Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features

Abstract

Abstract-Effective search in structured information based on textual user input is of high importance in thousands of applications. Query expansion methods augment the original query of a user with alternative query elements with similar meaning to increase the chance of retrieving appropriate resources. In this work, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Open Data. We evaluate the effectiveness of each feature individually as well as their combinations employing several machine learning approaches. The evaluation is carried out on a training dataset extracted from the QALD question answering benchmark. Furthermore, we propose an optimized linear combination of linguistic and lightweight semantic features in order to predict the usefulness of each expansion candidate. Our experimental study shows a considerable improvement in precision and recall over baseline approaches.

Exploiting Image Generality for Lexical Entailment Detection

“…Automatic detection of lexical entailment is useful for a number of NLP tasks including search query expansion (Shekarpour et al, 2013), recognising textual entailment (Garrette et al, 2011), metaphor detection (Mohler et al, 2013), and text generation (Biran and McKeown, 2013). Given two semantically related words, a key aspect of detecting lexical entailment, or the hyponym-hypernym relation, is the generality of the hypernym compared to the hyponym.…”

Survey on challenges of Question Answering in the Semantic Web

“…However, AQE can be used as a backup method in case there is no direct match. One of the surveyed publications is an experimental study [120] that evaluates the impact of AQE on SQA. It has analyzed different lexical 19 and semantic 20 expansion features and used machine learning to optimize weightings for combinations of them.…”

Personalized Semantic Query Expansion Based on Dynamic User Query Profile and Spreading Activation Model

“…N. A. Segura et al considered concepts and relations together in the expansion process, concepts with meaningful relations were given a high priority for expansion [12]. There are also other semantic supporters like Linked data [21] and so on. In addition, other strategies can be introduced into semantics query expansion, L. Li et al found out terms both semantically and co-occurrence related to query on massive web page set and search engine performance evaluation data [22].…”