Example-based Exploration: Exploring Knowledge through Examples

Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis

Abstract:

Exploration is one of the primordial ways to accrue knowledge about the world and its nature. As we accumulate, mostly automatically, data at unprecedented volumes and speed, our datasets have become complex and hard to understand. In this context, exploratory search provides a handy tool for progressively gather the necessary knowledge by starting from a tentative query that can provide cues about the next queries to issue.

An exploratory query should be simple enough to avoid complicate declarative languages (such as SQL or SPARQL) and convoluted mechanism, and at the same time retain the flexibility and expressiveness required to express complex information needs. Recently, we have witnessed a rediscovery of the so called example-based methods, in which the user, or the analyst circumvent query languages by using examples as input.

This shift in semantics has led to a number of methods receiving as query a set of example members of the answer set. The search system then infers the entire answer set based on the given examples and any additional information provided by the underlying database. In this tutorial, we present an excursus over the main example-based methods for exploratory analysis. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. We conclude by providing a unifying view of this query-paradigm and identify new exciting research directions.

The tutorial will be presented in May, at ESWC'20 See the official program.

The content of this tutorial has been expanded in a book:
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You can also visit the official page on data-exploration.ml