On Human-Aware Information Seeking

Simon Schiff; Ralf Möller

On Human-Aware Information Seeking

Abstract

Large parts of scientific work relies on seeking for information in very large datasets and respective metadata (e.g., document repositories on the web, databases, local image collections). Based on a search string or even sample data as a query, information retrieval systems (IR systems) return lists of ranked items that match the query, together with a short preview of the item. Using search strings or example data, it is not easy to express certain information needs, however. In this extended abstract we discuss in what way the interaction of a user with an information retrieval (IR) system can optimized with human-aware collaborative planning strategies.

Original language	English
Title of host publication	Humanities-Centred Artificial Intelligence, CHAI 2021 : CEUR Workshop Proceedings
Publication date	2021
Pages	31-39
Publication status	Published - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)
Research Area: Intelligent Systems

Cite this

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title = "On Human-Aware Information Seeking",

abstract = "Large parts of scientific work relies on seeking for information in very large datasets and respective metadata (e.g., document repositories on the web, databases, local image collections). Based on a search string or even sample data as a query, information retrieval systems (IR systems) return lists of ranked items that match the query, together with a short preview of the item. Using search strings or example data, it is not easy to express certain information needs, however. In this extended abstract we discuss in what way the interaction of a user with an information retrieval (IR) system can optimized with human-aware collaborative planning strategies.",

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PY - 2021

Y1 - 2021

N2 - Large parts of scientific work relies on seeking for information in very large datasets and respective metadata (e.g., document repositories on the web, databases, local image collections). Based on a search string or even sample data as a query, information retrieval systems (IR systems) return lists of ranked items that match the query, together with a short preview of the item. Using search strings or example data, it is not easy to express certain information needs, however. In this extended abstract we discuss in what way the interaction of a user with an information retrieval (IR) system can optimized with human-aware collaborative planning strategies.

AB - Large parts of scientific work relies on seeking for information in very large datasets and respective metadata (e.g., document repositories on the web, databases, local image collections). Based on a search string or even sample data as a query, information retrieval systems (IR systems) return lists of ranked items that match the query, together with a short preview of the item. Using search strings or example data, it is not easy to express certain information needs, however. In this extended abstract we discuss in what way the interaction of a user with an information retrieval (IR) system can optimized with human-aware collaborative planning strategies.

M3 - Conference contribution

SP - 31

EP - 39

BT - Humanities-Centred Artificial Intelligence, CHAI 2021

ER -

On Human-Aware Information Seeking

Abstract

UN SDGs

Research Areas and Centers

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