- Semantic Core Dimensions
- Marker Queries
- Query Parsing
- Cleaning Of Queries
- Clustering Of Queries
A semantic core (SC) is a set of keywords and phrases that are used to promote search engines. It is possible to create the best structure for site marketing with the help of the semantic core. Keyword queries can vary in frequency, competition, and commercial component. Building semantics involves collecting and grouping keywords, as well as optimizing pages depending on the collected keywords. A well-chosen semantic core is the key to a website’s effective ranking.
Keywords are terms that customers type into search engines to discover specific information.
The following is a classification of keywords based on their frequency:
- low-frequency (LF): up to 1,000 hits;
- middle-frequency (MF): 1-3 thousand hits;
- high-frequency (HF): 3-20 thousand hits.
HF queries are used for the main page and the main sections, MF queries are used for internal pages, LF queries are used for product pages with specific properties.
According to their needs, search queries are divided into the following types:
- informational – used to find information;
- transactional – used to perform an action;
- miscellaneous – keys are difficult to recognize;
- navigational – looking for information on certain websites.
You should think about your audience’s demands when creating a SC. Informational queries should be used for constructing information sections and blogs, whereas transactional queries should be used when creating an online store.
Keyword searches may be geo-dependent and geo-independent. Geo- dependent queries are region-specific and commercial in character (“buy a car in London”). Geo-independent queries are informational in nature and have nothing to do with a specific place. When developing a business site, geo-dependent queries should be used to attract more visitors.
Semantic Core Dimensions
A minimum of 100 queries should be included in the site’s semantic core. The maximum number of keywords is limitless, because the semantic core must be continually enlarged. The size of the core is determined by the site’s subject matter. The number of keywords for sites with a specific subject matter (e.g., refrigerator spare parts selection) should be between 100 and 500. The number of keywords for popular subject matter sites (online stores, information portals, etc.) might range from 1 to 40 thousand.
Words or phrases that properly capture the essence of the page are known as marker queries.
Marker queries are put together in stages:
- 1. Make a list of h1 subheadings. To build headings, you can use special software called “spiders,” which simulate search engine robots.
- 2. Making changes to the headings. It is required to use special services to monitor the frequency of headings. Don’t employ multiple intents (user needs) to optimize a page,. Instead of a single section titled “Spoons and Forks,” two sections titled “Spoons” and “Forks” should be developed. Also, avoid creating pages for similar sets of questions, since this may result in queries being excluded from the search. To look for queries, you can utilize a clustering service.
- 3. Heading expansion. When acquiring markers, gather the keywordss and prepare explanatory questions such as “price,” “order,” “buy,” and so on. Extraneous characters, such as “?” must be eliminated.
- 4. Compile a list of the headings of your competitors’ websites. It’s worth looking at competitor sites at the top of the search results based on key queries. The employment of “spider” programs for analysis is conceivable. This type of analysis is beneficial to the growth of your SC.
- 5. Queries should be normalized. This is to figure out what the most common query is. For example, the most common query for the term “sofa” is “buy a sofa.”
Tails are used to expand the set of tokens, allowing semantic queries to be extended into a search niche. Additional words are collected using parsers. They look through the content of web pages, picking and storing relevant information. When choosing keywords, search engine searches, theme sites, and statistics are considered. Parsing provides for the generation of a list of phrases with high frequency of occurrence, as well as the selection of the most relevant word combinations. Google Search Console data can be used in the search. Duplicate phrases and similar word forms must be sifted from the obtained keywords. You should also perform a comprehensive competitive analysis. Data from Google Analytics may be utilized for this.
You can expand your own semantic core based on the study of competitors.
Cleaning of Queries
Junk queries must be removed from the semantic core before it can be cleaned. Repetitive, irrelevant, off-topic, minus words (for example, “free”), with links to competitors and unsuitable locations, and mistakes are all examples of junk queries. Low-frequency queries can also be removed if they repeat the main ones. Junk can be cleaned manually or evaluated using analyzers. The final product should be a semantic core that has been thoroughly cleaned and contains only relevant keywords. After cleaning, you may start clustering queries.
Clustering of Queries
Clustering is the grouping of queries into clusters based on context and attribute. Its outcomes have an impact on the volume and type of content, as well as on mass distribution. Clustering allows you to figure out the SC and use it to build the structure of the site pages. When doing a large number of queries (over 1000), clustering is recommended. When clustering, the nature of query – informational or commercial – should be determined. Informational queries are junk terms for a commercial resource (“where”, “how”, “when” and others). You must also choose between two types of pages: main and internal. For example, internal sites were boosted for the query “buy shoes,” while the main page was promoted for the query “shoes online store.” It’s also necessary to check whether optimization queries on the same page are compatible.
The following are the main forms of clustering:
- Soft. A frequency keyword is chosen and compared to others based on the number of URLs that match. All keywords in the cluster are linked by a major common word or phrase, but they are not always connected. This strategy is employed for informational webpages with a basic structure.
- Hard. When URLs in all queries overlap, the phrases are aggregated into a cluster. The keyword phrases should be completely consistent with one another. This strategy is employed to promote competitive issues such as insurance, loans, and so on.
The Hard technique is more accurate, while the Soft method allows for more queries to be added to the cluster.
Clustering is a process that may be done both manually and automatically. For a tiny core of 500 queries, the manual way is ideal. It is sufficient to combine queries of the same intent. The usage of specific tools is required for automatic clustering. These services consider the level of competition as well as search results. The final clusters are carefully verified manually for discrepancies after automated clustering.
To optimize the entire site or individual pages, a semantic core must be built. It is important to collect a list of keywords and apply specific clustering services in order to assemble the SC. To minimize clustering and analysis problems, you should hire a skilled SEO master to assemble the SC. The cost of putting together the semantic core depends on the specifics and size of the site.