Open knowledge extraction through compositional language processing
Benjamin Van Durme, Lenhart K. Schubert · 2008
We present results for a system designed to perform Open Knowledge Extraction, based on a tradition of compositional language processing, as applied to a large collection of text derived from the Web. Evaluation through manual assessment shows that well-formed propositions of reasonable quality, representing general world knowledge, given in a logical form potentially usable for inference, may be extracted in high volume from arbitrary input sentences. We compare these results with those obtained in recent work on Open Information Extraction, indicating with some examples the quite different kinds of output obtained by the two approaches. Finally, we observe that portions of the extracted knowledge are comparable to results of recent work on class attribute extraction.