Fast Top-k Area Topics Extraction with Knowledge Base

TL;DR

This paper introduces FastKATE, a novel method for efficiently extracting top-k representative research topics in AI using knowledge base embeddings, with proven effectiveness and real-time performance.

Contribution

It formulates the top-k topic extraction as an NP-hard problem and proposes a fast heuristic algorithm leveraging knowledge base embeddings, demonstrating superior results.

Findings

Effective in real-world datasets

Returns results in less than 1 second

Outperforms several alternative methods

Abstract

What are the most popular research topics in Artificial Intelligence (AI)? We formulate the problem as extracting top-$k$ topics that can best represent a given area with the help of knowledge base. We theoretically prove that the problem is NP-hard and propose an optimization model, FastKATE, to address this problem by combining both explicit and latent representations for each topic. We leverage a large-scale knowledge base (Wikipedia) to generate topic embeddings using neural networks and use this kind of representations to help capture the representativeness of topics for given areas. We develop a fast heuristic algorithm to efficiently solve the problem with a provable error bound. We evaluate the proposed model on three real-world datasets. Experimental results demonstrate our model's effectiveness, robustness, real-timeness (return results in $<1$s), and its superiority over several alternative methods.