Improving relational queries search results with bag of entities and graph search

Relational queries like “IT jobs in Virginia” or “Authors of Information Retrieval books” etc are common. Standard query parsers that analyze queries as a Bag of Words(BoW) although retrieve quality results, they fail to incorporate the context and correlation in scene. Example query such as “Authors of IR books” can possibly return writers of Search Engine, Data mining, ML, and related topics. In this implementation, a custom query parser is developed which extracts entities from the user’s search query and deduces relationships among the extracted entities to eventually incorporate the related notion into leading search query.

This presentation will walk through the approach and implementation of relational queries parser that leverages Apache Solr’s Entity Tagging, Joins and Graph Traversal APIs and conclude by demonstrating the test setup and discussing empirical results.

Rajani Maski

LucidWorks

I have 12 years of full stack development experience with specialization in Search along with a Master's degree in Information Retrieval and Machine Learning. I have extensive experience in design and engineering of large scale and analytics systems. Challenges involving massive-scale data mining and semantic search are my areas of interest. I currently work at Lucidworks as a Search Consultant/Architect.