Michael Kotzyba

Graph clusterings with overlaps: Adapted quality indices and a generation model

Many real-world networks have a structure of overlapping cohesive groups. In order to uncover this structure several clustering algorithms have been developed. In this paper, we focus on the evaluation of these algorithms. Quality measures are commonly used for this purpose and provide a means to assess the quality of a derived cluster structure. Currently, there are too few measures for graph clusterings with overlaps available that would enable a meaningful evaluation, even though many well studied crisp quality measures exist. In order to expand the pool of overlapping measures we propose three methods to adapt existing crisp quality measures so that they can handle graph overlaps appropriately. We demonstrate our methods on the well known measures Density, Modularity and Conductance. We also propose an enhancement of an existing modularity measure for networks with overlapping structure. We analyse the proposed quality indices using experiments on artificial graphs that possess overlapping structure. For this evaluation, we apply a graph generation model to create clustered graphs with overlaps that are similar to real-world networks, i.e., their node degree and cluster size distribution follow a power law.

Describing Human Emotions Through Mathematical Modelling

Abstract To design a companion technology we focus on the appraisal theory model to predict emotions and determine the appropriate system behaviour to support Human-Computer-Interaction. Until now, the implementation of emotion processing was hindered by the fact that the theories needed originate from diverging research areas, hence divergent research techniques and result representations are present. Since this difficulty arises repeatedly in interdisciplinary research, we investigated the use of mathematical modelling as an unifying language to translate the coherence of appraisal theory. We found that the mathematical category theory supports the modelling of human emotions according to the appraisal theory model and hence assists the implementation.

Knowledge Journey Exhibit: Towards Age-Adaptive Search User Interfaces

We describe an information terminal that supports interactive search with an age-adaptable search user interface whose main focus group are young users. The terminal enables a flexible adaptation of the search user interface to address changing requirements of users at different age groups. The interface is operated using touch interactions as they are considered to be more natural for children than using a mouse. Users search within a safe environment; For this purpose a search index was created using a focused crawler.

Professional Collaborative Information Seeking: On Traceability and Creative Sensemaking

The development of systems to support collaborative information seeking is a challenging issue for many reasons. Besides the support expected for an individual user, such as query formulation, relevance judgement, result set organization and summarization, the smooth exchange of search related information within the team of users seeking information has to be supported. This imposes strong requirements on visualization and interaction to enable user to easily trace and interpret the search activities of other team members and to jointly make sense of gathered information in order to solve the initial information need. In this paper, we briefly motivate specific requirements with a focus on collaborative professional search, review existing work and point out major challenges. In addition, we briefly introduce a system that has been specifically developed to support collaborative technology search.

Towards Identifying User Intentions in Exploratory Search using Gaze and Pupil Tracking

Exploration in large multimedia collections is challenging because the user often navigates into misleading directions or information areas. The vision of our project is to develop an assistive technology that is able to support the individual user and enhance the efficiency of an ongoing exploratory search. Such a technical search aid should be able to find out about the users current interests and goals. Respective parameters can be found in the central and in the peripheral nervous system as well as in overt behavior. Therefore, we aim at using eye movements, pupillometry and EEG to assess respective information. Here, we describe the set-up and the first results of a preliminary user study investigating the effects of searching an image collection on eye movements and pupil dilations. First data show that numbers of fixation, fixation durations as well as pupil dilations differ systematically when looking at a subsequently selected target as compared with not selected items. These results support our vision that further research additionally investigating EEG can in fact result in better predicting the searchers goals and next choices.

Professional Collaborative Information Seeking: Towards Traceable Search and Creative Sensemaking

The development of systems to support collaborative information seeking is a challenging issue for many reasons. Besides the expected support of an individual user in tasks such as keyword based query formulation, relevance judgement, result set organization and summarization, the smooth exchange of search related information within a team of users seeking information has to be supported. This imposes strong requirements on visualization and interaction to enable user to easily trace and interpret the search activities of other team members and to jointly make sense of gathered information in order to satisfy an initial information need. In this paper, we briefly motivate specific requirements with a focus on collaborative professional search, review existing work and propose an adapted model for professional collaborative information seeking. In addition, we discuss the results of a use case study and point out major challenges in professional collaborative search. Finally, we briefly introduce a system that has been specifically developed to support collaborative technology search.

Interpreting and Leveraging Browser Interaction for Exploratory Search Tasks.

In this paper we introduce a novel approach for modeling and interpreting search behavior for exploratory search by using a so called exploration graph. We use an existing methodology of logging and analyzing user interactions with a web browser and add an additional interpretation step that can be used, e. g. to integrate sensemaking or browsing patterns into the log data. We conducted a user study and are able to show that: (a) interaction logs can be interpreted semantically, (b) semantic interpretations lead to a more connected exploration graph, and (c) multiple (even contradicting) interpretations of the same search behavior may exist at the same time. We also show how our theoretical model can be applied in the area of professional search by incorporating insights gained from the model into novel recommendation and machine learning approaches.

Exploration of Web Search Results Based on the Formal Concept Analysis

In this paper, we present an approach to support exploratory search by structuring search results based on concept lattices, which are created on the fly using advanced methods from the area of Formal Concept Analysis (FCA). The main aim of the approach is to organize query based search engine results (e.g. web documents) as a hierarchy of clusters that are composed of documents with similar attributes. The concept lattice provides a structured view on the query-related domains and hence can improve the understanding of document properties and shared features. Additionally, we applied a fuzzy extension of FCA in order to support the usage of different types of attributes within the analyzed query results set. The approach has been integrated into an interactive web search interface. It provides a smooth integration of keyword-based web search and interactive visualization of concept lattice and its concepts in order to support complex search tasks.

Model-Based Frameworks for User Adapted Information Exploration: An Overview

The target group of search engine users in the Internet is very wide and heterogeneous. The users differ in background, knowledge, experience, etc. That is why, in order to find relevant information, such search systems not only have to retrieve web documents related to the search query but also have to consider and adapt to the user’s interests, skills, preferences and context. In addition, numerous user studies have revealed that the search process itself can be very complex, in particular if the user is not providing well-defined queries to find a specific piece of information, but is exploring the information space. This is very often the case if the user is not completely familiar with the search topic and is trying to get an overview of or learn about the topic at hand. Especially in this scenario, user- and task-specific adaptations might lead to a significant increase in retrieval performance and user experience. In order to analyze and characterize the complexity of the search process, different models for information(-seeking) behavior and information activities have been developed. In this chapter, we discuss selected models, with a focus on models that have been designed to cover the needs of individual users. Furthermore, an aggregated framework is proposed to address different levels of information(-seeking) behavior and to motivate approaches for adaptive search systems. To enable Companion-Systems to support users during information exploration, the proposed models provide solid and suitable frameworks to allow cooperative and competent assistance.

Modeling Aspects in Human-Computer Interaction: Adaptivity, User Characteristics and Evaluation

During system interaction, the user’s emotions and intentions shall be adequately determined and predicted to recognize tendencies in his or her interests and dispositions. This allows for the design of an evolving search user interface (ESUI) which adapts to changes in the user’s emotional reaction and the users’ needs and claims.