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Stephan Baier

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3 papers

2 author rows

UAI Conference 2018 Conference Paper

Holistic Representations for Memorization and Inference

Yunpu Ma
Marcel Hildebrandt
Volker Tresp
Stephan Baier

In this paper we introduce a novel holographic memory model for the distributed storage of complex association patterns and apply it to knowledge graphs. In a knowledge graph, a labelled link connects a subject node with an object node, jointly forming a subject-predicateobjects triple. In the presented work, nodes and links have initial random representations, plus holistic representations derived from the initial representations of nodes and links in their local neighbourhoods. A memory trace is represented in the same vector space as the holistic representations themselves. To reduce the interference between stored information, it is required that the initial random vectors should be pairwise quasi-orthogonal. We show that pairwise quasi-orthogonality can be improved by drawing vectors from heavy-tailed distributions, e. g. , a Cauchy distribution, and, thus, memory capacity of holistic representations can significantly be improved. Furthermore, we show that, in combination with a simple neural network, the presented holistic representation approach is superior to other methods for link predictions on knowledge graphs.

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IJCAI Conference 2018 Conference Paper

Improving Information Extraction from Images with Learned Semantic Models

Stephan Baier
Yunpu Ma
Volker Tresp

Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects. In particular, a great deal of semantic information is carried in the relationships between objects. We have previously shown, that the combination of a visual model and a statistical semantic prior model can improve on the task of mapping images to their associated scene description. In this paper, we review the model and compare it to a novel conditional multi-way model for visual relationship detection, which does not include an explicitly trained visual prior model. We also discuss potential relationships between the proposed methods and memory models of the human brain.

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NeSy Conference 2017 Conference Paper

Learning with Knowledge Graphs

Volker Tresp
Yunpu Ma
Stephan Baier

In recent years a number of large-scale triple-oriented knowledge graphs have been generated. They are being used in research and in applications to support search, text understanding and question answering. Knowledge graphs pose new challenges for machine learning, and research groups have developed novel statistical models that can be used to compress knowledge graphs, to derive implicit facts, and to detect errors in the knowledge graph. In this paper we decribe the concept of triple-oriented knowledge graphs and corresponding learning approaches. We also discuss episodic knowledge graphs which are able to represent temporal data; learning with episodic data can be the basis for decision support systems, e.g. in a clinical context. Finally we discuss how knowledge graphs can support perception, by mapping subsymbolic sensory inputs, such as images, to semantic triples. A particular feature of our approach would be that perception, episodic memory and semantic memory are highly interconnected and that, in a cognitive interpretation, all rely on the same brain structures.

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