  Named Entity Disambiaguation (NED) is a central task for applications dealing
with natural language text. Assume that we have a graph based knowledge base
(subsequently referred as Knowledge Graph) where nodes represent various real
world entities such as people, location, organization and concepts. Given data
sources such as social media streams and web pages Entity Linking is the task
of mapping named entities that are extracted from the data to those present in
the Knowledge Graph. This is an inherently difficult task due to several
reasons. Almost all these data sources are generated without any formal
ontology; the unstructured nature of the input, limited context and the
ambiguity involved when multiple entities are mapped to the same name make this
a hard task. This report looks at two state of the art systems employing two
distinctive approaches: graph based Accurate Online Disambiguation of Entities
(AIDA) and Mined Evidence Named Entity Disambiguation (MENED), which employs a
statistical inference approach. We compare both approaches using the data set
and queries provided by the Knowledge Base Population (KBP) track at 2011 NIST
Text Analytics Conference (TAC). This report begins with an overview of the
respective approaches, followed by detailed description of the experimental
setup. It concludes with our findings from the benchmarking exercise.
