NaLa-Search: A multimodal, interaction-based architecture for faceted search on linked open data

Abstract

Mobile devices are the technological basis of computational intelligent systems, yet traditional mobile application interfaces tend to rely only on the touch modality. That said, such interfaces could improve human–computer interaction by combining diverse interaction modalities, such as visual, auditory and touch. Also, a lot of information on the Web is published under the Linked Data principles to allow people and computers to share, use and/or reuse high-quality information; however, current tools for searching for, browsing and visualising this kind of data are not fully developed. The goal of this research is to propose a novel architecture called NaLa-Search to effectively explore the Linked Open Data cloud. We present a mobile application that combines voice commands and touch for browsing and searching for such semantic information through faceted search, which is a widely used interaction scheme for exploratory search that is faithful to its richness and practical for real-world use. NaLa-Search was evaluated by real users from the clinical pharmacology domain. In this evaluation, the users had to search and navigate among the DrugBank dataset through voice commands. The evaluation results show that faceted search combined with multiple interaction modalities (e.g. speech and touch) can enhance users’ interaction with semantic knowledge bases.

A Comprehensive Survey of Facet Ranking Approaches Used in Faceted Search Systems

“…Other FSSs which are based on a single resource type usually use only p-facets. The majority of the remaining FSSs mix the two facet types and handle them in the same way [30,32,36,44], many others are reported in the summary Table 1 at the end of this paper.…”

“…Table 1 shows the classification of the surveyed systems using the aspects discussed in this paper. From the classification summary we can observe that the majority of FSSs operate on knowledge graphs or structured data [1,7,8,11,16,[18][19][20]25,44,45,47,53,59], or the structured part of semi-structured data [3,5,26,35,43,48,50,51]. In this case the facets are derived from the existing attributes.…”

“…It is also noticeable that most FSSs developed to date operate on single domain [3,5,11,13,[20][21][22][23][24][25][26][27][28]30,36,39,41,44,46,51], although some experiment with multiple domains [1,40,43,47,48], and a few FSSs attempt open domains search, e.g., for the general web [14,18,50].…”

Comprehensive Review and Future Research Directions on Dynamic Faceted Search

“…Contrary to objective metrics, the subjective metrics assess and evaluate the simplicity and flexibility of FS [129][130][131][132][133][134][135]. Two main methods are usually used here, namely intrinsic and extrinsic evaluations: Intrinsic evaluations: Standard query facets are built by human annotators and used as the ground truth to compare with facets produced by separate schemes [136,137].…”