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Table of Contents
Nymology DB
Nymology uses a database Nymbase with the following specs.
Tables with Common fields
COMMON FIELDS:
name = entry word/concept
by = entry author
src = original source
url = info link
area = field of study
z = generality (0.0-1.0)
Phrase:
qode = single-Q template
subs = NEROS-string
depth = substitution count
realm = domains
Fable:
qode = multi-Q template
subs = NEROS-string
depth = substitution count
width = quadranym count
realm = domains
Quadranym:
pos = part-of-speech
realm = domains
e = expansion
r = reduction
o = objective
s = subjective
epos = expansion POS
rpos = reduction POS
opos = objective POS
spos = subjective POS
Story:
phrases = names (CSV)
quadranym = names (CSV)
depth = pair count
realm = domains
Tale:
fable = Fable (foreign key)
quadranym = names (CSV)
Polynym:
mode = part/step/type
depth = nym count
p1 - p12 = nyms
Polymap:
pa = Polynym A (foreign key)
pb = Polynym B (foreign key)
r1 - r12 = element-to-element relation (relations from ConceptNet)
Tables without Common fields
Fortune:
body = original text
qode = enqoded text
q1 = Q matches 1 sub
q2 = Q matches 2+ subs
subs = NEROS-string
depth = substitution count
Winner:
by = author
app = game played
round = rounds played
score = high score
Technical Specs
Nymology runs on Python, Django and PostgreSQL with help from:
- ConceptNet - semantic network API
- spaCy - NLP tools
- pattern.en - NLP conjugator
- HighCharts - JavaScript charting library
