Sources of commonsense knowledge aim to support applications in natural language understanding, computer vision, and knowledge graphs. These sources contain complementary knowledge to each other, which makes their integration desired. Yet, such integration is not trivial because of their different foci, modeling approaches, and sparse overlap  . In this paper, we propose to consolidate commonsense knowledge by following five principles . We apply these principles to combine seven key sources into a first integrated CommonSense Knowledge Graph (CSKG). We perform analysis of CSKG and its various text and graph embeddings, showing that CSKG is a well-connected graph and that its embeddings provide a useful entry point to the graph. Moreover, we show the impact of CSKG as a source for reasoning evidence retrieval, and for pre-training language models for generalizable downstream reasoning . CSKG and all its embeddings are made publicly available to support further research on commonsense knowledge integration and reasoning.
Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs. Given their complementarity, their integration is desired. Yet, their different foci, modeling approaches, and sparse overlap make integration difficult . In this paper, we consolidate commonsense knowledge by following five principles , which we apply to combine seven key sources into a first integrated CommonSense Knowledge Graph (CSKG). We analyze CSKG and its various text and graph embeddings, showing that CSKG is well-connected and that its embeddings provide a useful entry point to the graph. We demonstrate how CSKG can provide evidence for generalizable downstream reasoning and for pre-training of language models . CSKG and all its embeddings are made publicly available to support further research on commonsense knowledge integration and reasoning.
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aim to
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These sources contain complementary knowledge to each other, which makes their integration
Given their complementarity, their integration is
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such integration is not trivial because of
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make integration difficult
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propose to
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. We apply these principles
, which we apply
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perform analysis of
analyze
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a
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graph
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Moreover, we show the impact of CSKG as a source for reasoning evidence retrieval,
We demonstrate how CSKG can provide evidence for generalizable downstream reasoning
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language models for generalizable downstream reasoning
of language models