Analytic Computing

Topics

The focus group Artificial Intelligence and Knowledge Graphs pays special attention to symbolic methods for AI. Symbolic methods are based on explicit, human-readable representations. They usually offer more transparency and better explainability than subsymbolic methods, but can be more difficult to maintain and to learn from data. Knowledge graphs are currently one of the most popular symbolic methods in industry and one of the driving forces behind intelligent software products by companies like eBay, Facebook, Google, IBM and Microsoft[1]. Challenges include building up knowledge graphs from unstructured data, resolving inconsistent and redundant information and the efficient processing of more and more expressive queries over larger and larger knowledge bases.

Some of our current projects include:

KnowGraphs: scale knowledge graphs to be accessible to a wide audience of users across multiple domains including companies (in domains including Industry 4.0, biomedicine, finance, law) of all sizes and even end users (e.g., through personal assistants and web search). The KnowGraphs team focuses on addressing four of the facets of knowledge graph management: representation, construction and maintenance, operation and exploitation.

EVOWIPE, COFFEE: support the reuse of designs and processes in product development by intentional forgetting methods. Intentional forgetting aims at simulating human forgetting and can be seen as a natural counterpart of machine learning. While machine learning deals with adding new knowledge, forgetting deals with removing irrelevant information.

LISeQ: the flexibility of graph-based data models makes them attractive, but programming with such data models is error-prone. Our goal is to allow for type-safe programming with graph data. This entails the integration of logic-based data descriptions into the type checking process of programming languages.

[1] Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale knowledge graphs: Lessons and challenges. Queue, 17(2), 48-75.

The focus group of Artificial Intelligence and Human-Computer Interaction pays special attention on computational methodologies from data science and AI to enhance HCI research and applications to make digital technology more effective, efficient, and expressive to use. In particular, we follow multimodal and natural interaction modalities and styles (e.g., eye gaze, touch, VR, AR, conversational voice or text user interfaces), and enhancement of traditional user interfaces (e.g. Web, GUI). The twofold goal of focus groups is to enhance the accessibility and the usability of interfaces.  We aim to enhance the accessibility of interfaces through novel interaction channels and its adaptation with AI-enabled components. For enhancing usability we understand user behaviour patterns and the semantics of interfaces using machine learning methods. We believe that the intersection of AI and HCI enables users to better explore, steer, and extend their capabilities in challenging tasks. 

Some of our current projects include:

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Description in preparation.

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