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

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

  • Sara Abdollahi
  • Tin Kuculo
  • Simon Gottschalk

Organisationseinheiten

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Details

OriginalspracheEnglisch
Titel des SammelwerksAdvances in Information Retrieval
Untertitel46th European Conference on Information Retrieval, ECIR 2024
Herausgeber/-innenNazli Goharian, Nicola Tonellotto, Yulan He, Aldo Lipani, Graham McDonald, Craig Macdonald, Iadh Ounis
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten333-348
Seitenumfang16
ISBN (elektronisch)978-3-031-56060-6
ISBN (Print)9783031560590
PublikationsstatusVeröffentlicht - 16 März 2024
Veranstaltung46th European Conference on Information Retrieval, ECIR 2024 - Glasgow, Großbritannien / Vereinigtes Königreich
Dauer: 24 März 202428 März 2024

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band14609 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

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.

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Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph. / Abdollahi, Sara; Kuculo, Tin; Gottschalk, Simon.
Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. Hrsg. / Nazli Goharian; Nicola Tonellotto; Yulan He; Aldo Lipani; Graham McDonald; Craig Macdonald; Iadh Ounis. Springer Science and Business Media Deutschland GmbH, 2024. S. 333-348 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14609 LNCS).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Abdollahi, S, Kuculo, T & Gottschalk, S 2024, Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph. in N Goharian, N Tonellotto, Y He, A Lipani, G McDonald, C Macdonald & I Ounis (Hrsg.), Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 14609 LNCS, Springer Science and Business Media Deutschland GmbH, S. 333-348, 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, Großbritannien / Vereinigtes Königreich, 24 März 2024. https://doi.org/10.1007/978-3-031-56060-6_22
Abdollahi, S., Kuculo, T., & Gottschalk, S. (2024). Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph. In N. Goharian, N. Tonellotto, Y. He, A. Lipani, G. McDonald, C. Macdonald, & I. Ounis (Hrsg.), Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024 (S. 333-348). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14609 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-56060-6_22
Abdollahi S, Kuculo T, Gottschalk S. Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph. in Goharian N, Tonellotto N, He Y, Lipani A, McDonald G, Macdonald C, Ounis I, Hrsg., Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. Springer Science and Business Media Deutschland GmbH. 2024. S. 333-348. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-56060-6_22
Abdollahi, Sara ; Kuculo, Tin ; Gottschalk, Simon. / Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph. Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. Hrsg. / Nazli Goharian ; Nicola Tonellotto ; Yulan He ; Aldo Lipani ; Graham McDonald ; Craig Macdonald ; Iadh Ounis. Springer Science and Business Media Deutschland GmbH, 2024. S. 333-348 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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.",
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Download

TY - GEN

T1 - Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph

AU - Abdollahi, Sara

AU - Kuculo, Tin

AU - Gottschalk, Simon

N1 - Funding Information: This work was partially funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany (“ATTENTION!”, 01MJ22012D).

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Y1 - 2024/3/16

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AB - 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.

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KW - Event Knowledge Graphs

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KW - Query Expansion

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