# Combining Vector Space Embeddings with Symbolic Logical Inference over Open-Domain Text

@inproceedings{Gardner2015CombiningVS, title={Combining Vector Space Embeddings with Symbolic Logical Inference over Open-Domain Text}, author={Matt Gardner and Partha P. Talukdar and Tom Michael Mitchell}, booktitle={AAAI Spring Symposia}, year={2015} }

We have recently shown how to combine random walk inference over knowledge bases with vector space representations of surface forms, improving performance on knowledge base inference. In this paper, we formalize the connection of our prior work to logical inference rules, giving some general observations about methods for incorporating vector space representations into symbolic logic systems. Additionally, we present some promising preliminary work that extends these techniques to learning open…

## 15 Citations

### Explaining automatic answers generated from knowledge base embedding models.

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This work improves an existing method designed to provide explanations for predictions made by embedding models, and focuses on non-relational classifiers (such as deep neural networks).

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This thesis presents methods for reasoning over very large knowledge bases, and shows how to apply these methods to models of machine reading, which can successfully incorporate knowledge base information into machine learning models of natural language.

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It is shown that the random walk probabilities computed by PRA provide no discernible benefit to performance on this task, so they can safely be dropped, and this allows a simpler algorithm for generating feature matrices from graphs, which is called subgraph feature extraction (SFE).

### Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

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It is shown how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.

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This thesis proposes a formulation for abductive reasoning in natural language and shows its effectiveness, especially in domains with limited training data, and presents the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use.

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Experimental results show that with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels.

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This work proposes a framework using energy-based models for multiple structured prediction tasks in Sanskrit, which is an arc-factored model, similar to the graph-based parsing approaches, that enables it to incorporate language-specific constraints to prune the search space and to filter the candidates during inference.

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A new, scalable probabilistic logic called ProPPR is proposed to combine the best of the symbolic and statistical worlds, and a second-order abductive theory, whose parameter corresponds to plausible first-order inference rules is proposed.

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