3.2. Ontology

How to use ontologies for matching in Natex.

Ontology

Let us create a dialogue flow to talk about animals:

transitions = {
    'state': 'start',
    '`What is your favorite animal?`': {
        '[{dog, ape, rat}]': {
            '`I love mammals!`': 'end'
        },
        '[{snake, lizard}]': {
            '`Reptiles are slick, haha`': 'end'
        },
        '[{frog, salamander}]': {
            '`Amphibians can be cute :)`': 'end'
        },
        'error': {
            '`I\'ve never heard of that animal.`': 'end'
        }
    }
}

Match

S: What is your favorite animal?
U: I love frog
S: Amphibians can be cute :)

UM1

UM2

For each type of animal, however, the list can be indefinitely long (e.g., there are over 5,400 mammal species). In this case, it is better to use an ontology (e.g., WordNet, FrameNet).

Let us create a JSON file, ontology_animal.json, containing an ontology of animals:

{
    "ontology": {
        "animal": ["mammal", "fish", "bird", "reptile", "amphibian"],
        "mammal": ["dog", "ape", "rat"],
        "reptile": ["snake", "lizard"],
        "amphibian": ["frog", "salamander"],
        "dog": ["golden retriever", "poodle"]
    }
}

• #2: the key ontology is paired with a dictionary as a value.

• #3: the key animal represents the category, and its subcategories are indicated in the list.

• #4-6: each subcategory, mammal, reptile, and amphibian, has its own subcategory.

• #7: the ontology hierarchy: animal -> mammal -> dog.

Given the ontology, the above transitions can be rewritten as follow:

transitions = {
    'state': 'start',
    '`What is your favorite animal?`': {
        '[#ONT(mammal)]': {
            '`I love mammals!`': 'end'
        },
        '[#ONT(reptile)]': {
            '`Reptiles are slick, haha`': 'end'
        },
        '[#ONT(amphibian)]': {
            '`Amphibians can be cute :)`': 'end'
        },
        'error': {
            '`I\'ve never heard of that animal.`': 'end'
        }
    }
}

• #4: matches the key "mammal" as well as its subcategories: "dog", "ape", and "rat".

• #5: matches the key "reptile" as well as its subcategories: "snake" and "lizard".

• #6: matches the key "amphibian" as well as its subcategories: "frog" and "salamander".

M1

S: What is your favorite animal?
U: I love frogs
S: Amphibians can be cute :)

Unlike set matching, ontology matching handles plurals (e.g., "frogs").

M2

UM1

Currently, ontology matching does not handle plurals for compound nouns (e.g., "golden retrievers"), which will be fixed in the following version.

Expression

It is possible that a category is mentioned in a non-canonical way; the above conditions do not match "puppy" because it is not introduced as a category in the ontology. In this case, we can specify the aliases as "expressions":

{
    "ontology": {
        "animal": ["mammal", "fish", "bird", "reptile", "amphibian"],
        "mammal": ["dog", "ape", "rat"],
        "reptile": ["snake", "lizard"],
        "amphibian": ["frog", "salamander"],
        "dog": ["golden retriever", "poodle"]
    },

    "expressions": {
        "dog": ["canine", "puppy"]
    }
}

• #10: the key expressions is paired with a dictionary as a value.

• #4: allows matching "canine" and "puppy" for the dog category.

Once you load the updated JSON file, it now understands "puppy" as an expression of "dog":

Match

S: What is your favorite animal?
U: I cannot live without my puppy!
S: I love mammals!

It is possible to match "puppy" by adding the term as a category of "dog" (#7). However, it would not be a good practice as "puppy" should not be considered a subcategory of "dog".

Variable

Values matched by the ontology can also be stored in variables:

transitions = {
    'state': 'start',
    '`What is your favorite animal?`': {
        '[$FAVORITE_ANIMAL=#ONT(mammal)]': {
            '`I love` $FAVORITE_ANIMAL `!`': 'end'
        },
        '[$FAVORITE_ANIMAL=#ONT(reptile)]': {
            '$FAVORITE_ANIMAL `are slick, haha`': 'end'
        },
        '[$FAVORITE_ANIMAL=#ONT(amphibian)]': {
            '$FAVORITE_ANIMAL `can be cute :)`': 'end'
        },
        'error': {
            '`I\'ve never heard of that animal.`': 'end'
        }
    }
}

• #4,7,10: the matched term gets stored in the variable FAVORITE_ANIMAL.

• #5,8,11: the system uses the value of FAVORITE_ANIMAL to generate the response.

M1

S: What is your favorite animal?
U: I love frogs
S: frogs can be cute :)

M2

Loading

The custom ontology must be loaded to the knowledge base of the dialogue flow before it runs:

df = DialogueFlow('start', end_state='end')
df.knowledge_base().load_json_file('resources/ontology_animal.json')
df.load_transitions(transitions)

• #1: loads the ontology in ontology_animal.json to the knowledge base of df.

Code Snippet

def natex_ontology() -> DialogueFlow:
    transitions = {
        'state': 'start',
        '`What is your favorite animal?`': {
            '[$FAVORITE_ANIMAL=#ONT(mammal)]': {
                '`I love` $FAVORITE_ANIMAL `!`': 'end'
            },
            '[$FAVORITE_ANIMAL=#ONT(reptile)]': {
                '$FAVORITE_ANIMAL `are slick, haha`': 'end'
            },
            '[$FAVORITE_ANIMAL=#ONT(amphibian)]': {
                '$FAVORITE_ANIMAL `can be cute :)`': 'end'
            },
            'error': {
                '`I\'ve never heard of that animal.`': 'end'
            }
        }
    }

    df = DialogueFlow('start', end_state='end')
    df.knowledge_base().load_json_file('resources/ontology_animal.json')
    df.load_transitions(transitions)
    return df
    
if __name__ == '__main__':
    natex_ontology().run()