This is the fourth part of a series of articles on semantic SEO and addresses the question of whether Google really understands the meaning of documents and search queries or whether it is just interpreting statistical analyses.
Table of contents [ Hide ]
1 Semantic understanding as a goal of Google
2 Google's path to semantic search
3 The Knowledge Graph as a Semantic Database
4 Google today – semantic search or just statistical information retrieval?
5 Machine Learning or Deep Learning for Scalability
6 Conclusion: Google is on the way to semantic understanding
Semantic understanding as a goal of Google
One of Google's most important goals has long been to achieve a semantic understanding of search terms and indexed documents in order to display more relevant search results. Semantic understanding is present when, for example, one can clearly understand a (search) question and the terms contained in it or clearly recognize their meaning. Clear interpretation is often made difficult by ambiguity of terms, previously unknown terminology, unclear wording, individual understanding, etc.
The words used, their order or the thematic, temporal or luxembourg phone number data geographical context can contribute to a better understanding. Through machine learning, such as that used by Rankbrain, Google is now able to quickly recognize terms and entities in search queries and documents and automatically create new classes of entity types using cluster analysis . The creation of new vector spaces for vector space analyses is also easier. More on this in further articles in this series.
This ensures a high level of detail as well as scalability and performance.
Statistics in combination with machine learning are increasingly leading to a semantic interpretation that comes very close to a semantic understanding of search queries and documents. Google wants to " recreate " a semantic search using statistical methods and machine learning .
In addition, a central element of today's Google search engine , the Knowledge Graph, is based on semantic structures.