Below, the algorithm capabilities of a Lexical Search Engine and Entity-based Search Engine are compared. Thus, the Lexical Search Search Engine feature has been renewed with the Semantic Search Engine or Structured Search Engine feature. Search Engines, such as Google, know the connection of a particular concept to another and have a Knowledge Base for it and create a Knowledge Graph for, a better understanding of search intent. Search Engines give importance to the meaning and conceptual links of words as much as “string matching”. During these periods, concepts such as the length of content or keyword density were still important. That is, they were looking at whether a word in the user’s query was in a corpus. Microsoft Bing, Google, Yahoo, Yandex, DuckDuckGo, and other Search Engines were just “Lexical”.
Words with the same meaning in different languages are thus combined at the same point, even if they are spoken in different ways. Sometimes different “strings” are included in the same “entity”. Search Engines treat words in two different ways “phrase-based” or “string” or “entity-based” meaning. Lexical Search Engines and Semantic Search Relation Last Thoughts on Semantic Search, Semantic Search Behavior, and Structured Search Engine along with Holistic SEO Which Google technologies play a role in how semantic search works? Why is Semantic Search Important for SEO? Dynamic Organization of SERP for Multi-faceted Search Behavior
The Need to Understand Lexical Hierarchy and Entity Relationships The need to reflect personal interests and trends Many searches are unintentionally ambiguous Why do Search Engines Pursue Semantic Search? Knowledge Graph, BERT, RankBrain, Neural Matching, and Semantic Search Semantic Search Engines and Their Query Understanding Capacity
Anatomy of Semantic Search and Meaning of Words Search Engines adapt to the Semantic Search structure by extracting these links, the meanings of words, and their semantic links with Natural Language Processing, and using them on the Web with Machine Learning Models. However, we can make this inference with the meaning bond that words have. “One morning, I have seen an eagle while running on a horse in my suit.” In this sentence, it is not clear grammatically exactly who the runner is or who is in the suit. In this context, even if a sentence is grammatically correct, if its meaning is weak, the sentence is not suitable for the Semantic Search structure. Semantic Search is linked to the meanings of words as well as to grammatical rules and the way words characterize each other. Thus, a SERP Design is created in which more organized, logical, and relevant information is interconnected. This semantic query network is fed by user behavior. Search Engines such as Google, Bing, and Yandex put frequently searched or asked questions and queries about a certain concept in a Semantic structure.
The content of a concept or entity combines with other meanings and concepts at different points to form a Semantic Hierarchy of Meaning. Semantic Search is the way users act on the Search Engine according to the semantic meaning relationships of words and concepts.