A Survey of Graph Theory and Applications in Neo4J - Talk

This is a link to a talk given at a recent meet-up in Arlington, VA.

The talk starts out with pretty introductory material but as it progresses it gets more interesting.  Definitely worth a read during a treadmill session.

Here is another relevant link.

My opinion of Neo4J after using for 1 year for experimental purposes is that it is a decent application but I highly doubt its scalability for big data.  I never tested this but it is a hunch based on my use.

Also if you are using Neo4J to store triples, no, don't do that, it is way too much work.  Just use a triple store.

Using PageRank to Locally Partition A Graph

Using PageRank to Locally Partition A Graph

Paper Summary - Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference - Part 1

Toward Conditional Models of Identity Uncertainty
with Application to Proper Noun Coreference
A. McCallum and B. Wellner


This paper is interesting. They make the point that pairwise decisions may not always be independent of others. One may be able to resolve inconsistencies by using a dependence model. They mention work, Relational Probabilistic Model, which captures this dependence. However since it is a generative model, they state this could lead to complexities due to many features with varying degrees of granularity. They discuss Hidden Markov models and conditional random fields briefly and Relational Markov networks as a similar model but improved classification.

They then discuss their work specifically which is "three conditional undirected graphical
models for identity uncertainty" which make the coreference decisions. Their first model connects mentions, entity-assignments, and each attribute of the mention. Edges indicate dependence. There is the concept of a clique, parameters may be part of different cliques which results in patterns of parameters called clique templates. Parts of the graph that depend on a number of entities are removed and replaced with random variables indicating coreference (Read this paper again to make sure we are clear on this). Per-entity attribute nodes are removed and replaced with attributes of mention. They then use graph partitioning. There is a lot in this paper and really requires another read to understand their methods better.

Hadoop Meet-up - Large Scale Graph Processing On HBase and Map/Reduce on Greenplum

I attended the Hadoop Meet-up on Tuesday titled "Large Scale Graph Processing On HBase and Map/Reduce on Greenplum". You can view the event here.

I attended this event for two reasons: It has been a couple of years since I worked intimately with Hadoop and I wanted to see how others are using it. I was also hoping the discussion on Large Scale Graph Processing would be useful for my research.

Though the presentations were somewhat stimulating, I didn't find much I could use for my work.

Paper Summary - Performing Object Consolidation on the Semantic Web Data Graph

Hogan, A.; Harth, A.; and Decker, S. 2007. Performing object
consolidation on the semantic web data graph. In In Proceedings
of I3: Identity, Identifiers, Identification. Workshop at 16th
International World Wide Web Conference (WWW2007).

This paper describes identities and the integration of data. They present a method for merging instances across multiple data sources (**large scale**). They describe how they determine two instances represent the same entity using inverse functional properties. Their dataset includes over 72 million instances (wow).

Key points:

• There isn't much agreement on use of common URIs to identify entities (optional in RDF) so URI represents multiple instances at times
  
• There is a lack of formal specification for determining equivalences among entities
  
• Existing methods that perform object consolidation rely upon probabilistic methods
  
• The use of inverse functional properties in the Semantic Web world is widely used
  
  
  
  
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