Example-based Search: A new frontier for exploratory search

TY - GEN

T1 - Example-based Search

T2 - 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019

AU - Lissandrini, Matteo

AU - Mottin, Davide

AU - Palpanas, Themis

AU - Velegrakis, Yannis

PY - 2019

Y1 - 2019

N2 - 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) 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.

AB - 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) 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.

UR - http://www.scopus.com/inward/record.url?scp=85073778569&partnerID=8YFLogxK

U2 - 10.1145/3331184.3331387

DO - 10.1145/3331184.3331387

M3 - Article in proceedings

AN - SCOPUS:85073778569

SP - 1411

EP - 1412

BT - SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

PB - Association for Computing Machinery

Y2 - 21 July 2019 through 25 July 2019

ER -