A Context-Aware Approach to Entity Linking

A technical paper by Veselin Stoyanov and James Mayfield; Tan Xu and Douglas W. Oard; Dawn Lawrie; Tim Oates and Tim Finin. Entity linking refers to the task of assigning mentions in documents to their corresponding knowledge base entities. Entity linking is a central step in knowledge base population. Current entity linking systems do not explicitly model the discourse context in which the communication occurs.

Connecting Deep Neural Networks with Symbolic Knowledge

Arjun Kumar and Tim Oates Abstract—Neural networks have attracted significant interest in recent years due to their exceptional performance in various domains ranging from natural language processing to image identification and classification. Modern deep neural networks demonstrate state-of-the-art results in complex tasks such as epileptic seizure detection [1] and time series classification [2]. The internal…

Implementing Feedback for Programming by Demonstration

Karan K. Budhraja and Tim Oates Abstract—Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the…

Ecosembles: A Rapidly Deployable Image Classification System Using Feature-Views

Adrian Rosebrock, Tim Oates, Jesus Caban Abstract—Constructing an image classification system using strong, local invariant descriptors is both time consuming and tedious, requiring many experimentations and parameter tunings to obtain an adequately performing model. Furthermore training a system in a given domain and then migrating the model to a separate domain will likely yield poor…

Graph Node Embeddings using Domain-Aware Biased Random Walks

Sourav Mukherjee, PhD; Tim Oates, PhD; Ryan Wright The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of research.…