Proactive Query Expansion for Streaming Data Using External Source

TL;DR

This paper presents an automated query expansion pipeline for streaming data, leveraging external sources and probabilistic models to improve information retrieval during emergent events.

Contribution

It introduces a novel method combining Dynamic Eigenvector Centrality and LDA for real-time query expansion using external data sources in streaming environments.

Findings

Enhanced retrieval of relevant tweets during Baltimore protests

Effective detection of emergent events through eigenvector centrality

Improved query relevance using external data sources

Abstract

Query expansion is the process of reformulating the original query by adding relevant words. Choosing which terms to add in order to improve the performance of the query expansion methods or to enhance the quality of the retrieved results is an important aspect of any information retrieval system. Adding words that can positively impact the quality of the search query or are informative enough play an important role in returning or gathering relevant documents that cover a certain topic can result in improving the efficiency of the information retrieval system. Typically, query expansion techniques are used to add or substitute words to a given search query to collect relevant data. In this paper, we design and implement a pipeline of automated query expansion. We outline several tools using different methods to expand the query. Our methods depend on targeting emergent events in streaming data over time and finding the hidden topics from targeted documents using probabilistic topic models. We employ Dynamic Eigenvector Centrality to trigger the emergent events, and the Latent Dirichlet Allocation to discover the topics. Also, we use an external data source as a secondary stream to supplement the primary stream with relevant words and expand the query using the words from both primary and secondary streams. An experimental study is performed on Twitter data (primary stream) related to the events that happened during protests in Baltimore in 2015. The quality of the retrieved results was measured using a quality indicator of the streaming data: tweets count, hashtag count, and hashtag clustering.