Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings
Table 3: Results from a CI query to find papers with related titles using proximityAvg(), proximityMax(), and proximityTop2Avg()
['[BOLD] Query Results using ProximityAvg()', 'Cos. Dist.']
In this query, the title of the paper with number 471 is “Native Xquery processing in oracle XMLDB”. In this query, vectors for different tokens in a title are compared in three ways to identify similar titles: proximityAvg() selects the title whose average vectors are close, whereas proximityMax() uses maximum closeness between individual tokens to select related titles. Finally, proximityTop2Avg() uses the average of the top two cosine distances of its two token sequences arguments, thereby ’mixing’ maximum and average. proximityMax() chooses two papers with Xquery in their titles as this is the closest match (1.0), proximityAvg() returns a broader set of papers (note that there is no XML or relational token in the query title.) proximityTop2Avg () uses two top tokens to determine similarity, thus produces a different ordering. For example, the the closest title chosen by proximityTop2Avg() containts both XQuery and XML tokens.