id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_n76p34ulzzhl3bferrd3xk3pgu	Bhavana Dalvi Mishra	Domain-Targeted, High Precision Knowledge Extraction	2017	14	.pdf	application/pdf	8285	721	66	high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. and a novel canonical schema learning algorithm (called CASI), that produces high precision knowledge targeted to a particular domain in our case, elementary science. able to extract (subject,predicate,object) tuples relevant to a domain with precision in excess of 80%. Second, we present a novel canonical schema induction method (called CASI) that identifies clusters of similar-meaning predicates, and maps them use an (independent) corpus of domain text to characterize the target science knowledge, and measure useful, these resources have been constructed to target only a small set of relations, providing only limited coverage for a domain of interest. this work we combine semantic high-quality features from WordNet, Moby thesaurus with weak distributional similarity features from AMIE to generate schema mapping rules. Table 3: Precision and coverage of tuple-expressible elementary science knowledge by existing resources vs.	./cache/work_n76p34ulzzhl3bferrd3xk3pgu.pdf	./txt/work_n76p34ulzzhl3bferrd3xk3pgu.txt