Link Prediction Between Structured Geopolitical Events: Models and Experiments

Often thought of as higher-order entities, events have recently become important subjects of research in the computational sciences, including within complex systems and natural language processing (NLP). One such application is event link prediction. Given an input event, event link prediction is t...

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Autor principal:	Mayank Kejriwal
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	event representations representation learning geopolitical event link prediction word embeddings multi-partite networks Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/842bbebe44194e148319c8ff2822d291
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id	oai:doaj.org-article:842bbebe44194e148319c8ff2822d291
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spelling	oai:doaj.org-article:842bbebe44194e148319c8ff2822d2912021-12-01T19:31:36ZLink Prediction Between Structured Geopolitical Events: Models and Experiments2624-909X10.3389/fdata.2021.779792https://doaj.org/article/842bbebe44194e148319c8ff2822d2912021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fdata.2021.779792/fullhttps://doaj.org/toc/2624-909XOften thought of as higher-order entities, events have recently become important subjects of research in the computational sciences, including within complex systems and natural language processing (NLP). One such application is event link prediction. Given an input event, event link prediction is the problem of retrieving a relevant set of events, similar to the problem of retrieving relevant documents on the Web in response to keyword queries. Since geopolitical events have complex semantics, it is an open question as to how to best model and represent events within the framework of event link prediction. In this paper, we formalize the problem and discuss how established representation learning algorithms from the machine learning community could potentially be applied to it. We then conduct a detailed empirical study on the Global Terrorism Database (GTD) using a set of metrics inspired by the information retrieval community. Our results show that, while there is considerable signal in both network-theoretic and text-centric models of the problem, classic text-only models such as bag-of-words prove surprisingly difficult to outperform. Our results establish both a baseline for event link prediction on GTD, and currently outstanding challenges for the research community to tackle in this space.Mayank KejriwalFrontiers Media S.A.articleevent representationsrepresentation learninggeopolitical event link predictionword embeddingsmulti-partite networksInformation technologyT58.5-58.64ENFrontiers in Big Data, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	event representations representation learning geopolitical event link prediction word embeddings multi-partite networks Information technology T58.5-58.64
spellingShingle	event representations representation learning geopolitical event link prediction word embeddings multi-partite networks Information technology T58.5-58.64 Mayank Kejriwal Link Prediction Between Structured Geopolitical Events: Models and Experiments
description	Often thought of as higher-order entities, events have recently become important subjects of research in the computational sciences, including within complex systems and natural language processing (NLP). One such application is event link prediction. Given an input event, event link prediction is the problem of retrieving a relevant set of events, similar to the problem of retrieving relevant documents on the Web in response to keyword queries. Since geopolitical events have complex semantics, it is an open question as to how to best model and represent events within the framework of event link prediction. In this paper, we formalize the problem and discuss how established representation learning algorithms from the machine learning community could potentially be applied to it. We then conduct a detailed empirical study on the Global Terrorism Database (GTD) using a set of metrics inspired by the information retrieval community. Our results show that, while there is considerable signal in both network-theoretic and text-centric models of the problem, classic text-only models such as bag-of-words prove surprisingly difficult to outperform. Our results establish both a baseline for event link prediction on GTD, and currently outstanding challenges for the research community to tackle in this space.
format	article
author	Mayank Kejriwal
author_facet	Mayank Kejriwal
author_sort	Mayank Kejriwal
title	Link Prediction Between Structured Geopolitical Events: Models and Experiments
title_short	Link Prediction Between Structured Geopolitical Events: Models and Experiments
title_full	Link Prediction Between Structured Geopolitical Events: Models and Experiments
title_fullStr	Link Prediction Between Structured Geopolitical Events: Models and Experiments
title_full_unstemmed	Link Prediction Between Structured Geopolitical Events: Models and Experiments
title_sort	link prediction between structured geopolitical events: models and experiments
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/842bbebe44194e148319c8ff2822d291
work_keys_str_mv	AT mayankkejriwal linkpredictionbetweenstructuredgeopoliticaleventsmodelsandexperiments
_version_	1718404641157283840

Link Prediction Between Structured Geopolitical Events: Models and Experiments

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