An Empirical Investigation of Implicit and Explicit Knowledge-Enhanced Methods for Ad Hoc Dataset Retrieval

Weiqing Luo, Qiaosheng Chen, Zhiyang Zhang, Zixian Huang, Gong Cheng

Submission Type: Regular Long Paper

Submission Track: Information Retrieval and Text Mining

Keywords: ad hoc dataset retrieval, dataset search, dense retrieval, semantic search

TL;DR: We investigate whether existing dense reranking and KB-based reranking methods are effective in ad hoc dataset retrieval.

Abstract: Ad hoc dataset retrieval has become an important way of finding data on the Web, where the underlying problem is how to measure the relevance of a dataset to a query. State-of-the-art solutions for this task are still lexical methods, which cannot capture semantic similarity. Semantics-aware knowledge-enhanced retrieval methods, which achieved promising results on other tasks, have yet to be systematically studied on this specialized task. To fill the gap, in this paper, we present an empirical investigation of the task where we implement and evaluate, on two test collections, a set of implicit and explicit knowledge-enhancement retrieval methods in various settings. Our results reveal the unique features of the task and suggest an interpolation of different kinds of methods as the current best practice.

Submission Number: 5339