A scientific paper from Google gives some interesting insights into how Google today probably divides search queries into different thematic areas. Here is my summary of the paper in this thread.🧵 #seo #semanticsearch #google The document"Improving semantic topic clustering for search queries with word co-occurrence and bipartite graph co-clustering"presents two methods that Google uses to contextually classify search queries.So-called lift scores play a central role in word co-occurrence clustering.

Since 2016, I have researched over 100 Google patents and would like to take you through my insights from this work in relation to a semantic search engine like Google. In my new article I have gathered my insights on search query processing. I hope you will support it ❤🧵 In semantic information retrieval systems, entities play a central role in several tasks.

📌Understanding the search query (Search Query Processing)
📌Relevance determination at document level (scoring)
📌Evaluation at domain level or author level (E-A-T)
📌Compilation of SERPs

To build a semantic database like the Knowledge Graph, Google needs to access unstructured data sources in addition to structured data.

📌The problem with manually maintained databases and semi-structured websites such as Wikipedia is that the data is not complete or up-to-date. 📌The closed extraction of entities has the major disadvantage that long-tail entities can only be minded very slowly. Scaling requires an open extraction.

📌Machine learning or natural language processing is the key to open extraction.