Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph

verfasst von: Sara Abdollahi, Tin Kuculo, Simon Gottschalk
Abstract: Event-specific document ranking is a crucial task in supporting users when searching for texts covering events such as Brexit or the Olympics. However, the complex nature of events involving multiple aspects like temporal information, location, participants and sub-events poses challenges in effectively modelling their representations for ranking. In this paper, we propose MusQuE (Multi-stage Query Expansion), a multi-stage ranking framework that jointly learns to rank query expansion terms and documents, and in this manner flexibly identifies the optimal combination and number of expansion terms extracted from an event knowledge graph. Experimental results show that MusQuE outperforms state-of-the-art baselines on MS-MARCOEVENT, a new dataset for event-specific document ranking, by 9.1% and more.
Organisationseinheit(en): Forschungszentrum L3S
Typ: Aufsatz in Konferenzband
Seiten: 333-348
Anzahl der Seiten: 16
Publikationsdatum: 16.03.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Theoretische Informatik, Informatik (insg.)
Elektronische Version(en): https://doi.org/10.1007/978-3-031-56060-6_22 (Zugang: Geschlossen)

BibTeX

@inproceedings{ca758c91bace4e7f9b8c4e27fd68c78b,
title = "Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph",
abstract = "Event-specific document ranking is a crucial task in supporting users when searching for texts covering events such as Brexit or the Olympics. However, the complex nature of events involving multiple aspects like temporal information, location, participants and sub-events poses challenges in effectively modelling their representations for ranking. In this paper, we propose MusQuE (Multi-stage Query Expansion), a multi-stage ranking framework that jointly learns to rank query expansion terms and documents, and in this manner flexibly identifies the optimal combination and number of expansion terms extracted from an event knowledge graph. Experimental results show that MusQuE outperforms state-of-the-art baselines on MS-MARCOEVENT, a new dataset for event-specific document ranking, by 9.1% and more.",
keywords = "Document Retrieval, Event Knowledge Graphs, Event-specific Document Ranking, Query Expansion",
author = "Sara Abdollahi and Tin Kuculo and Simon Gottschalk",
note = "Funding Information: This work was partially funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany (“ATTENTION!”, 01MJ22012D).; 46th European Conference on Information Retrieval, ECIR 2024 ; Conference date: 24-03-2024 Through 28-03-2024",
year = "2024",
month = mar,
day = "16",
doi = "10.1007/978-3-031-56060-6_22",
language = "English",
isbn = "9783031560590",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "333--348",
editor = "Nazli Goharian and Nicola Tonellotto and Yulan He and Aldo Lipani and Graham McDonald and Craig Macdonald and Iadh Ounis",
booktitle = "Advances in Information Retrieval",
address = "Germany",
}