Simple information lookup tasks (e.g. “What the weather like in London?” or “What is the population of the UK?”), are currently well supported with traditional search engines, and more recently with intelligent personal assistants. Intensive knowledge tasks, (e.g. “How do countries with low GDP per capita rank in emigration?”), however, require combining and cross referencing data from multiple sources to get to an answer have typically not been well supported. Our ability to support these types of information tasks on the Web is currently compromised by the inherent document/application nature of the Web itself. End-user mashup tools traditionally approach this problem by assisting users in structuring unstructured content form web pages and then support information-oriented tasks over the structured content. Motivated by the fact that more and more structured data is available on Web pages we investigate another possible solution: how to extend traditional Web navigation, which the majority of end users find intuitive, to include more data-centric behaviour. With mashpoint we propose a simple architecture, which would support an interaction that allows web pages to be linked based on similarities of the entities in their data. Linked in this way, queries that traditionally require the tedious work of joining information form several pages can be performed with simple web-like navigation. The paper focuses on evaluating if the proposed interaction is one that users would be able to understand to execute intensive knowledge tasks. We ran two separate studies: first to explore if the interaction concepts introduced are easily learnable and to gather initial feedback on our prototype, and second to explore design options which can inform how to address discovery challenges when large amount of pages are linked in this way, therefore assessing the feasibility of this model to work on a Web-scale.