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Use Cases

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Anja Jentzsch edited this page Jul 2, 2014 · 2 revisions

There are two primary use cases when it comes to suggesting properties:

Table of Contents

1) Suggesting generally helpful properties for a newly created item

Even though an official ontology for the items in wikidata does not exist, there are still "classifying" properties/classifiers (e.g InstanceOf, SubclassOf, memberOf etc.). The values of those properties are classes.

Items that belong to the same class often by nature have a lot in common. This makes knowing an item's classifiers combined with their concrete values very useful for suggesting properties.

Therefore a selection of widely used/important classifiers could be suggested as initial properties to a users that wants to create a new item. The idea is that after the property value was filled in, the user can profit from the derived suggestions during further editing.

Due to the fact that there are no predefined/fixed classifiers the first step is to find a way to identify properties that serve as classifiers - a few thoughts regarding this issue:

  • Only properties with the (value) type 'item' should be considered a potential classifier since a class that is used as the value for a classifier is most likely going to be represented by an item in wikidata.
  • Classifier by definition group together similiar objects. So in order to decide if a certain property is potentially used as a classifier it is a good idea to compare items that have the same value for this property and look for patterns like common properties or property values. Comming from a different angle one could also use the fact that the sole information that an item has a certain classifier (not considering its value) is in most cases not very valuable for finding patterns/suggesting properties
  • Frequentliy used classifiers are more relevant for the described use case, because they are more likely to be an useful suggestion for a new item

2) Suggesting properties for an item based on its preexisting properties

Two major steps are necessary in order to be able to do 2):

  1. For every property we need to know how often it is used in general and how often it appears in combination with every single other property. So that given the properties p1 and p2 we can determine the probability for p1 being used together with p2 (and of course the other way around).
    This information can intuitively be stored in a table like structure. This way it is easy to look up the probability of a certain property combination by just finding the corresponding row and clumn.
    The table can be precomputed and only needs to be updated from time to time (updates could be done incrementally)
  2. The next step for suggesting new properties for an item based on its preexisting properties is to create a ranking including every property, which is not already used to describe the item on hand.
This can be done using the information that is stored in the table. An intuitiv algorithm for ranking a certain property p could look like this:
  • look up the probability for all combinations that involve p being used in connection with one of the preexisting properties
  • compute the arithmetic mean of all the probabilities and use it for the ranking.
As the last step the highest ranking properties should be suggested as additional attributes for the item on hand

Once classifiers as described in 1) are identified, classifier/value combinations can be integrated in the previously mentioned table, so that they - like properties - can be suggested and used to suggest properties or other classifier/value pairs respectively.

Other promising Use Cases

Suggesting *misfit* properties

The approach for suggesting new properties could be slightly modified in order to analyze a given set of properties and find the ones that do not fit in with the rest. In terms of suggesting properties an approach for this task could look like the following: Given a set of Properties P and a property p element P, we generate suggestions for P-p and filter the result in order to get the correlation value for p. We do this for every Property in the set and finally filter out the ones with a correlation value that lies below a certain threshold.

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