Google has come a long way since the days of simple Unlinked Brand Mentions keyword and link ranking. Today. The search engine relies heavily on entities —clearly identified people, places, brands, and concepts—to understand the meaning of web pages and user queries. Entity citations (brand mentions without hyperlinks) are generating a lot of interest: can they influence a site’s ranking on Google?
So, are these unrelated brand mentions simply lost words on the betting email list web or powerful signals for Google? To answer this. You need to understand the engine behind Google’s intelligence: the Knowledge Graph. How does it feed? How does it assess the reputation of an entity (like your company)? And most importantly, how much weight does it give to a non-clickable citation? I’ll try to uncover the theory, the data, and the potential impact on your ranking.
This post directly echoes one of my posts on Linkedin about my vision of SEO:
Google’s Knowledge Graph: Entities and Relationships
A bit of history: Google’s Knowledge Graph is a vast database that organizes information about real-world entities (people, organizations, places, objects, concepts) and the relationships the importance of understanding your customer’s path between them. Launched in 2012, it draws its information from reliable sources like Freebase (an open database acquired by Google), Wikipedia , the CIA World Factbook, and other structured databases. This data allows Google to have verified facts about entities (dates, descriptions, attributes) and to establish semantic connections between them.
For example, instead of simply matching keywords, Google might understand that a query like “capital of country with Eiffel Tower” is targeting the entity “Paris, France,” even if neither “Paris” nor “France” are explicitly mentioned in the query. This shift from processing text strings to understanding ” things , not strings” marked Google’s shift toward entity-based semantic search .
Why is this important?
Being present in the Knowledge Graph is a major asset for a website or brand. If your brand, product, organization, or person is already clearly defined in Google’s Knowledge Base, you have a head start over a competitor who isn’t listed there. This is because Google knows your entity and can more easily enrich the information about it, for example, by displaying a Knowledge Panel (information box) when people search for your name.
The Knowledge Graph itself stores the facts, while the Knowledge Panel is the visual showcase in search results. Google decides to display a panel for an entity when it believes it has sufficient confidence in its identity and reputation. To be recognized, it is not enough to belgium business directory manually add information: Google relies on verifiable data and reliable sources. If your entity is already identified, the strategy is then to enrich its file by providing Google with structured and validated information (for example via schema.org markup , Wikipedia/Wikidata pages, official profiles). Over time. Google aggregates this data from various sources to complete the portrait of the entity in its knowledge graph.
From keyword search to entity-oriented search
Google’s algorithm has gradually evolved from literal keyword matching to a contextual and conceptual understanding of queries. As early as 2013. The Hummingbird update emphasized semantic search , allowing Google to interpret the overall meaning of a sentence, not just individual words. Entities play a central role in this semantics: Google attempts to identify the “things” mentioned in content and queries, then relies on its Knowledge Graph to establish context. This means that web pages and user questions are increasingly processed based on the entities they contain or target. Rather than just word occurrences.
What are we talking about? How? With whom? Why?
In concrete terms. An entity-oriented query allows Google to draw on its knowledge of a concept instead of just a series of terms. For example, searching for “Thomas Edison invention light bulb” will return relevant results about Thomas Edison and the light bulb, because Google knows these entities and the connection between them (inventor → invention). Thanks to the Knowledge Graph. Google understands semantic relationships (Thomas Edison is the inventor of the light bulb) and can therefore provide more precise answers. This contextual understanding has been further enhanced with technologies like RankBrain and BERT . Which help capture the nuances of natural language and connect different formulations of the same search intent.
