Facilitating Navigation on Linked Data through Top-K Link Patterns 2015.4.13

Introduction Users navigating on Linked Data often follow semantic links by using Linked Data browsers. Just as traditional Web browsers allow users to navigate between HTML pages by following hypertext links, Linked Data browsers allow users to navigate between data resources by following RDF links.

User Browsing / Exploring

Many-to-many transitions

Challenges The high entropy and diversity of links between the entities. Two kinds of user navigation paradigm (single-focus-oriented and multi-focus-oriented). There are many potential and complex relationships between the current entity(entities) and its(their) related entities. In the process of navigation, users may retrieve interesting entities with a fuzzy or clear initial need(e.g. Cincinnati, the films directed by Steven Spielberg). Meanwhile, users may explore and extend their domain knowledge (e.g. which films are directed and also produced by Steven Spielberg?)

Approach In this study, we propose a novel approach that facilitates navigation on Linked Data. Given an entity (some entities) being browsed, we collect the link patterns between current entity (entities) and its (their) related entities, and then generate a link pattern lattice based on Formal Concept Analysis (FCA), to represent a road-map for navigation. Further, to help users quickly find target entities, we select top-K link patterns as starting road-signs for navigation.

Related Work

Evaluation Among these tools, we have selected gFacet and OpenLink Data Explorer to make a comparison. We focus on the evaluation of the approaches in terms of usability, and have measured the ability of users to carry out tasks, as well as their response times. Usability metrics Task success rate Task consumption time UI Component Satisfaction Task Level Satisfaction Surprised Knowledge

Task

Q&A Thanks