Adaptive Linking between Text and Photos Using Common Sense Reasoning
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Temporal event clustering for digital photo collections
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Documenting life: videography and common sense
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Inferring generic activities and events from image content and bags of geo-tags
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
What's next?: emergent storytelling from video collection
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Intelligent assistance for conversational storytelling using story patterns
Proceedings of the 16th international conference on Intelligent user interfaces
Raconteur: integrating authored and real-time social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Composition method of folk-tales based on STRIPS-like framework
International Journal of Knowledge and Web Intelligence
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When editing a story from a large collection of media, such as photos and video clips captured from daily life, it is not always easy to understand how particular scenes fit into the intent for the overall story. Especially for novice editors, there is often a lack of coherent connections between scenes, making it difficult for the viewers to follow the story. In this paper, we present Raconteur, a story editing system that helps users assemble coherent stories from media elements, each annotated with a sentence or two in unrestricted natural language. It uses a Commonsense knowledge base, and the AnalogySpace Commonsense reasoning technique. Raconteur focuses on finding story analogies - different elements illustrating the same overall "point", or independent stories exhibiting similar narrative structures.