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Google Entity Search

SEO Tactics for Google Entity Search using Google Cloud Natural Language API for entity analysis

Application knowledge of how entities play a role in search engine results pages (SERPs) is a critical SEO skill. Learn how to use tools like the Google Cloud Natural Language API for Entity Analysis

Understanding and optimizing for entities is no longer an option but a necessity for businesses striving for online success. SEO agencies that can provide expert entity SEO services are behind many brands winning more clicks on search engine result pages (SERPs).

Our search revolution presses a need to think beyond the longstanding practice of optimizing for keywords. It inspires refreshed marketing strategies to explore how the SERPs evolve. Your publications can overcome content ambiguity and gain better visibility with an entity-based approach.

Before you can conduct an entity analysis using Google Cloud’s Natural Language API, it helps to understand a few basic terms.

Table of Contents

What is an entity?

We can define an entity as “a thing or concept that is singular, distinctive, clearly stated or described, and unique.” For example, an entity may be a person, place, brand, sports team, item, organization, idea, abstract concept, concrete element, other suitable thing, or any combination thereof.

Google’s Knowledge Graph is a knowledge base or library collection of entities and the relationships between them.

Entity search involves using semantic and entity understanding to connect and retrieve information based on specific knowledge of a “thing” versus solely keyword matching.

What are entity-driven SERPs?

SEO entities are defined data points that represent events, places, individual people, places, organizations, or concepts – basically, “things.” They are not just search queries or keywords but encompass the concept that gives meaning to the entity. Think of it as entities assisting natural language processing systems in understanding word relationships and in overcoming linguistic hurdles.

If I conduct a search using “entity search” only, today that largely triggers Business Entity Search results with states. If “SEO entity search” or “Entity-based SEO” is used, then my Google Search results have a better context for this topic. While people may still understand the term based on their earlier understanding, entity search optimization will help Google match the query better. It is important to understand what triggers influence how Google Search works.

As Google Gemini fine-tunes and Bard are rolled into Google Assistant, entity-driven SERPs are vastly expanding. It will offer more contextually helpful search experiences right on people’s phones. Queries conducted on mobile phones may produce a conversational overlay in “a completely new way to interact” while searching using your phone.[1]

Should we work to clarify the meanings of ambiguous terms?

While it is nearly impossible to educate a wide audience on the application of a nuanced term, embracing search semantics and entity search will improve the understanding of your content.

Writing content for people means they can best understand you if you use their common language. Trying to coin a new acronym or word often is a temptation to define it using a lower bar. You can build a tuning data pipeline and track the experiment results; LLM prompt engineering can include your parameters, inputs, outputs, and experimental results. This can reduce manual steps in the entity development process, resulting in a more efficient workflow.

An entity is a bite of information in Google’s Knowledge Graph database. Entities are records within their database that represent something.

Google SEO entities are foundational to building semantic relationships and your knowledge graph. It has become ever so clear that a host of legacy SEO metrics and tactics have little to do with winning SERP entities and your knowledge graph database. We see major shifts in search, but thankfully, we have the Natural Language API for analyzing and parsing text through syntactic entity analysis.

Unique Identifier Property

Google’s entity knowledge base relies on identifying each separate entity. This requires each entity to have its own unique identity. A unique identifier in Google’s Knowledge Graph Search API explorer.

The way Google sees it, they are meaningful units used to organize information on the web.

Think of Google’s Knowledge Graph as a vast repository of information that stores entities together with their descriptions and attributes. It catalogs the entities so that it makes relational connections between entities and concepts. This helps us work intelligently with Google entities.

Google is known to build knowledge graph entities from its own Knowledge Graph, social shares (LinkedIn), scholarily white pages, Wikidata, Crunchbase, Amazon’s product graph and any other trusted sources on the world web. These entities form the nodes of its graphs. Google’s Knowledge Graph Search API lets you find entities in the Google Knowledge Graph. The API uses standard schema.org types and is compliant with the JSON-LD specification.

What is an entity analysis?

Entity analysis inspects the given text for known entities (proper nouns such as public figures, landmarks, etc.), and returns information about those entities. Entity analysis is performed with the analyzeEntities method.

Entity and Sentiment Analysis conducted using Google’s Natural Language API let you gain Google’s perspective on that particular entity. They are essential SEO tactics for people trying to improve their organic search performance.

SEOs can gain insightful data using the Google Cloud Platform Natural Language API – at a minimum cost of five dollars. It provides natural language understanding technologies, such as sentiment analysis, entity recognition, entity sentiment analysis, as well as custom text annotations by using pre-trained machine learning models.

Topical Entity-Rich Content Gives a Competitive Edge

Progressive search marketers and SEO experts are unlocking enhanced SERP visibility, credibility, and providing business owners with a competitive edge in the digital landscape.

As someone active in Google Search tests, it’s clear that Google Search is rapidly changing.

Google’s Search Generative Experience is pulling in more:

  • AI-Generated answers.
  • Favoring long-tail content that aligns with article segments having higher relevance to serve niche queries better.
  •  Low-volume search queries matter more when they provide helpful content your active audience is searching for.

What this means is that topical entity-rich articles provide a competitive advantage over other traditional content types. Google largely deprecated FAQ and How To listings within the search results Search Console reporting. This may signal how it is now using AI’s ability to understand the context and is less reliant on former tactics.

True – user queries are evolving. However, FAQ content continues to have a strong role in populating SGE content. Both the People Also Ask (PAA) feature and “Ask a follow-up question” emerge as favored SERP rich results, surpassing even the once-dominant featured snippet

  1. Get familiar with AI entity recognition.
  2. Understand the use of abstraction for entity or entities.
  3. Have one clear, main entity per page.
  4. Include entity in headings and description.
  5. Include ontolgy engineering.
  6. Implement strategic structured data markup.
  7. Use internal linking to improve contextual analysis.
  8. Use entity analyser tools.
  9. Evaluate entity salience scores.
  10. Establish authority on your entity selection.

For years, many SEOs focussed on building links. I think focusing on a useful and strong web presence your audience follows is more important. Links have traditionally been the main signal that correlated with site quality. Today, they are one of many signals that can be used.

1. Get familiar with AI entity recognition

Start a SERP analysis by Googling your entity. Etity recognition is an AI-enabled technique used to analyze and identify real-world objects, people, places, or basically, entities. For SEOs to understand how entities shape the SERPs, it helps to know how Google defines the characteristics of an entity reference.

Entity search SEO is a form of on-page optimization that can help improve your website’s ranking in SERPs. It is a far cry form Google simply looking at the keywords that are on your website. It is about the bigger picture that those keywords form and a clear intent of the words on your web page.

“A node representing organizational data may be included in a knowledge graph. These may be referred to herein as entity type nodes. As used herein, an entity type node may refer to a node in a knowledge graph, while an entity type may refer to the concept represented by an entity type node. An entity type may be a defining characteristic of an entity reference.” – Ranking search results based on entity metrics

2. Understand the use of abstraction for entity or entities

When referring to the semantic web, an entity is whatever the “thing” is that is described in a document. If you were a computer, it’s how you would understand everything about a person, organization, topic, or a place mentioned in a document. Abstraction is the process of taking away or removing characteristics from something to reduce it to some set of essential characteristics. Google’s journey to semantic understanding involves both subtraction and extracting semantic information about objects or entities.

For me, starting to grasp what “entity search” is began years ago. It has involved building my contextal understanding by collecting and reviewing connected patents, non-patent citations, and many scholarly articles as well as practical use.

Since the words “entity” and “entities” can have many uses, one can get very off topic without context.

While the Google patent “Identifying subjective attributes by analysis of curation signals“, contains the words “entity” and “entities” a lot, it is about a recommender search system that leverages user generated content. [3] Only by understanding the context and an overarching related concepts, articles, or patents can one gain a useable understanding.

You can also discover SEO entities to round out your article by conducting a competitive analysis. Consider a successful site that highly relates and abstract their query (keyword) clusters. Look past keyword phrases to identify which general topics they refer to.

3. Have one clear, main entity per page

Search is becoming more personalized and granular. This means it is best to select one unique main entity to each page. With that in your H1 title tag and description tag, you can ensure each page can be uniquely defined by one entity. You can thread subtopics within that page. When more in-depth is deemed useful, write web content supporting those main pages.

You may get displayed in “People also search for” this way. By providing in-depth information your answers may surface when Goolge provides searchers with more and more pre-written questions until they discover exactly what they’re looking for. While we never know which patent’s Google uses or how, the one below is interesting to me.

“Previously generated data is retrieved associated with at least one search result of the one or more of search results, the data comprising one or more entity references in the at least one search result corresponding to the type of entity. The one or more entity references are ranked, and an entity result is selected from the one or more entity references based at least in part on the ranking. An answer to the query is provided based at least in part on the entity result.” – Question answering using entity references in unstructured data, Patent US20160371385A1 granted in 2019

4. Include entity in headings and description

Include the main entity of each page into its heading and provide a concise introduction summary explaining what that entity means. When content writers are deemed to roam for that article intent, Google commonly selects a different heading and description that it finds meets the searcher’s intent better.

“Data-snippet on all content forces the meta description to be used.” – @Patrick Stox and @Propellernet [2]

Adjust page names and H1 titles and so they align with user search preferences. To illustrate, consider the “rose” entity, which encompasses everything related to roses. Subtopics with unique headings may include grafting, garden cultivation, scent/colors, types (bush, ground cover, hybrid, etc.), growth tips, uses in arrangements, drying, history, and more. In contrast, using the simple keyword “rose” could even be for rose water or rose wine. A keyword strategy alone that lacks semantics and context can improve with entity search.

Google Search can infer context for multiple entities with the same name. To use the above example, if I ask a “where” rose questions, I may get zone information. If I start out with “How roses…” SERPs display “Key points about growing roses”. Querying “what goes good with roses” the query produces suggested garden campanions; “medicinal roses” triggers rosehip oils and aromatherapy, while “aged roses” queries yield SERPs about gastronomic wines.

H1 – Main entity focus

Your H1 header establishes what the publication is about. Keeping it concise means it is easier for readers on mobile devices. It shouls clear indicate your overall theme.

H2 – Related subtopics and entities

Search bots scan H2 headers to determine subtopics or supporting entities that expound on your main theme. Each H2 should underscore and relate to the primary entity in a highly structure page format.

H3 – Suplemental context and information

H3 headers are useful to head up additional specific aspects or key points that align with your subtopics. This adds conprehensive depth to your article and signals entity relevance to search engines.

5. Include ontology engineering

Ontologies provide schemas to structure the data on the web. SEO ontology engineering involves adding semantic metadata to web pages and data; SEO professionals can help to make a page’s content more discoverable and easier to understand by search engines. This also includes other information retrieval systems used in entity search. I’m fascinated with the study of similarities and distinctions between ontologies and LLMs, and how they might be used together.

By adding meaning and context to your content, ontologies make it easier for users to find what they are searching for. This duo tactic can help establish more advanced and personalized user experiences. We utilize methods to create a mapping between the web page content and Schema.org tags.

6. Implement strategic structured data markup

Search engines can disambiguate your content easily through Structured Data markup. By ‘marking up’ your entities, chances for intent misunderstandings are reduced. Schema markup is “HTML code that describes code” that search bots quickly assimilate. Testings show that longer snippets in Google SERPs may signal featured snippet eligibility. Your content entities may be in the consideration process for featured snippets, while different from the one chosen to display at the top.

Machine readable markup helps search engines in the following ways:

  1. It is one way they can collect and catalog data on authors, publishers, and creators.
  2. Schema makes it easier for machine learning and AI to extract entities from unstructured data. Natural Language Processing (NLP), a branch of AI, assists in information extraction from text.

By tracking site optimizations with schema, increases in related web traffic indicate that it helps Google better understand connections between topic entities. In many applications, ths semantic vocabulary helps search engines characterize and categorize the content of web pages, which inturn, can improve organic performance.

“Search engines are integrating semantics to address complex search queries to improve the results. This requires identification of well-known concepts or entities and their relationship from web page contents. Trained models are used to predict entities from new data that can then be mapped to predetermined schema.org tags. – Autonomous schema markups based on intelligent computing for search engine optimization by the National Institute of Health [4]

“With regard to using structured data in general for ranking, I think that’s kind of tricky. So, on the one hand we do use structured data to better understand the entities on a page and to find out where that page is more relevant. It (SD) communicates information efficiently and that is good.” – John Mueller on how Google uses schema markup to understand an entity [5]

For example, a healthcare related website thats content creation practices use authoritative medical entities like reknowned medical institutions and peer-reviewed research can improve its credibility and trustworthiness. Healthcare websites that leverage AI can strengthed their SERP position.

7. Use internal linking to improve contextual analysis

Build site-wide entity connections to help people and search engines to understand how things are connected. As well as internal links, breadcrumbs and URL hierarchy can all help.

Search bots understand internally linked anchor texts which supplement your main ‘keyword’ with synonyms and related terminology. Oue internal link strategy begins with our most important pages. It flows naturally using varying lengths and related ways to say the same thing.

Your website’s entity data can be expresses through its internal linking structure. A entity-based strategy for internally linking helps build a topic map within your site. This adds context to what your write about by relying on relevant, contextually informed anchor texts. The quality and relevance of your page’s interlinking entities impacts ranking.

Optimize for entities beyond a single page. A successful SEO strategy can leverage AI to become comprehensive and see relationships between pages. Inlcude a plan for all entities across your website and how to best interconnect them through internal linking.

8. Develop skills using entity analyser tools:

Well-structured content fulfills contextual understanding by including relevant entities and concepts that belong to your topic’s core meaning. These tools can provide insights into which entities Google will likely expect in a document that seeks to rank for a specific topic.

  • InLinks Entity Analyzer tool: to see how many of your page’s entities have been indexed by Google.
  • Google Knowledge Graph API Search: to assess entities within the knowledge graph and information related to them. More than ever, Google Cloud tools help SEOs align with search trends.
  • Use Python: to assess entities within the knowledge graph and information related to them. You can perform topic mapping, n-gram analysis, query counting, and clustering tasks. For more information, see the Enterprise Knowledge Graph Python API reference documentation. [6]
  • WordLift: This platform derives semantic information from the user’s content by leveraging freely available datasets such as DBpedia and the user’s local vocabulary. It then builds new concepts in your local vocabulary. Its system uses a sophisticated “name-entity disambiguation” (NED) mechanism to correctly detected locations, company and people to unique “instances” in the web of data.
  • Similarweb’s Web Category Analysis Search Leaders report: It will provide query entities for your cluster. It also is helpful to build entity hierarchies into your site architecture.
  • Google Search Console Entity lookup: It can show you the entity code. The Entity lookup tool is not intended to provide the status of an entity in the Google Knowledge Graph. Rather, it displays the status of the entity in the most recent feed ingestion. Go to Google’s web based API explorer. In the Query field, add your search term. Then click execute. [7]
  • Rasa DIET for Entity Recognition and Intent Classification: is an open-source machine learning framework that intends to help developers build conversational AI systems. It is a Dual Intent and Entity Transformer model is a sophisticated multilayer that is capable of handling both intent classification and entity recognition tasks. [8]

Explicit semantic representations of queries and documents are found in the entity space to augment term-based representations. Make it easier for search engines to catalog associations between search queries and your related pieces of content. This way, you increase your chances of being served up for the most relevant results to the searcher.

9. Evaluate entity salience scores

Google Search goes beyond listing entities. It also assign an entity salience number to decipher which entities in a document merit higher significant, relevancy, or that best support the document’s meaning. This is one way that SEOs may improve rankings for Knowledge Graph inclusion and user experience enhancements.

SEO strategies to highlight core document entites may include:

  • Term Frequency-Inverse Document Frequency (TF-IDF): Entity salience is determined by identifing relative frequency versus only understanding the pure frequency of an entity. Just Google dislikes keywork stuffing, “entity stuffing” is not a helful tactic. Balanced, prominently featured key entities can help boost rankings.
  • Semantic salience: This leverages Large Language Models (LLMs) to determine how intensively a text is connected with a topic and its relationship. The connection is assessed by semantic relevance. Content that covers multiple interconnected entities within a specific web page may be considered as useful information for the knowledge graph.
  • Positional salience: The position of where main entities appear in a can signal a higher weight or importance. document. Placing prominent entities upfront in a text document signals the importance of an entity to the author.

Google’s engineers continue to develop entity salience calculations.

This is where Entity Relationship Diagram Model comes in play. I use entity analysis to locate and label fields within a given document. You can access communication in emails, chat, social media, and more, as well. Follow-up sentiment analysis reveals actionable product and user expereince insights into your customer opinions.

Three main elements of entity relationship modeling are:

  1. Entities.
  2. Attributes.
  3. Relationships.

Entity salience offers insights into how Google’s AI appraises content in order to create an objective score for web pages. We engage with an AI-enabled technique when developing entity salience for product page improvements. Choose your favorite framework for leveraging AI-enabled applications.

10. Establish authority on your entity selection

Effective SEO content marketing includes a conprehensive network of content pieces that expand deeper on your entity and all related entities. Proper ontology to that content establishes your site architecture for a great user experience and an AI readable hierarchy.

Broadly, there are four steps to establishing entity authority:

  1. Conduct Market Research to idendify how Google views your entity.
  2. Build site-wide internal entity linked connections.
  3. Begin with your priority pages and complete on-page entity SEO.
  4. Consistently publish high-quality content that is entity supported.

How to use the Google Cloud Natural Language API for Entity Analysis

Google’s natural language processing algorithms are designed to comprehend human language.

  1. In the Google Cloud console, select Navigation menu > APIs & Services > Library.
  2. Scroll to the “Cloud Natural Language API” and click to enable it.
  3. Use an existing account or create a service account and download the private key file.
  4. Open your Cloud Shell and run the command gcloud services enable language.googleapis.com.
  5. Next use curl to send a request to the Natural Language API. Enter command “export API_KEY=____”. Executing this in the terminal ensures that the API_KEY has added to the environment variables so that it is not required to be called for each request.
  6. Now either pass the text directly within a content field or passs a Google Cloud Storage URI within a gcsContentUri field. The Natural Language API can also be used by sending files stored in Cloud Storage for text processing. If you take this route, replace content with your gcsContentUri and give it a value of our text file’s uri in Cloud Storage.
  7. Send your request to the API’s analyzeEntities endpoint.

MOTE: You can also create your NL API key by navigating to the Credentials section of APIs & services in your Cloud console. Next, select the “Create credentials” dropdown item and choose API Key.

Google provides the following table for Entity Recognition tasks:

Tab Tasks
Simple Entities tab

  • Enabling and disabling entity recognition
  • Adding new simple entities or updating existing entities
  • Deleting entities
  • Downloading dictionaries
  • Editing dictionaries
Composite Entities tab

  • Adding new composite entities
  • Updating composite entities
  • Deleting composite entities
  • Downloading the composite entities definition file
  • Uploading a composite entities definition file
Blacklist tab

  • Downloading the entity blacklist file
  • Deleting the entity blacklist file
  • Uploading the entity blacklist file
Entity Diagnostics tab

  • Testing Entity Recognition
Adjustments tab
  • Adjusting Parameters

Entity Analysis Example Using the Google Cloud Natural Language API

Here is the code I entered to see related entities in Winston Churchill’s Knowledge Grpah


{"entities": [{
      "name": "Winston Churchill",
      "type": "PERSON",
      "metadata": {
        "mid": "/m/082xp",
        "wikipedia_url": "https://en.wikipedia.org/wiki/Winston_Churchill"
      },
      "salience": 0.7980405,
      "mentions": [
        {"text": {
            "content": "Winston Churchill",
            "beginOffset": 0
          },
          "type": "PROPER"
        },{
          "text": {
            "content": "Churchill",
            "beginOffset": 53
          },
          "type": "PROPER"
        },{
          "text": {
            "content": "novelist",
            "beginOffset": 96
          },
          "type": "COMMON"
        }, {
          "text": {
            "content": "Winston Churchill",
            "beginOffset": 65
          },
          "type": "PROPER"
        }
]}]}

We can view the code that populates his Google Knowlege Graph to learn more about its key enties.

Winston Churchill Knowlege Graph Entities
born died
spouse childern
education nationality
dates knighted organizations founded
artworks books
cousins title

If I query “Read Winston Churchill”, Google Search respond to the verb and intent change by providing SERP listings of book. By slightly changing my query to “Winston Churchill quotes”, Google starts to return 101 Winston Churchill Quotes to Live By and images with quote.

Knowledge graphs use AI to respond to verbs change intent due to abilities to understand the relationships between entities.

For me, entities were at first a vauge unreachable concept. However, once I thought of verbs used in conjunction with a primary entity rather than keywords that might be involved, my mind wa connecting entities. What was initally vague took a very specific structurte.

With this knowledge you can ensure that you or your writers’ “focus entities” are supported by also talking about closely related entities. Well-stuctured and planned copy will likely already have this in place. It underscores Google’s intent to reward helpful content.

Answering Common Questions about “Entities”

The purpose of entity semantic search is to help disambiguate concepts and entities for better query matching. It involves retrieving information by scanning processes of all structured and unstructured content. When it entails leveraging AI to provide the most relevant results, it is called entity/semantic search. Entity search SEO is one type of on-page optimization intended to improve a website’s ranking in SERPs.

Entities often encompass broader topics from which keywords may come from. With “thing” being so general, a distinction between entities or keywords is necessary for an interconnected knowledge graph. Google’s Knowledge Graph relies on a deep understanding of entities and their node relationships or connections. This is also called semantic triples or tuples. Entities on the knowledge graph are identified by distinct machine-readable entity IDs (MREID).

How does the author schema entity work?

Author schema assists in the AI identification of the author of a piece of content. Additionally, this code helps Google differentiate between authors that have the same (or similar) names. An author bio link can take user to either a person or an organization profile page. Specifying the author tells readers who wrote a page. Google is placing more importance on the content source, specifically the author.

At Pubcon in Austin, Texas March 2023, Gary Illyes reconfirmed that Google is still not using authors as a ranking factor. When ranking search results, the user experience matters. Authorship indicates authority and trust to the people who consume content. Users tend to gauge the validity of a piece of content by an established author entity.

What is an entity-optimized pillar page?

Search engine’s newer algorithms favor topic-based content. A pillar page is a central piece of content that serves as the cornerstone for a topic cluster. It performs better with supportive articles that address specific, long-tailed questions with authoritative answers. Query entity search pulls together your publications that directly relate or are subtopics. This approach is vital to seeing future organic growth that triggers business revenue growth.

Agile marketing always involves a look ahead to understand and adapt to changing SERPs. By creating highly structured pillar pages, you can benefit from the power of entity modeling.

This is easier to accomplish with a holistic approach to your SEO strategy. Create fantastic, entity-optimized category or parent pages that link to authoritative blog posts on your website.

How do entities influence E-E-A-T?

Google is demonstrating that Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) are important criteria in the sea of AI-produced content. When Google’s raters are to evaluate which content is quality and should rank at the top of the SERPs, content entities that demonstrate expertise are spotted.

Sites with clear and strong entity attributes in both on and offsite signals make  Google’s understanding of your site easier. This clarity and trusted E-E-A-T signals encourage the likelihood of your content being ranked over your competitors. Optimizing for entities has become a means to convey your content’s context, experience and your trustworthiness to be the expert that is sourced.

One cannot simply write more content and expect yesterday’s results in this new area. Content must include clear and fresh concepts in conjunction with semantically related and added-value articles.

Google catalogs, in an organized format, its entire data through its knowledge graph. Knowing how content is AI assessed and used for knowledge graph population helps for incorporating entities into your updated content marketing strategies.

Integrated search is a search method that pulls results from multiple sources. In contrast, entity search is a method that helps search engines with specificity and context tasks. Entities help search engines to connect global information together, regardless of the language.

In order to be found and known for your topics, provide a wealth of helpful content on the topic that is most important to your active audience. Remember, schema markup can help you explain to search engines what your topics expertise is. It can disambiguate entities on your pages.

We get a glimpse of how Google’s journey in its patent granted in 2013.

“…precise search results can be shown more rapidly, satisfaction with search results for a query term in which multiple entities coexist can be improved, the ambiguity of query terms is overcome…”

(For example, when a query term is ‘Ontology Semantic Web’, two topic entities, that is, ‘Ontology’ and ‘Semantic Web’, are included), the entities are precisely separated and then processed, so that multi-entity-centric integrated search results, superior to simple integrated search results, are presented.” – Multi-Entity-Centric Integrated Search System and Method [9]

How are entities and keywords different?

Entities refer to bits of information in Google’s Knowledge Graph that define something in a distinguishable way. In contrast, keywords are word strings that represent how people use text to search on Google.

Google relies less on keywords and uses entities as a unit for categorizing information. It organizies entities into relationships, that are useful to display a rich search experience. This means when users use Google Search, they frequentluy see SERP features that include related entities and entity attributes.

This gives Google the ability to bring short answers to users’ questions. You can often see these answers featured in SERP features.

Google elects at times to display a new mobile format within “Perspectives” where the carousel extends upon a Featured Snippet at the top of the page. Clicking an entity search result takes users to the Perspectives filter page which may have more information on what they searched for.

What is an example of how businesses use Google Natural Language API for entity research?

Google tells us that its API can “Identify entities within documents—including receipts, invoices, and contracts—and label them by types such as date, person, and media. Understand the overall opinion, feeling, or attitude sentiment expressed in a block of text.” Insights drawn from entity data extractuib can help you focus on entity-based content optimization. Many sites are experiencing a drop in organic search traffic amidst the evolving capabilities of search engines. There are many use cases for the Google Natural Language API to help you stay competitive in your niche.

Learn which content pieces to enrich so they align better to what your potential clients or buyers are looking for by using multiple NLP techniques. We can help you identify how to navigate user search changes and ensure that your content remains visible and relevant to them. As well, its entity analysis response fields return a set of detected entities, the parameters associated with those entities (example: entity type), relevance of the entity to the overall text, and locations in the text that refer to that specific entity.

Google’s API demo gives you a clear indication of how Google actually understands a text document. This is not new, Google’s engineers were developing entity salience calculations as early as 2014.

As we learn that Bard will roll into Google Assistant, voice search which relies a lot on natural language processing, may increase how businesses benefit from entity recognition.

Again, while NLP processing is rapidly advancing, this is not new. Gianluca Demartini talked about Entity Ranking (ER) as an emerging search task in Information Retrieval back in October 2008.

“…combining simple Link Analysis, Natural Language Processing, and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@10 and 35% for MAP.” – A Model for Ranking Entities and Its Application to Wikipedia

This powerful combination continues to help search engines better understand your content and rank it higher in search results.

SUMMARY: Use Entity Search Analysis to Create Entity-informed Content

Entities are foundational to Google’s decision-making. You can profit from this form of advanced SEO and digital marketing. It’s exciting to embard on holistic SEO strategies for your content to surface in entity search.

Contact Hill Web Marketing at 651-206-2410 to Research and Build Your Brand Entity

Resources

[1] https://blog.google/products/assistant/google-assistant-bard-generative-ai/

[2] https://t.co/DemefpRxlS https://x.com/patrickstox/status/1292957533829828608

[3] https://patents.google.com/patent/US9811780B1/en

[4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748814/

[5]https://www.youtube.com/watch?v=5mUaRBadTxw

[6] https://cloud.google.com/python/docs/reference/enterpriseknowledgegraph/latest

[7] https://cloud.google.com/enterprise-knowledge-graph/docs/samples/enterpriseknowledgegraph-lookup and https://support.google.com/webmasters/answer/10432298

[8] https://mdh.diva-portal.org/smash/get/diva2:1803935/FULLTEXT01.pdf

[9] https://patents.google.com/patent/US20090254527A1/en