Philipp Heim

RelFinder: Revealing Relationships in RDF Knowledge Bases

The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.

Interactive relationship discovery via the semantic web

This paper presents an approach for the interactive discovery of relationships between selected elements via the Semantic Web. It emphasizes the human aspect of relationship discovery by offering sophisticated interaction support. Selected elements are first semi-automatically mapped to unique objects of Semantic Web datasets. These datasets are then crawled for relationships which are presented in detail and overview. Interactive features and visual clues allow for a sophisticated exploration of the found relationships. The general process is described and the RelFinder tool as a concrete implementation and proof-of-concept is presented and evaluated in a user study. The application potentials are illustrated by a scenario that uses the RelFinder and DBpedia to assist a business analyst in decision-making. Main contributions compared to previous and related work are data aggregations on several dimensions, a graph visualization that displays and connects relationships also between more than two given objects, and an advanced implementation that is highly configurable and applicable to arbitrary RDF datasets.

The RelFinder user interface: interactive exploration of relationships between objects of interest

Being aware of the relationships that exist between objects of interest is crucial in many situations. The RelFinder user interface helps to get an overview: Even large amounts of relationships can be visualized, filtered, and analyzed by the user. Common concepts of knowledge representation are exploited in order to support interactive exploration both on the level of global filters and single relationships. The RelFinder is easy-to-use and works on every RDF knowledge base that provides standardized SPARQL access

Graph-based Visualization of Requirements Relationships

Understanding the relationships between requirements is important in order to understand the requirements themselves. Existing requirements management tools mainly use lists, tables, trees, and matrices to visualize requirements and their interrelations. However, all these visualization forms have a limited capability to show multiple relationships of different types. In this paper, we propose to extend traditional requirements analysis and management by a graph-based visualization that allows to represent multidimensional relations in a direct and flexible way. In particular, we propose a special presentation form that enables the exploration of requirements along their relationships and facilitates understanding of dependencies between requirements.

SemLens: visual analysis of semantic data with scatter plots and semantic lenses

Querying the Semantic Web and analyzing the query results are often complex tasks that can be greatly facilitated by visual interfaces. A major challenge in the design of these interfaces is to provide intuitive and efficient interaction support without limiting too much the analytical degrees of freedom. This paper introduces SemLens, a visual tool that combines scatter plots and semantic lenses to overcome this challenge and to allow for a simple yet powerful analysis of RDF data. The scatter plots provide a global overview on an object collection and support the visual discovery of correlations and patterns in the data. The semantic lenses add dimensions for local analysis of subsets of the objects. A demo accessing DBpedia data is used for illustration.

Involving End Users in Distributed Requirements Engineering

Active involvement of end users in the development of interactive systems is both highly recommended and highly challenging. This is particularly true in settings where the requirements of a large number of geographically distributed users have to be taken into account. In this paper, we address this problem by introducing an integrated, web-based approach that enables users to easily express their ideas on how the interaction with a system could be improved. In addition, the user input is contextualized, allowing for highly structured means to access, explore, and analyze the user requirements.

Exploiting the Semantic Web for Interactive Relationship Discovery in Technology Enhanced Learning

This paper presents the concept of Interactive Relationship Discovery (IRD) and illustrates its application potentials in technology enhanced learning. Datasets of the Semantic Web are exploited to support learners in understanding how things are related. The approach is based on a user-oriented process model that has been implemented in an easy-to-use tool. A particular emphasis is on the interactive and subjective aspects of relationship discovery. Evaluation results support the applicability and potential of the approach in learning contexts. Benefits and limitations compared to related work are summarized in a final discussion.

Exploring Relationships between Annotated Images with the ChainGraph Visualization

Understanding relationships and commonalities between digital contents based on metadata is a difficult user task that requires sophisticated presentation forms. In this paper, we describe an advanced graph visualization that supports users with these activities. It reduces several problems of common graph visualizations and provides a specific chain arrangement of nodes that facilitates visual tracking of relationships. We present a concrete implementation for the exploration of relationships between images based on shared tags. An evaluation with a comparative user study shows good performance results on several dimensions. We therefore conclude that the ChainGraph approach can be considered a serious alternative to common graph visualizations in situations where relationships and commonalities between contents are of interest. After a discussion of the limitations, we finally point to some application scenarios and future enhancements.

Web-based Stakeholder Participation in Distributed Requirements Elicitation

Today, outsourcing and globally distributed development are common in software engineering industry. This situation raises demands for new forms of stakeholder participation and interaction in requirements elicitation. This poster describes an approach that enables stakeholders to collaboratively participate in requirements elicitation by providing web-based tool support. It combines approaches from research areas such as participatory design, social Web, and semantic collaboration.

A model for human-computer interaction in the semantic web

For the general public, the breakthrough of the Semantic Web as an important addition and further development of the WWW has not yet taken place. This is primarily due to its yet not obvious and largely still not existing benefits for the average Web user. To address this problem, we advocate a greater focus on the topic of human-computer interaction in the Semantic Web. Since to date, however, there are only fragmentary considerations on this subject, in this paper we present the first time a general model specifically designed for the human-computer interaction in the Semantic Web and propose it as a basis for work in this area. The model builds on the concept of interactive alignment, which is known from human-to-human communication. In this concept, human and computer inform each other iteratively and in short intervals in what way they understood each others in order to be able to identify misunderstandings quickly and thus correct them early on. The model provides a comprehensive description and thus a better understanding of the processes, dependencies and ways of cooperation involved within human-computer interaction in the Semantic Web which is the key to new ideas and innovations in this area. Once the mechanism of human-computer interaction in the Semantic Web is understood, new applications can be designed and new business opportunities can be discovered more easily so that the benefits of the Semantic Web can be made available also for the wider public.