Publications - Current Year

2024

Conference paper

S. Ghosh, S. Razniewski, D. Graux, and G. Weikum

“CardiO: Predicting Cardinality from Online Sources,” in The ACM Web Conference 2024 (WWW 2024), Singapore, Singapore, 2024.

@inproceedings{Ghosh_WWW2024,
TITLE = {{CardiO}: Predicting Cardinality from Online Sources},
AUTHOR = {Ghosh, Shrestha and Razniewski, Simon and Graux, Damien and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {979-8-4007-0172-6},
DOI = {10.1145/3589335.365147},
PUBLISHER = {ACM},
YEAR = {2024},
BOOKTITLE = {The ACM Web Conference 2024 (WWW 2024)},
EDITOR = {Chua, Tat-Seng and Ngo, Chong-Wah and Lee, Roy Ka-Wei and Kumar, Ravi and Lauw, Hady},
PAGES = {573--576},
ADDRESS = {Singapore, Singapore},
}

Endnote

%0 Conference Proceedings
%A Ghosh, Shrestha
%A Razniewski, Simon
%A Graux, Damien
%A Weikum, Gerhard
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T CardiO: Predicting Cardinality from Online Sources : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-13B6-E
%R 10.1145/3589335.365147
%D 2024
%8 13.05.2024
%B ACM Web Conference
%Z date of event: 2024-05-13 - 2024-05-17
%C Singapore, Singapore
%B The ACM Web Conference 2024
%E Chua, Tat-Seng; Ngo, Chong-Wah; Lee, Roy Ka-Wei; Kumar, Ravi; Lauw, Hady
%P 573 - 576
%I ACM
%@ 979-8-4007-0172-6

Paper

Y. Hu, S. Ghosh, T.-P. Nguyen, and S. Razniewski

“GPTKB: Building Very Large Knowledge Bases from Language Models,” 2024. [Online]. Available: https://arxiv.org/abs/2411.04920.

Abstract

General-domain knowledge bases (KB), in particular the "big three" --
Wikidata, Yago and DBpedia -- are the backbone of many intelligent
applications. While these three have seen steady development, comprehensive KB
construction at large has seen few fresh attempts. In this work, we propose to
build a large general-domain KB entirely from a large language model (LLM). We
demonstrate the feasibility of large-scale KB construction from LLMs, while
highlighting specific challenges arising around entity recognition, entity and
property canonicalization, and taxonomy construction. As a prototype, we use
GPT-4o-mini to construct GPTKB, which contains 105 million triples for more
than 2.9 million entities, at a cost 100x less than previous KBC projects. Our
work is a landmark for two fields: For NLP, for the first time, it provides
\textit{constructive} insights into the knowledge (or beliefs) of LLMs. For the
Semantic Web, it shows novel ways forward for the long-standing challenge of
general-domain KB construction. GPTKB is accessible at gptkb.org.

BibTeX

@online{Hu_2411.04920,
TITLE = {{GPTKB}: Building Very Large Knowledge Bases from Language Models},
AUTHOR = {Hu, Yujia and Ghosh, Shrestha and Nguyen, Tuan-Phong and Razniewski, Simon},
LANGUAGE = {enn},
URL = {https://arxiv.org/abs/2411.04920},
EPRINT = {2411.04920},
EPRINTTYPE = {arXiv},
YEAR = {2024},
ABSTRACT = {General-domain knowledge bases (KB), in particular the "big three" --<br>Wikidata, Yago and DBpedia -- are the backbone of many intelligent<br>applications. While these three have seen steady development, comprehensive KB<br>construction at large has seen few fresh attempts. In this work, we propose to<br>build a large general-domain KB entirely from a large language model (LLM). We<br>demonstrate the feasibility of large-scale KB construction from LLMs, while<br>highlighting specific challenges arising around entity recognition, entity and<br>property canonicalization, and taxonomy construction. As a prototype, we use<br>GPT-4o-mini to construct GPTKB, which contains 105 million triples for more<br>than 2.9 million entities, at a cost 100x less than previous KBC projects. Our<br>work is a landmark for two fields: For NLP, for the first time, it provides<br>\textit{constructive} insights into the knowledge (or beliefs) of LLMs. For the<br>Semantic Web, it shows novel ways forward for the long-standing challenge of<br>general-domain KB construction. GPTKB is accessible at http://gptkb.org.<br>},
}

Endnote

%0 Report
%A Hu, Yujia
%A Ghosh, Shrestha
%A Nguyen, Tuan-Phong
%A Razniewski, Simon
%+ External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
%T GPTKB: Building Very Large Knowledge Bases from Language Models : 
%G enn
%U http://hdl.handle.net/21.11116/0000-0010-133A-8
%U https://arxiv.org/abs/2411.04920
%D 2024
%X   General-domain knowledge bases (KB), in particular the "big three" --<br>Wikidata, Yago and DBpedia -- are the backbone of many intelligent<br>applications. While these three have seen steady development, comprehensive KB<br>construction at large has seen few fresh attempts. In this work, we propose to<br>build a large general-domain KB entirely from a large language model (LLM). We<br>demonstrate the feasibility of large-scale KB construction from LLMs, while<br>highlighting specific challenges arising around entity recognition, entity and<br>property canonicalization, and taxonomy construction. As a prototype, we use<br>GPT-4o-mini to construct GPTKB, which contains 105 million triples for more<br>than 2.9 million entities, at a cost 100x less than previous KBC projects. Our<br>work is a landmark for two fields: For NLP, for the first time, it provides<br>\textit{constructive} insights into the knowledge (or beliefs) of LLMs. For the<br>Semantic Web, it shows novel ways forward for the long-standing challenge of<br>general-domain KB construction. GPTKB is accessible at http://gptkb.org.<br>
%K Computer Science, Computation and Language, cs.CL,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB

Conference paper

Z. Jia, P. Christmann, and G. Weikum

“Faithful Temporal Question Answering over Heterogeneous Sources,” in The ACM Web Conference 2024 (WWW 2024), Singapore, Singapore, 2024.

@inproceedings{Jia_WWW2024,
TITLE = {Faithful Temporal Question Answering over Heterogeneous Sources},
AUTHOR = {Jia, Zhen and Christmann, Philipp and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {979-8-4007-0172-6},
DOI = {10.1145/3589334.3645547},
PUBLISHER = {ACM},
YEAR = {2024},
BOOKTITLE = {The ACM Web Conference 2024 (WWW 2024)},
EDITOR = {Chua, Tat-Seng and Ngo, Chong-Wah and Lee, Roy Ka-We and Kumar, Ravi and Lauw, Hady W.},
PAGES = {2052--2063},
ADDRESS = {Singapore, Singapore},
}

Endnote

%0 Conference Proceedings
%A Jia, Zhen
%A Christmann, Philipp
%A Weikum, Gerhard
%+ External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T Faithful Temporal Question Answering over Heterogeneous Sources : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000E-7D1D-7
%R 10.1145/3589334.3645547
%D 2024
%8 13.05.2024
%B ACM Web Conference
%Z date of event: 2024-05-13 - 2024-05-17
%C Singapore, Singapore
%B The ACM Web Conference 2024
%E Chua, Tat-Seng; Ngo, Chong-Wah; Lee, Roy Ka-We; Kumar, Ravi; Lauw, Hady W.
%P 2052 - 2063
%I ACM
%@ 979-8-4007-0172-6

Conference paper

Z. Jia, P. Christmann, and G. Weikum

“TIQ: A Benchmark for Temporal Question Answering with Implicit Time Constraints,” in The ACM Web Conference 2024 (WWW 2024), Singapore, Singapore, 2024.

@inproceedings{Jia_WWW24,
TITLE = {{TIQ}: {A} Benchmark for Temporal Question Answering with Implicit Time Constraints},
AUTHOR = {Jia, Zhen and Christmann, Philipp and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {979-8-4007-0172-6},
DOI = {10.1145/3589335.3651895},
PUBLISHER = {ACM},
YEAR = {2024},
BOOKTITLE = {The ACM Web Conference 2024 (WWW 2024)},
EDITOR = {Chua, Tat-Seng and Ngo, Chong-Wah and Lee, Roy Ka-Wei and Kumar, Ravi and Lauw, Hady W.},
PAGES = {1394--1399},
ADDRESS = {Singapore, Singapore},
}

Endnote

%0 Conference Proceedings
%A Jia, Zhen
%A Christmann, Philipp
%A Weikum, Gerhard
%+ External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T TIQ: A Benchmark for Temporal Question Answering with Implicit Time Constraints : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-4EDB-4
%R 10.1145/3589335.3651895
%D 2024
%B ACM Web Conference
%Z date of event: 2024-05-13 - 2024-05-17
%C Singapore, Singapore
%B The ACM Web Conference 2024
%E Chua, Tat-Seng; Ngo, Chong-Wah; Lee, Roy Ka-Wei; Kumar, Ravi; Lauw, Hady W.
%P 1394 - 1399
%I ACM
%@ 979-8-4007-0172-6

Conference paper

T.-P. Nguyen, S. Razniewski, and G. Weikum

“Cultural Commonsense Knowledge for Intercultural Dialogues,” in CIKM ’24, 33rd ACM International Conference on Information and Knowledge Management, Boise, ID, USA, 2024.

@inproceedings{Nguyen_CIKM24,
TITLE = {Cultural Commonsense Knowledge for Intercultural Dialogues},
AUTHOR = {Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {979-8-4007-0436-9},
DOI = {10.1145/3627673.3679768},
PUBLISHER = {ACM},
YEAR = {2024},
DATE = {2024},
BOOKTITLE = {CIKM '24, 33rd ACM International Conference on Information and Knowledge Management},
EDITOR = {Serra, Edoardo and Spezzano, Francesca},
PAGES = {1774--1784},
ADDRESS = {Boise, ID, USA},
}

Endnote

%0 Conference Proceedings
%A Nguyen, Tuan-Phong
%A Razniewski, Simon
%A Weikum, Gerhard
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T Cultural Commonsense Knowledge for Intercultural Dialogues : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000E-7348-0
%R 10.1145/3627673.3679768
%D 2024
%B 33rd ACM International Conference on Information and Knowledge Management
%Z date of event: 2024-10-21 - 2024-10-25
%C Boise, ID, USA
%B CIKM '24
%E Serra, Edoardo; Spezzano, Francesca
%P 1774 - 1784
%I ACM
%@ 979-8-4007-0436-9

Conference paper

K. Pal, H. Arnaout, S. Razniewski, and G. Weikum

“FASETS: Discovering Faceted Sets of Entities,” in The ACM Web Conference 2024 (WWW 2024), Singapore, Singapore, 2024.

@inproceedings{Pal_WWW24,
TITLE = {{FASETS}: {D}iscovering Faceted Sets of Entities},
AUTHOR = {Pal, Koninika and Arnaout, Hiba and Razniewski, Simon and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {979-8-4007-0172-6},
DOI = {10.1145/3589335.3651924},
PUBLISHER = {ACM},
YEAR = {2024},
BOOKTITLE = {The ACM Web Conference 2024 (WWW 2024)},
EDITOR = {Chua, Tat-Seng and Ngo, Chong-Wah and Lee, Roy Ka-Wei and Kumar, Ravi and Lauw, Hady W.},
PAGES = {1521--1529},
ADDRESS = {Singapore, Singapore},
}

Endnote

%0 Conference Proceedings
%A Pal, Koninika
%A Arnaout, Hiba
%A Razniewski, Simon
%A Weikum, Gerhard
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T FASETS: Discovering Faceted Sets of Entities : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-4ED6-9
%R 10.1145/3589335.3651924
%D 2024
%B ACM Web Conference
%Z date of event: 2024-05-13 - 2024-05-17
%C Singapore, Singapore
%B The ACM Web Conference 2024
%E Chua, Tat-Seng; Ngo, Chong-Wah; Lee, Roy Ka-Wei; Kumar, Ravi; Lauw, Hady W.
%P 1521 - 1529
%I ACM
%@ 979-8-4007-0172-6

Article

S. Razniewski, H. Arnaout, S. Ghosh, and F. Suchanek

“Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey,” ACM Computing Surveys, vol. 56, no. 6, 2024.

@article{Razniewski24,
TITLE = {Completeness, Recall, and Negation in Open-world Knowledge Bases: {A} Survey},
AUTHOR = {Razniewski, Simon and Arnaout, Hiba and Ghosh, Shrestha and Suchanek, Fabian},
LANGUAGE = {eng},
ISSN = {0360-0300; 1557-7341},
DOI = {10.1145/3639563},
PUBLISHER = {ACM},
ADDRESS = {New York, NY},
YEAR = {2024},
DATE = {2024},
JOURNAL = {ACM Computing Surveys},
VOLUME = {56},
NUMBER = {6},
PAGES = {1--42},
EID = {150},
}

Endnote

%0 Journal Article
%A Razniewski, Simon
%A Arnaout, Hiba
%A Ghosh, Shrestha
%A Suchanek, Fabian
%+ External Organizations
External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
%T Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000E-76FD-1
%R 10.1145/3639563
%D 2024
%J ACM Computing Surveys
%O ACM Comput. Surv. Computing surveys CSUR
%V 56
%N 6
%& 1
%P 1 - 42
%Z sequence number: 150
%I ACM
%C New York, NY
%@ false

Conference paper

T. P. Schrader, L. Lange, S. Razniewski, and A. Friedrich

“QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), Miama, FL, USA, 2024.

@inproceedings{Schrader_EMNLP24,
TITLE = {{QUITE}: {Q}uantifying Uncertainty in Natural Language Text in {B}ayesian Reasoning Scenarios},
AUTHOR = {Schrader, Timo Pierre and Lange, Lukas and Razniewski, Simon and Friedrich, Annemarie},
LANGUAGE = {eng},
ISBN = {979-8-89176-164-3},
URL = {https://aclanthology.org/2024.emnlp-main.153},
PUBLISHER = {Association for Computational Linguistics},
YEAR = {2024},
DATE = {2024},
BOOKTITLE = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)},
EDITOR = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
PAGES = {2634--2652},
ADDRESS = {Miama, FL, USA},
}

Endnote

%0 Conference Proceedings
%A Schrader, Timo Pierre
%A Lange, Lukas
%A Razniewski, Simon
%A Friedrich, Annemarie
%+ External Organizations
External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
%T QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0010-2E26-1
%U https://aclanthology.org/2024.emnlp-main.153
%D 2024
%B Conference on Empirical Methods in Natural Language Processing
%Z date of event: 2024-11-12 - 2024-11-16
%C Miama, FL, USA
%B Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%E Al-Onaizan, Yaser; Bansal, Mohit; Chen, Yun-Nung
%P 2634 - 2652
%I Association for Computational Linguistics
%@ 979-8-89176-164-3
%U https://aclanthology.org/2024.emnlp-main.153.pdf

Paper

S. Singhania, S. Razniewski, and G. Weikum

“Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents,” 2024. .

Abstract

Methods for relation extraction from text mostly focus on high precision, at
the cost of limited recall. High recall is crucial, though, to populate long
lists of object entities that stand in a specific relation with a given
subject. Cues for relevant objects can be spread across many passages in long
texts. This poses the challenge of extracting long lists from long texts. We
present the L3X method which tackles the problem in two stages: (1)
recall-oriented generation using a large language model (LLM) with judicious
techniques for retrieval augmentation, and (2) precision-oriented
scrutinization to validate or prune candidates. Our L3X method outperforms
LLM-only generations by a substantial margin.

BibTeX

@online{Singhania_2405.02732,
TITLE = {Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents},
AUTHOR = {Singhania, Sneha and Razniewski, Simon and Weikum, Gerhard},
LANGUAGE = {eng},
EPRINT = {2405.02732},
EPRINTTYPE = {arXiv},
YEAR = {2024},
ABSTRACT = {Methods for relation extraction from text mostly focus on high precision, at<br>the cost of limited recall. High recall is crucial, though, to populate long<br>lists of object entities that stand in a specific relation with a given<br>subject. Cues for relevant objects can be spread across many passages in long<br>texts. This poses the challenge of extracting long lists from long texts. We<br>present the L3X method which tackles the problem in two stages: (1)<br>recall-oriented generation using a large language model (LLM) with judicious<br>techniques for retrieval augmentation, and (2) precision-oriented<br>scrutinization to validate or prune candidates. Our L3X method outperforms<br>LLM-only generations by a substantial margin.<br>},
}

Endnote

%0 Report
%A Singhania, Sneha
%A Razniewski, Simon
%A Weikum, Gerhard
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-75A0-8
%D 2024
%X   Methods for relation extraction from text mostly focus on high precision, at<br>the cost of limited recall. High recall is crucial, though, to populate long<br>lists of object entities that stand in a specific relation with a given<br>subject. Cues for relevant objects can be spread across many passages in long<br>texts. This poses the challenge of extracting long lists from long texts. We<br>present the L3X method which tackles the problem in two stages: (1)<br>recall-oriented generation using a large language model (LLM) with judicious<br>techniques for retrieval augmentation, and (2) precision-oriented<br>scrutinization to validate or prune candidates. Our L3X method outperforms<br>LLM-only generations by a substantial margin.<br>
%K Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR

Conference paper

A. Tigunova, G. H. Torbati, A. Yates, and G. Weikum

“STAR: Sparse Text Approach for Recommendation,” in CIKM ’24, 33rd ACM International Conference on Information and Knowledge Management, Boise, ID, USA, 2024.

@inproceedings{Tigunova_CIKM24,
TITLE = {{STAR}: {S}parse Text Approach for Recommendation},
AUTHOR = {Tigunova, Anna and Torbati, Ghazaleh Haratinezhad and Yates, Andrew and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {979-8-4007-0436-9},
DOI = {10.1145/3627673.3679999},
PUBLISHER = {ACM},
YEAR = {2024},
DATE = {2024},
BOOKTITLE = {CIKM '24, 33rd ACM International Conference on Information and Knowledge Management},
EDITOR = {Serra, Edoardo and Spezzano, Francesca},
PAGES = {4086--4090},
ADDRESS = {Boise, ID, USA},
}

Endnote

%0 Conference Proceedings
%A Tigunova, Anna
%A Torbati, Ghazaleh Haratinezhad
%A Yates, Andrew
%A Weikum, Gerhard
%+ External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T STAR: Sparse Text Approach for Recommendation : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-FD24-C
%R 10.1145/3627673.3679999
%D 2024
%B 33rd ACM International Conference on Information and Knowledge Management
%Z date of event: 2024-10-21 - 2024-10-25
%C Boise, ID, USA
%B CIKM '24
%E Serra, Edoardo; Spezzano, Francesca
%P 4086 - 4090
%I ACM
%@ 979-8-4007-0436-9

Conference paper

G. H. Torbati, A. Tigunova, G. Weikum, and A. Yates

“Recommendations in Sparse-Data Low-Resource Settings by Constructing Concise User Profiles from Review Text,” in 3rd International Workshop on Industrial Recommendation Systems (at CIKM 2024) (IRS 2024), Boise, ID, USA, 2024.

@inproceedings{Torbati_IRS24,
TITLE = {Recommendations in Sparse-Data Low-Resource Settings by Constructing Concise User Profiles from Review Text},
AUTHOR = {Torbati, Ghazaleh Haratinezhad and Tigunova, Anna and Weikum, Gerhard and Yates, Andrew},
LANGUAGE = {eng},
PUBLISHER = {ACM},
YEAR = {2024},
BOOKTITLE = {3rd International Workshop on Industrial Recommendation Systems (at CIKM 2024) (IRS 2024)},
ADDRESS = {Boise, ID, USA},
}

Endnote

%0 Conference Proceedings
%A Torbati, Ghazaleh Haratinezhad
%A Tigunova, Anna
%A Weikum, Gerhard
%A Yates, Andrew
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
External Organizations
%T Recommendations in Sparse-Data Low-Resource Settings by Constructing Concise User Profiles from Review Text : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0010-0BD1-6
%D 2024
%8 21.10.2024
%B 3rd International Workshop on Industrial Recommendation Systems
%Z date of event: 2024-10-25 - 2024-10-25
%C Boise, ID, USA
%B 3rd International Workshop on Industrial Recommendation Systems (at CIKM 2024)
%I ACM

Conference paper

G. H. Torbati, A. Tigunova, and G. Weikum

“SIRUP: Search-based Book Recommendation Playground,” in WSDM ’24, 17th ACM International Conference on Web Search and Data Mining, Merida, Mexico, 2024.

@inproceedings{TorbatiWSDM24,
TITLE = {{SIRUP}: {S}earch-based Book Recommendation Playground},
AUTHOR = {Torbati, Ghazaleh Haratinezhad and Tigunova, Anna and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {979-8-4007-0371-3},
DOI = {10.1145/3616855.3635692},
PUBLISHER = {ACM},
YEAR = {2024},
DATE = {2024},
BOOKTITLE = {WSDM '24, 17th ACM International Conference on Web Search and Data Mining},
EDITOR = {Ang{\'e}lica Caudillo Mata, Luz and Lattanzi, Silvio and Mu{\~n}oz Medina, Andr{\'e}s and Akoglu, Leman and Gionis, Aristides and Vassilvitskii, Sergei},
PAGES = {1062--1065},
ADDRESS = {Merida, Mexico},
}

Endnote

%0 Conference Proceedings
%A Torbati, Ghazaleh Haratinezhad
%A Tigunova, Anna
%A Weikum, Gerhard
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T SIRUP: Search-based Book Recommendation Playground : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000E-A663-7
%R 10.1145/3616855.3635692
%D 2024
%B 17th ACM International Conference on Web Search and Data Mining
%Z date of event: 2024-03-04 - 2024-03-08
%C Merida, Mexico
%B WSDM '24
%E Ang&#233;lica Caudillo Mata, Luz; Lattanzi, Silvio; Mu&#241;oz Medina, Andr&#233;s; Akoglu, Leman; Gionis, Aristides; Vassilvitskii, Sergei
%P 1062 - 1065
%I ACM
%@ 979-8-4007-0371-3

Conference paper

H. D. Tran, A. Yates, and G. Weikum

“Conversational Search with Tail Entities,” in Advances in Information Retrieval (ECIR 2024), Glasgow, UK, 2024.

@inproceedings{Tran_ECIR24,
TITLE = {Conversational Search with Tail Entities},
AUTHOR = {Tran, Hai Dang and Yates, Andrew and Weikum, Gerhard},
LANGUAGE = {eng},
ISBN = {978-3-031-56059-0},
DOI = {10.1007/978-3-031-56060-6_20},
PUBLISHER = {Springer},
YEAR = {2024},
DATE = {2024},
BOOKTITLE = {Advances in Information Retrieval (ECIR 2024)},
EDITOR = {Goharian, Nazli and Tonellotto, Nicola and He, Yulan and Lipani, Aldo and McDonald, Graham and Macdonald, Craig and Ounis, Iadh},
PAGES = {303--317},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {14609},
ADDRESS = {Glasgow, UK},
}

Endnote

%0 Conference Proceedings
%A Tran, Hai Dang
%A Yates, Andrew
%A Weikum, Gerhard
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T Conversational Search with Tail Entities : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-042C-C
%R 10.1007/978-3-031-56060-6_20
%D 2024
%B 46th European Conference on Information Retrieval
%Z date of event: 2024-03-24 - 2024-03-28
%C Glasgow, UK
%B Advances in Information Retrieval
%E Goharian, Nazli; Tonellotto, Nicola; He, Yulan; Lipani, Aldo; McDonald, Graham; Macdonald, Craig; Ounis, Iadh
%P 303 - 317
%I Springer
%@ 978-3-031-56059-0
%B Lecture Notes in Computer Science
%N 14609