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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2019

17.12.2018 | Regular Paper

F2ConText: how to extract holistic contexts of persons of interest for enhancing exploratory analysis

verfasst von: Md Abdul Kader, Arnold Priguna Boedihardjo, Mahmud Shahriar Hossain

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2019

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Abstract

A wide variety of publicly available heterogeneous data has provided us with an opportunity to meander through contextual snippets relevant to a particular event or persons of interest. One example of a heterogeneous source is online news articles where both images and text descriptions may co-exist in documents. Many of the images in a news article may contain faces of people. Names of many of the faces may not appear in the text. An expert on the topic may be able to identify people in images or at least recognize the context of the faces who are not widely known. However, it is difficult as well as expensive to employ topic experts of news topics to label every face of a massive news archive. In this paper, we describe an approach named F2ConText that helps analysts build contextual information, e.g., named entity context and geographical context of facial images found within news articles. Our approach extracts facial features of the faces detected in the images of publicly available news articles and learns probabilistic mappings between the features and the contents of the articles in an unsupervised manner. Afterward, it translates the mappings to geographical distributions and generates a contextual template for every face detected in the collection. This paper demonstrates three empirical studies—related to construction of context-based genealogy of events, tracking of a contextual phenomenon over time, and creation of contextual clusters of faces—to evaluate the effectiveness of the generated contexts.
Vorheriger Artikel Effective and efficient location influence mining in location-based social networks
Nächster Artikel Short-term trend prediction in financial time series data

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Codes and data are provided here: http://​dal.​cs.​utep.​edu/​projects/​storyboarding/​KAIS/​. Password: 16context.
 
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Metadaten
Titel
F2ConText: how to extract holistic contexts of persons of interest for enhancing exploratory analysis
verfasst von
Md Abdul Kader
Arnold Priguna Boedihardjo
Mahmud Shahriar Hossain
Publikationsdatum
17.12.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1304-9

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