We propose a resource-based Named Entity Classification (NEC) system, which combines named entity extraction with simple language-independent heuristics. Large lists (gazetteers) of named entities are automatically extracted making use of semi-structured information from the Wikipedia, namely infoboxes and category trees. Languageindependent heuristics are used to disambiguate and classify enti...

We are interested in developing a method to accurately infer the type of an edge based on the neighboring entities and the local graph structure that it is embedded in. Working with a modified Freebase network, we project a node into a latent space learned from the neighboring relations with different entities and use its affinity to other nodes in such a space for making predictions. With this...

This paper presents our system for the “Making Sense of Microposts 2014 (#Microposts2014)” challenge. Our system is based on AIDA, an existing system that links entity mentions in natural language text to their corresponding canonical entities in a knowledge base (KB). AIDA collectively exploits the prominence of entities, contextual similarities, and coherence to effectively disambiguate entit...

In this work we address the problem of visualizing the uncertainty in sensed data for manufacturing applications Constructing geometric models for the objects from sense data is the intermediate step in a reverse engineering manufacturing system Sensors are usually inaccurate providing uncertain sense information We construct geometric entities with uncertainty models for the objects under cons...