The Engine in Action: A Step-by-Step Teardown of a Google Query

We’ve talked about the theories and the patents from query parsing and context vectors to the origins of the Knowledge Graph.

Now, it’s time to see the engine in action.

The most illuminating section of Koray Gübür’s presentation, “Semantic Search Engine & Query Parsing” is a detailed, step-by-step deconstruction of how Google handles a seemingly simple query: “when was martin luther king jr born.”

This example, originally from a presentation by Google engineer Andrew Hogue on the “Structured Search Engine,” is a masterclass in applied semantics.

It reveals how every principle we’ve discussed comes together in a fraction of a second to deliver a single, accurate answer.

Let’s walk through the process.

Step 1: Named Entity Recognition (NER) - “What is the ‘Thing’?”#

The first step isn’t to find keywords, but to identify the primary entity in the query.

The system recognizes “Martin Luther King Jr.” as a proper noun, a specific person.

This is the Named Entity Recognition (NER) process.

What it does: It isolates the “thing” the user is asking about.
Why it matters: This immediately shifts the search from a string-matching exercise to an entity-oriented one. The engine isn’t looking for pages with those words; it’s querying its internal database (the Knowledge Graph) about a specific entity.

Step 2: Entity Resolution and Attribute Extraction - “What About the ‘Thing’?”#

Next, the system must understand what aspect of the entity is being requested.

It identifies “when” and “born” as words that point to a specific attribute.

What it does: It performs Entity Resolution, confirming this is the famous civil rights leader, and then extracts the desired attribute: a date of birth. It understands the query isn’t about his death, his speeches, or his family. It’s about his inception.
Why it matters: This narrows the search from the entire universe of facts about MLK Jr. to a single data point. This is crucial for providing a direct, concise answer.

Step 3: Synonym Extraction - “How Else Do People Ask This?”#

Now the system gets clever.

It knows that there are many ways to ask for a date of birth and many ways to refer to Martin Luther King Jr.

It performs Synonym Extraction for both the entity and the attribute.

For the entity: martin luther king, mlk, reverend king, dr. king.
For the attribute: date of birth, date born, appeared, dob.
What it does: It expands the original query into a cluster of synonymous queries. This dramatically increases the pool of potential documents and data sources it can use to find and corroborate the answer.
Why it matters: This builds confidence. If multiple trusted sources using different phrasing all point to the same answer, the system’s confidence score skyrockets.

Step 4: Question Format and Data Type Expectation - “What Should the Answer Look Like?”#

Based on the previous steps, the system solidifies the Question Format.

It recognizes the “when was X born” structure.

More importantly, it now knows the expected answer data type: a date.

What it does: It primes itself to look for a date-formatted string (e.g., January 15, 1929, or 1/15/1929). It will deprioritize or ignore text that doesn’t fit this format.
Why it matters: This is a powerful filter that eliminates noise. A paragraph that mentions “1929” in the context of the stock market crash will be seen as less relevant than a table row that explicitly labels “January 15, 1929” as “Birth Date.”

Step 5: Entity Reconciliation and Data Accuracy Audit - “Which Answer is the Right One?”#

This is the final and most critical phase.

The system has gathered multiple potential answers from various sources.

Now, it must perform an Entity Reconciliation and data accuracy audit.

What it does: It compares the facts extracted from different sources (nobelprize.org, wikipedia.org, history.com). It sees that birth date is “January 15, 1929,” birthplace is “Atlanta, GA,” and birth name is “Michael King, Jr.” It standardizes this conflicting and complementary information into a single, cohesive entity profile. It uses the authority and historical performance of the sources to weigh the answers. A fact corroborated by three trusted historical websites will beat a contradictory fact from an unknown blog.
Why it matters: This is how Google maintains the integrity of the Knowledge Graph and provides a single, trusted answer in a Knowledge Panel or Featured Snippet. It’s not just finding answers; it’s arbitrating truth based on authority and consensus. John Mueller’s quote on Slide 57 about entity reconciliation is a direct window into this process.

What does this mean for you?#

This step-by-step process is the entire semantic search engine in miniature.

It shows a system that:

Identifies the entity.
Determines the attribute sought.
Understands synonyms and context.
Expects a specific answer format.
Corroborates the answer across trusted sources.

Your SEO approach should mirror this process.

Create content that clearly identifies entities, provides structured answers to attribute-based questions, uses synonymous language naturally, formats data logically, and cites authoritative sources.

When you do this, you are no longer just “doing SEO.”

You are creating a resource that is perfectly designed to be understood, ingested, and trusted by the structured search engine Google has been building for over twenty years.

In my next article, I’ll look to the future, exploring how modern language models like BERT, MuM, and LaMDA are not replacing this system, but are instead supercharging it to achieve an even deeper level of understanding.