We also compare our results with existing knowledge base to outline the similarities and differences of the granularity and diversity of the harvested knowledge.

0 A comparison of the results with some large existing knowledge bases .

Initially, we decided to conduct an automatic evaluation comparing our results to knowledge bases that have been extracted in a similar way (i.e., through pattern application over unstructured text).

In this section, we compare the performance of our approach with the semantic knowledge base Yago4 that contains 2 million entitiess, 95% of which were manually confirmed to be correct.

We evaluate OntoUSP by using it to extract a knowledge base from biomedical abstracts and answer questions.

Besides, many of them either bootstrap from heuristic patterns (e.g., Hearst patterns (Hearst, 1992)) or build on existing structured or semistructured knowledge bases (e.g., WordNet (Fellbaum, 1998) and Wikipedial), thus are limited in coverage.

Our approach can also leverage existing ontologies and knowledge bases to conduct semi-supervised ontology induction (e.g., by incorporating existing structures as hard constraints or penalizing deviation from them).

These MAP parses formed the knowledge base (KB).

Veale and Hao (2008), however, did not evaluate to which extent their knowledge base of Talking Points and the associated reasoning framework are useful to interpret metaphorical expressions occurring in text.

If the system fails to recognize metonymy, it proceeds to search the knowledge base for a relevant analogy in order to discriminate metaphorical relations from anomalous ones.

met* then searches its knowledge base for a triple containing a hypemym of both the actual argument and the desired argument and finds (thing, use, energy_source), which represents the metaphorical interpretation.

One of the first attempts to create a multipurpose knowledge base of source—target domain mappings is the Master Metaphor List (Lakoff et al., 1991).

Many researchers are trying to use information extraction (IE) to create large-scale knowledge bases from natural language text on the Web.

Many researchers are trying to use IE to create large-scale knowledge bases from natural language text on the Web, but existing relation-specific techniques do not scale to the thousands of relations encoded in Web text — while relation-independent techniques suffer from lower precision and recall, and do not canonicalize the relations.

Wikipedia is an ideal starting point for our longterm goal of creating a massive knowledge base of extracted facts for two reasons.