  Many projects have applied knowledge patterns (KPs) to the retrieval of
specialized information. Yet terminologists still rely on manual analysis of
concordance lines to extract semantic information, since there are no
user-friendly publicly available applications enabling them to find knowledge
rich contexts (KRCs). To fill this void, we have created the KP-based
EcoLexicon Semantic SketchGrammar (ESSG) in the well-known corpus query system
Sketch Engine. For the first time, the ESSG is now publicly available inSketch
Engine to query the EcoLexicon English Corpus. Additionally, reusing the ESSG
in any English corpus uploaded by the user enables Sketch Engine to extract
KRCs codifying generic-specific, part-whole, location, cause and function
relations, because most of the KPs are domain-independent. The information is
displayed in the form of summary lists (word sketches) containing the pairs of
terms linked by a given semantic relation. This paper describes the process of
building a KP-based sketch grammar with special focus on the last stage,
namely, the evaluation with refinement purposes. We conducted an initial
shallow precision and recall evaluation of the 64 English sketch grammar rules
created so far for hyponymy, meronymy and causality. Precision was measured
based on a random sample of concordances extracted from each word sketch type.
Recall was assessed based on a random sample of concordances where known term
pairs are found. The results are necessary for the improvement and refinement
of the ESSG. The noise of false positives helped to further specify the rules,
whereas the silence of false negatives allows us to find useful new patterns.
