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Digital Maps and Automatic Narratives for the Interactive Global Histories
in Asian Review of World HistoriesSearch for other papers by Siew Ann Cheong in
Current site
Google Scholar
Search for other papers by Andrea Nanetti in
Current site
Google Scholar
Search for other papers by Mikhail Fhilippov in
Current site
Google Scholar
We describe a vision of historical analysis at the world scale, through the digital assembly of historical sources into a cloud-based database, where machine-learning techniques can be used to summarize the database into a time-integrated actor-to-actor complex network. Using this time-integrated network as a template, we then apply the method of automatic narratives to discover key actors (‘who’), key events (‘what’), key periods (‘when’), key locations (‘where’), key motives (‘why’), and key actions (‘how’) that can be presented as hypotheses to world historians. We show two test cases on how this method works. To accelerate the pace of knowledge discovery and verification, we describe how historians would interact with these automatic narratives through an online, map-based knowledge aggregator that learns how scholars filter information, and eventually takes over this function to free historians from the more important tasks of verification, and stitching together coherent storylines. Ultimately, multiple coherent story-lines that are not necessary compatible with each other can be discovered through human-computer interactions by the map-based knowledge aggregator.
| Insgesamt | Letzte 365 Tage | In den letzten 30 Tagen | |
|---|---|---|---|
| Aufrufe von Kurzbeschreibungen | 25 | 0 | 0 |
| Gesamttextansichten | 549 | 113 | 10 |
| PDF-Downloads | 629 | 119 | 21 |
Digital Maps and Automatic Narratives for the Interactive Global Histories
in Asian Review of World HistoriesSearch for other papers by Siew Ann Cheong in
Current site
Google Scholar
PubMed
Search for other papers by Andrea Nanetti in
Current site
Google Scholar
PubMed
Search for other papers by Mikhail Fhilippov in
Current site
Google Scholar
PubMed
We describe a vision of historical analysis at the world scale, through the digital assembly of historical sources into a cloud-based database, where machine-learning techniques can be used to summarize the database into a time-integrated actor-to-actor complex network. Using this time-integrated network as a template, we then apply the method of automatic narratives to discover key actors (‘who’), key events (‘what’), key periods (‘when’), key locations (‘where’), key motives (‘why’), and key actions (‘how’) that can be presented as hypotheses to world historians. We show two test cases on how this method works. To accelerate the pace of knowledge discovery and verification, we describe how historians would interact with these automatic narratives through an online, map-based knowledge aggregator that learns how scholars filter information, and eventually takes over this function to free historians from the more important tasks of verification, and stitching together coherent storylines. Ultimately, multiple coherent story-lines that are not necessary compatible with each other can be discovered through human-computer interactions by the map-based knowledge aggregator.
| Insgesamt | Letzte 365 Tage | In den letzten 30 Tagen | |
|---|---|---|---|
| Aufrufe von Kurzbeschreibungen | 25 | 0 | 0 |
| Gesamttextansichten | 549 | 113 | 10 |
| PDF-Downloads | 629 | 119 | 21 |
