Negative Statements Considered Useful
Table 9. Negative question answering.
['[BOLD] Query', '[BOLD] Google Web Search # hits', '[BOLD] Google Web Search Correct.(%)', '[BOLD] Google Web Search Rel.(%)', '[BOLD] WDAqua\xa0(Diefenbach et al.,  2017 ) # hits', '[BOLD] WDAqua\xa0(Diefenbach et al.,  2017 ) Correct.(%)', '[BOLD] WDAqua\xa0(Diefenbach et al.,  2017 ) Rel.(%)', '[BOLD] WD SPARQL  # hits', '[BOLD] WD SPARQL  Correct.(%)', '[BOLD] WD SPARQL  Rel.(%)', '[BOLD] Peer-based Inference # hits', '[BOLD] Peer-based Inference Correct.(%)', '[BOLD] Peer-based Inference Rel.(%)']
S6SS2SSS0Px1 Setup We compare them over a highly diverse set of sources: Google Web Search: WDAqua Wikidata SPARQL endpoint: Direct structured access to the Wikidata KB. Peer-based inference. For Google Web Search and WDAqua, we submit the queries in their textual form, and consider answers from Google if they come as structured knowledge panels. For Wikidata and peer-based inference, we transform the queries into SPARQL queries, which we either fully execute over the Wikidata endpoint, or execute the positive part over the Wikidata endpoint, while evaluating the negative part over a dataset produced by our peer-based inference method. As one can see, all methods are able to return highly correct statements, yet Google Web Search and WDAqua struggle to answer 3 and 2 of the queries at all. Wikidata SPARQL returns by far the highest number of results, yet does not perform ranking, thus returns results that are hardly relevant (e.g., a local Latvian actor to the Oscar question). The peer-based inference outperforms it by far in terms of relevance, and we point out that although Wikidata SPARQL results appear highly correct, this has no formal foundation, due to the absence of a stance of OWA KBs towards negative knowledge.