In this paper, we proposed the learning resource ontology(LRO) models to formally describe learning content and learning context, respectively. In addition to utilizing the models...
Zongkai Yang, Tao Huang, Qingtang Liu, Xia Li, Bei...
In this paper we propose a random set framework for learning linguistic models for prediction problems. We show how we can model prediction problems based on learning linguistic p...
This paper analyzes the contribution of semantic roles to TimeML event recognition and classification. For that purpose, an approach using conditional random fields with a variety...
This paper presents a method to infer hidden semantic cues by accumulating the knowledge learned from relevance feedback sessions. We propose to explicitly represent a semantic sp...
A shape feature by itself is not sufficient for effective 3D model retrieval. Long-lasting semantics shared by a community as well as a short-lived intention of a user determines ...