With the Latent Semantic Indexing (LSI) method, websites are indexed according to their subject area and not according to a specific keyword. Search engines try, for example, to find documents or websites that deal with the topic of house building, even if the word house building itself is not explicitly mentioned there. With the help of LSI, the search engines can distinguish between documents that really deal with house building and those that only mention the word house building.
According to AVIATIONOPEDIA, the aim of the LSO (the latent semantic optimization) is the ranking improve by the content next to the keyword, many containing the term semantically related words are included.
Importance of LSI for search engine optimization
In the early days of Google, indexing was purely keyword-based. In other words, the search engine checked the indexed documents to determine whether the search term entered by a user appears in them. A website on which the search term did not appear was therefore not displayed in the search results by Google, even if the topic of the page exactly matched the search query.
For this reason, individual landing pages were previously created for semantically very similar terms.
However, this has changed with the Hummingbird Update 2013. With the algorithm change in the course of this update, Google has brought semantic search / semantic indexing into focus, although Google itself has not yet confirmed that it is using LSI.
But the fact is: The relevance of a website to the topic has been given a much higher priority since the 2013 update. The semantic relation to the keyword on a landing page is more important than the mere mention of the keywords. Due to the latent semantic indexing, landing pages that do not contain the keyword at all can appear in the search results, but many terms that are semantically relevant for the keyword – the so-called LSI keywords.
However, this does not mean that the optimization to one main and several secondary keywords is superfluous. Google continues to check whether the search term / phrase is included, but at the same time checks the thematically matching documents for their semantic proximity to the keyword. Put simply: the better the semantic proximity of the content, the better the ranking of the page.
The response of the SEO experts to the latent semantic indexing is therefore the LSO.
What is LSO?
LSO means Latent Semantic Optimization or latent semantic optimization. With this type of optimization, care is taken to ensure that words or phrases are used in the texts that are related to the topic of the document or the website and are also used on other websites in connection with this topic. In order to find the most suitable semantically related words, helpful tools are used.
Other ways to find LSI keywords:
- Google Suggest
- Suggestions for similar searches at the bottom of a list of results in Google Search
The purpose of this optimization is to achieve a better positioning in the organic search results.
Connection between LSI and WDF * IDF
If you know the core functions of a search engine, it is also easier to create content that is optimized for the search engine. The basis here is Gerald Salton’s Vector Space Model, on which most search engines are based. In this model, the WDF * IDF method comes into play. A term is viewed from two perspectives:
- Frequency with which the term occurs in the document – WDF (Within Document Frequency)
- Occurrence of the term in all documents – IDF (Inverse Document Frequency)
Both are combined with each other in order to determine the actual meaning or relevance of the term. This weighting gives the term a certain place in the semantic space and all documents in turn receive a certain orientation in the vector space.
A search result would therefore arise as follows:
The search engine examines the indexed documents with regard to their vector alignment in relation to the search term. The most relevant documents are those that best match the search term from the LSI’s point of view.
It is therefore important for SEO to look at the semantic space of the documents that rank well for the search term, because from this it can be deduced how your own text can be optimized.