Knowledge graph search api

Knowledge graph example

You now have a screen with a graphical user interface (GUI) containing all the parameters contained in the Knowledge Graph API (details of which can be found here in its developer’s guide on the subject).

This means that it is important to be able to understand and recognize them. Fortunately, it is easy to determine what a free base MID looks like. They usually start with “/ m /” and then a very short string (for example, the MID for “Barack Obama” is / m / 02mjmr /).

As indicated in the specifications, a response message contains a list of entities, presented in JSON-LD format and compatible with schema.org schemas (with limited external extensions). Samples of the output are illustrated and interpreted in the Examples section below.

Another interesting field that is worth mentioning is the resultScore, which is defined as “[an] n indicator of how well the entity matched the request constraints.” (This is likely to be a type of entity metric, as I described in a previous article).

Google knowledge graph for business

Relationships between entities also play an important role. As mentioned in the previous example, the office title “President of the United States” is itself an entity associated with multi-person entities. Through this relationship, the knowledge panel of U.S. President Abraham Lincoln logically shows other U.S. presidents in the “Also frequently searched” area.

These connections also show where so-called graph databases, which store Knowledge Graph information, differ from so-called traditional relational databases. Let’s take as an example a relational database containing a table with all former governors of the state of California, another table with the filmography of all known Hollywood stars and a third table with all Austrian citizens.

Now, to query the attributes of the Arnold Schwarzenegger entity, information from three different tables must be combined. With a graph database, on the other hand, a query of the Arnold Schwarzenegger entity is sufficient to obtain all relevant properties and relationships.

Google knowledge graph tutorial

This is obviously not good news for those who need clicks to generate sales. After all, if there is no need to visit a site to find out when a company was founded, the user will have no need to click on any of the links on that company’s home page and therefore no direct sales results.

In terms of conquering Wikipedia, you definitely need to make sure you have a Wikidata entry. We’ve talked about how to create a Wikidata page and why it’s important for SEO in previous English blog articles, but more importantly, Wikidata stores all the structured Wikipedia data that is mostly used in Wikipedia articles.

As you work to improve your presence in the Knowledge Graph, you can keep an eye on the ranking of any domain, subdomain, subfolder or URL for the keywords you track to make sure you are providing the best possible content.

Google knowledge graph api example

This can give a lot of headaches when trying to understand Search Console graph data (graph data is always using the “per property” method) and table data (table data can be per property or per page, depending on whether we are filtering or requesting the “page” dimension).

But apart from this case, most of the time we are interested in knowing the data per URL, since it gives us more real information that is not summarized (or delimited) and using the data grouped by property can make us fall into a trap. For example, if we want to obtain the clicks that a certain KW brings to the site, we should not only count the clicks of the best positioned URL for that KW but the sum of ALL the KWs positioned for that KW, regardless of the position it occupies.

Perhaps with the impressions we do not see it so clearly, but instead of impressions let’s say that they are clicks, we will no longer see with the same eyes that the total number of clicks for a KW is 1, or 5… it is very important to be clear about this.