasing awareness about employees’ capabilities.

Semantic people tagging and ontology maturing: an enterprise social media approach to competence management

Semantic people tagging is an Enterprise 2.0-style approach to making expertise and individual capabilities transparent. By combining it with a collaborative ontology editor and thus the possibility to construct a shared vocabulary and understanding, it can be a supplement for cumbersome competence management, or expert finder solutions, which lack acceptance among employees, and suffer from outdated data. It gives human resources a timely overview of available and required competencies based on peer reviews and actual usage. However, it also needs to be tailored to the cultural characteristics of a specific company. Therefore, we have developed a design framework for semantic people tagging. We want to present the general approach based on the ontology maturing concept of gradual formalisation and its implementation based on a social semantic bookmarking system. Focus group interviews with HR experts have further have yielded insights into the wider context and validated the concept. The system has been ...

Building Expert Recommenders from Email-Based Personal Social Networks

In modern organisations there is the necessity to collaborate with people and establish interpersonal relationships. Contacting the right person is crucial for the success of the performed daily tasks. Personal email corpora contain rich information about all the people the user knows and their activities. Thus, an analysis of a person’s emails allows automatically constructing a realistic image of the surroundings of that person. This chapter aims to develop ExpertSN, a personalised Expert Recommender tool based on email Data Mining and Social Network Analysis. ExpertSN constructs a personal social network from the email corpus of a person by computing profiles—including topics represented by keywords and other attributes such as recency of communication—for each contact found in the emails and by extracting relationships between people based on measures such as co-occurrence in To and CC fields of the emails or reciprocity of communication. Having constructed such a personal social network, we then consider its application for people search in a given work context. Through an analysis of several use cases, we have derived requirements for a query language that allows exploiting the personal social network for people search, taking into account a variety of information needs that go well beyond classical expert search scenarios known from the literature. We further discuss the application of the people search interface in a personal task management environment for effectively retrieving collaborators for a work task. Finally, we report on a user study undertaken to evaluate the personal social network in ExpertSN that shows very promising results.

Using machine learning to support continuous ontology development

This paper presents novel algorithms to support the continuous development of ontologies; i.e. the development of ontologies during their use in social semantic bookmarking, semantic wiki or other social semantic applications. Our goal is to assist users in placing a newly added concept in a concept hierarchy. The proposed algorithm is evaluated using a data set from Wikipedia and provides good quality recommendation. These results point to novel possibilities to apply machine learning technologies to support social semantic applications.

Using the Ontology Maturing Process Model for Searching, Managing and Retrieving Resources with Semantic Technologies

Semantic technologies are very helpful in improving existing systems for searching, managing and retrieving of resources, e.g. image search, bookmarking or expert finder systems. They enhance these systems through background knowledge stored in ontologies. However, in most cases, resources in these systems change very fast. In consequence, they require a dynamic and agile change of underlying ontologies. Also, the formality of these ontologies must fit the users needs and capabilities and must be appropriate and usable. Therefore, a continuous, collaborative and work or task integrated development of these ontologies is required. In this paper, we present how these requirements occur in real world applications and how they are solved and implemented using our Ontology Maturing Process Model.

Feedback in social semantic applications

This article examines the role of feedback mechanisms in social semantic web applications. It introduces different social semantic applications and the function that system feedback can play in these, although only a small portion of possible roles of feedback in such applications is addressed by the state of the art. The authors present an approach with four concrete feedback mechanisms and an experiment on the use of explicit feedback to foster user contribution and motivation. Finally, the article details the large number of open research questions in this area.

Semantics in Knowledge Management

This chapter exemplarily illustrates the role that semantic technologies can play in knowledge management. Starting from a conceptual overview of knowledge management, the role of semantic technologies is explored along two dimensions: (1) on the one hand, the degree of formality of explicit knowledge (from tags in folksonomies to F-Logic rules); and (2) on the other hand, the degree of externalization of knowledge (from implicit knowledge in human’s heads to actionable knowledge in expert systems). Several examples from industry and research are used to illustrate operating points along these dimensions.

Evaluation of the Navigation through Image Parts in the ImageNotion Application

In our work on the ImageNotion methodology and tools, we apply semantic technologies on image archives. In this paper, we show evaluation results on our work on the user interface for semantic image search and expected navigation through image parts. We conducted an online survey with more than hundred participants. A unique feature of our evaluation is that our evaluators filled the survey based on a concrete, working semantic application, i.e., based on the publicly available online version of our system.