Machine Reading with Background Knowledge
Table 12: Comparision of our approach K-nom, to a baseline that does not make use of background knowledge. Precision is sampled from a total of max(100, recall). Recall is not shown in percentages as we do not have a gold standard for recall on the corpus.
['Relation', '[EMPTY]', 'Recall', 'Precision']
We extracted a total of 2,270,487 compound nouns. From these compound nouns, we extract 10 relations that are expressed by compound nouns and are in the NELL knowledge base. From this dataset we learned 291 mappings from types sequences to relations. Using these mappings we then predicted new relation instances. We compare our system K−nom to a baseline which is not knowledge aware. The baseline is created as follows: we generate sequences that have no awareness of background knowledge by discarding type information from the sequences. For example, the sequence  “author”   becomes  “author” < noun phrase> where “noun phrases” refers to any noun phrase found in text regardless of its type. In cases where the entire template is made up of semantic types only, for example,  , discarding semantic types results in a template which is too general. Therefore, for the baseline, we discarded such permissive templates that would be present a significant disadvantage for the baseline. K-nom also achieves high recall for some of the relations. The reason why K-nom yields low recall for some of the relations ia probably because while those relations are occasionally expressed using compound nouns, they are more commonly expressed in other forms such as using verbs.