Semantic search seeks to enhance the meaning in content, to more closely align the searcher and the available information resources. This means there is a strong user-centered aspect needed to unlock the benefits. What scenarios, needs, experiences, and mental models do our user bring to their search task? How does that inform our modeling of the “meaning” derived from the content? How do we avoid encoding rigidity of meaning by creating learning opportunities for both the users and the underlying search index and algorithms?

As we model content, we recognize that its character, structure, and context all matter. Alongside strategies for incorporating taxonomies and indexing the content itself, we will explore how you can prepare a knowledge graph that increases the potential for aligning meaning between your content and your users.

On the user experience side, we will introduce design approaches such as supporting iteration for exploratory search, modeling a language landscape, applying user context identification, creating feedback loops based on results selection and use, and using visual signposting for lightweight semantics in the user interface.