Table of contents
- The importance of smart entity retrieval models
- Semantic entity parsing
- Grasp the entity-oriented content model through entity referencing
- Take your time with your content
- Close the gap with entity-centric document building
The Importance Of Smart Entity Retrieval Models
Building semantically enriched entity retrieval models is crucial for smart content optimization. Referencing to meaningful structures, specific entities, types and relationships is what helps search engines form structure out of unstructured or semi-structured content, especially when it comes to text.
The process of semantic disambiguation needs to be embedded through all elements of the entity retrieval process. This is the process that experienced information developers use to come down to intent disambiguation through intelligent entity building. Intelligent entity building allows Google to present answers to questions your customers are asking about your products, offers and services on the Google search engine result pages.
Semantic Entity Parsing
Humans and robots usually start the disambiguation process by using various query assistance services, such as query facets, e-commerce facets or query auto-completion. The idea is to use these facets and auto-completions and combine them with machine learning capabilities to understand the true nature of the query and its type: informational, transactional, navigational or something in between.
In any case, we will not focus on deeper understanding of the mechanics of query enrichment. Instead, we will focus on understanding how semantic entity parsing and smart structured data can help us assist search engines like Google grasp our content and build proper information trees out of entity models that we build for them.
Grasp The Entity-Oriented Content Model Through Entity Referencing
When dealing with entity-oriented content, your strategy should be set in a way to exploit the rich structure associated with entities in a knowledge base. This procedure is known as entity linking in the information world where the subject and the predicate of the sentence are always represented as URIs.
One way of understanding the entity-oriented content model in a corpus of documents is to use the TF-IDF approach applied to entities. Imagine, those entities could then be weighted as if they were terms. For those who are unfamiliar with the concept, TF-IDF means term frequency inverse document frequency – a metric used in the process of information retrieval. The idea is to count words appearance in a document but also across a document corpus and determine their relevance by using the following formula:
According to the sklearn documentation, the idf is computed as “idf(t) = log [ n / df(t) ] + 1 (if smooth_idf=False), where n is the total number of documents in the document set and df(t) is the document frequency of t; the document frequency is the number of documents in the document set that contain the term t”.
In other words, we switch from the traditional term frequency model to entity frequency model which can help us understand the given document through the entities embedded in the text. Once we determine the URIs with entity resolution, the next step is to link every detected URI with its appropriate label name or in this case this will be the name of the entity in a given knowledge base. We at WordLift are proud to share that we’re partnering with DBpedia to build enriched entities to form the smart content model for SEOs like you.
Take Your Time With Your Content
Information extraction has its roots in natural language processing but it does overlap with information retrieval and databases. The area of large-scale knowledge extraction with a goal to build a knowledge base has become an interesting computer science topic for the research communities in the past 2 decades. In the process of advanced harvesting knowledge from text, we want to go beyond just populating the knowledge base with additional facts about the entities where we already defined their unique URI identifiers. The goal should be to discover new entities, form new relationships and use these connections to uncover new facts from there.
The standard process used in the past is also known as closed information extraction and will look something like in the snippet below:
<dbr:Barack Obama> <dbo:nationality> <dbr:United States>
<dbr:Barack Obama> <dbo:birthPlace> <dbr:Honolulu>
While the process of open information extraction will provide us with the following:
(Obama; is; US citizen) (Obama; born in; Honolulu, Hawaii). See the difference?😁
Close The Gap With Entity-Centric Document Building
Optimizing the content with keyphrases and keywords without considering the entity information contained in them is already outdated and belongs in the past. The modern approach is to focus on entity-centric document building where we determine the importance of the document depending on how entity rich is its information contained in the document itself. Each document gets assigned a relevance score on how well it covers the entity model needed to answer the questions that the searchers write in the search box. That is why our clients at WordLift actually use our services because we go beyond traditional keyword optimization and instead think of each document as an entity-based content model.
Here’s a visual representation of how the entity-centric architectural approach looks like:
To sum up, our experts at WordLift build entity models that assist proactive systems responsible for addressing users’ information needs. Entity optimization through intelligent schema markup enrichment is your tool in determining the right context and disambiguating user intents and interests.
I cannot emphasize this enough: users’ needs shift because nowadays they expect on-the-fly information extraction and answering. Intelligent agents will be expected to give immediate responses to users’ question queries instead of researching ranked results from SERPs.
What are you doing today to prepare for this deterministic future?
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