Application Tutorial

Note

The aim of this tutorial is to show you how to build a custom natural language interface to your own database using an example.

To illustrate how to use quepy as a framework for natural language interface for databases, we will build (step by step) an example application to access DBpedia.

The finished example application can be tried online here: Online demo

The finished example code can be found here: Code

The first step is to select the questions that we want to be answered with dbpedia’s database and then we will develop the quepy machinery to transform them into SPARQL queries.

Selected Questions

In our example application, we’ll be seeking to answer questions like:

Who is <someone>, for example:

  • Who is Tom Cruise?
  • Who is President Obama?

What is <something>, for example:

  • What is a car?
  • What is the Python programming language?

List <brand> <something>, for example:

  • List Microsoft software
  • List Fiat cars

Starting a quepy project

To start a quepy project, you must create a quepy application. In our example, our application is called dbpedia, and we create the application by doing:

$ quepy.py startapp dbpedia

You’ll find out that a folder and some files where created. It should look like this:

$ cd dbpedia
$ tree .

.
├── dbpedia
│   ├── __init__.py
│   ├── parsing.py
│   ├── dsl.py
│   └── settings.py
└── main.py

1 directory, 4 files

This is the basic structure of every quepy project.

  • dbpedia/parsing.py: the file where you will define the regular expressions that will match natural language questions and transform them into an abstract semantic representation.
  • dbpedia/dsl.py: the file where you will define the domain specific language of your database schema. In the case of SPARQL, here you will be specifing things that usually go in the ontology: relation names and such.
  • dbpedia/settings.py: the configuration file for some aspects of the installation.
  • main.py: this file is a optional kickstart point where you can have all the code you need to interact with your app. If you want, you can safely remove this file.

Configuring the application

First make sure you have already downloaded the necesary data for the nltk tagger. If not check the installation section.

Now edit dbpedia/settings.py and add the path to the nltk data to the NLTK_DATA variable. This file has some other configuration options, but we are not going to need them for this example.

Also configure the LANGUAGE, in this example we’ll use sparql.

Note

What’s a tagger anyway?

A “tagger” (in this context) is a linguistic tool help analyze natural language. It’s composed of:

If this is too much info for you, you can just treat it like a black box and it will be enough in the Quepy context.

Defining the regex

Note

To handle regular expressions, quepy uses refo, an awesome library to work with regular expressions as objects. You can read more about refo here.

We need to define the regular expressions that will match natural language questions and transform them into an abstract semantic representation. This will define especifically which questions the system will be able to handle and what to do with them.

In our example, we’ll be editing the file dbpedia/parsing.py. Let’s look at an example of regular expression to handle “What is ...” questions. The whole definition would look like this:

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from refo import Group, Question
from quepy.dsl import HasKeyword
from quepy.parsing import Lemma, Pos, QuestionTemplate

from dsl import IsDefinedIn

class WhatIs(QuestionTemplate):
    """
    Regex for questions like "What is ..."
    Ex: "What is a car"
    """

    target = Question(Pos("DT")) + Group(Pos("NN"), "target")
    regex = Lemma("what") + Lemma("be") + target + Question(Pos("."))

    def interpret(self, match):
        thing = match.target.tokens
        target = HasKeyword(thing)
        definition = IsDefinedIn(target)
        return definition

Now let’s discuss this procedure step by step.

First of all, note that regex handlers need to be a subclass from quepy.parsing.QuestionTemplate. They also need to define a class attribute called regex with a refo regex.

Then, we describe the structure of the input question as a regular expression, and store it in the regex attribute. In our example, this is done in Line 14:

regex = Lemma("what") + Lemma("be") + target + Question(Pos("."))

This regular expression matches questions of the form “what is X?”, but also “what was X?”, “what were X?” and other variants of the verb to be because it is using the lemma of the verb in the regular expression. Note that the X in the question is defined by a variable called target, that is defined in Line 13:

target = Question(Pos("DT")) + Group(Pos("NN"), "target")

The target variable matches a string that will be passed on to the semantics to make part of the final query. In this example, we define that we want to match optionally a determiner (DT) followed by a noun (NN) labeled as “target”.

Note that quepy can access different levels of linguistic information associated to the words in a question, namely their lemma and part of speech tag. This information needs to be associated to questions by analyzing them with a tagger.

Finally, if a regex has a successful match with an input question, the interpret method will be called with the match. In Lines 16 to 22, we define the interpret method, which specifies the semantics of a matched question:

def interpret(self, match):
    thing = match.target.tokens
    target = HasKeyword(thing)
    definition = IsDefinedIn(target)
    return definition

In this example, the contents of the target variable are the argument of a HasKeyword predicate. The HasKeyword predicate is part of the vocabulary of our specific database. In contrast, the IsDefinedIn predicate is part of the abstract semantics component that is described in the next section.

Defining the domain specific language

Quepy uses an abstract semantics as a language-independent representation that is then mapped to a query language. This allows your questions to be mapped to different query languages in a transparent manner.

In our example, the domain specific language is defined in the file dbpedia/dsl.py.

Let’s see an example of the dsl definition. The predicate IsDefinedIn was used in Line 21 of the previous example:

definition = IsDefinedIn(target)

IsDefinedIn is defined in the dsl file as follows:

from quepy.dsl import FixedRelation

class IsDefinedIn(FixedRelation):
    relation = "rdfs:comment"
    reverse = True

This means that IsDefinedIn is a Relation where the subject has rdf:comment. By creating a quepy class, we provide a further level of abstraction on this feature which allows to integrate it in regular expressions seamlessly.

The reverse part of the deal it’s not easy to explain, so bear with me. When we say relation = "rdfs:comment" and definition = IsDefinedIn(target) we are stating that we want

?target rdfs:comment ?definition

But how does the framework knows that we are not trying to say this?:

?definition rdfs:comment ?target

Well, that’s where reverse kicks in. If you set it to True (it’s False by default) you get the first situation, if not you get the second situation.

Using the application

With all that set, we can now use our application. In the main.py file of our example there are some lines of code to use the application.

import quepy
dbpedia = quepy.install("dbpedia")
target, query, metadata = dbpedia.get_query("what is a blowtorch?")
print query

This code should be enough to obtain the following query:

PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX quepy: <http://www.machinalis.com/quepy#>

SELECT DISTINCT ?x1 WHERE {
  ?x0 quepy:Keyword "blowtorch".
  ?x0 rdfs:comment ?x1.
}

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