Concept Similarity Measures the Understanding Between two Agents Jesús-M. Olivares-Ceja Adolfo...
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Transcript of Concept Similarity Measures the Understanding Between two Agents Jesús-M. Olivares-Ceja Adolfo...
Concept Similarity Measures the Understanding Between two Agents
Jesús-M. Olivares-Ceja
Adolfo Guzmán-Arenaswww.jesusolivares.com [email protected]
CIC - IPN
MEXICO
Research Center for ComputingCentro de Investigación en Computación
Finding similar concepts
thing (thing)
hardware(tool,hardware)
key (key)
data (data, item)
data_key (key)hammer (hammer)
thing (thing)
tool (tool)
tool_key (key)
vegetable (vegetable)
screwdriver (screwdriver)
citric(citric)
apple (apple)
lemon (lemon)
orange (orange)
wrench (key, wrench)
Finding similar concepts
thing (thing)
hardware(tool,hardware)
key (key)
data (data, item)
data_key (key)hammer (hammer)
thing (thing)
tool (tool)
tool_key (key)
vegetable (vegetable)
screwdriver (screwdriver)
citric(citric)
apple (apple)
lemon (lemon)
orange (orange)
wrench (key, wrench)
concept words
Finding similar concepts
thing (thing)
hardware(tool,hardware)
key (key)
data (data, item)
data_key (key)hammer (hammer)
thing (thing)
tool (tool)
tool_key (key)
vegetable (vegetable)
screwdriver (screwdriver)
citric(citric)
apple (apple)
lemon (lemon)
orange (orange)
wrench (key, wrench)
concept words
concept words
SIM algorithm
SIM has four cases
[Olivares 2002][Guzmán 2003]
A: Both parent and concept maps
B: Parent map, concept does not
C: Concept map, parent does not
D: None maps
SIM algorithm: case A
sv=1
fruit_vegetable(fruit, vegetable)
garlic_onion(garlic, onion)
vegetable(vegetable)
onion (onion)
white_onion (onion)
yellow_onion (onion)
purple_onion (onion)
SIM algorithm: case B
sv=0.75
citrus(citrus, citric)
lime(lime)
[color=green, texture=smooth. ...]
citric(citric)
celementine (clementine)
[color=green, texture=smooth,...]
SIM algorithm: case C
season_fruit(season_fruit)
apple(apple)
[shape=round, color=yellow. ...]
solid_food(solid_food)
apple (apple)
[shape=round, color=red,...]
sv=0.75
SIM algorithm: case Dthing
(thing, something)
perishable(perishable)
data_model(data_model, database)
relational_model (relational_model)
frozen_food (frozen_food)
meat(meat)
fruit_vegetable (...)
sv=0
Conclusion
No common ontology is needed for inter-agent communication
It is possible to discover concept cB which is most similar to CA
(sim discovers CB with sv [0,1])
It is possible for an agent to know how well it understands another agent
(du measures that, du [0,1])
Future Work
Improve knowledge representation expresiveness
Automatic knowledge extraction using NL techniques to build ontologies
Ontology merging (learning between two agents)
SIM should be tested with real knowledge to be tunned
SIM algorithm: sv case B
Call sim recursively to confirm that pB is the ancestor of cA
If the pB' found is thing sv=0 otherwise
(a special son of pB is searched in OB that)
most properties matches (using sim)
sv = fraction of similar properties
if not found
(check among the sons or grandsons of the father of pB)
sv = (fraction of similar properties) * (0.8)
or
sv = (fraction of similar properties) * (0.8)2
if still not found
return “son_of” pB, sv = 0.5