A structured Knowledge Graph is a key data store for any modern search engine. It stores all of the public facts about your brand and the relationships between them. Here is an example of a Knowledge Graph for a school:
The key to searching a structured Knowledge Graph is Named Entity Recognition (NER). Using NER, Answers identifies entities and fields in a query, the class of search, and the best set of results.
CLASSES OF SEARCH
There are three general class of searches that a user will make against a Knowledge Graph:
Field lookup is used when there is a single, direct answer to a query. Answers identifies the most relevant entity in the Knowledge Graph and the field for that entity that most directly answers the query. Questions often ask for discrete information:
The ideal response to a field lookup is a direct answer that shows the value of the field. Here is how this might look in Yext Answers:
An entity is surfaced when a user is asking a question about an event, person, product, or any other entity type in your Knowledge Graph. He/she is not asking for a specific field for that entity, but rather the entity itself. Generally, a user will look for an entity by name when they have one in mind.
The ideal response to a specific entity search is an entity card.
Most often, users are searching for a set of entities that match a specific set of filters. Using NER, Answers is able to parse out potential filters and apply them to return a filtered set of Knowledge Graph entities.
One of the most common filters is a location. It could be explicit (e.g., zip code) or implicit (e.g., “near me”).
The ideal response to a filtered entity list is a list of entities with a clear UX that shows that the relevant filters have been applied.
Named Entity Recognition (NER) is the process of identifying tokens—also known as search terms—in each query and classifying them into entity types. By aligning the tokens with how information is stored in the Knowledge Graph, Answers can quickly and accurately surface the best results.
Yext Answers automatically applies NER to every query to tag tokens and token spans. Answers automatically filters search results based on these tags. For example, Answers will automatically turn the query "Financial Advisor in New York who speaks Mandarin" into a structured graph query with the following filters: