Semantic publishing has spectacularly changed the way we munch through information. The process of organizing and deciding what to be displayed on the web is automated by semantic publishing. It helps the news and media publishers to manage their content accurately and within no time. The idea extends to deliver a more personalized user experience and needless to mention, increasing the engaging statistics. Semantic publishing entails several techniques with a major share of semantic markup and recommendations. These techniques help the computer to understand the meaning, structure, and context of the chunk of information. This piece of information regarding semantic publishing puts it right next to tagging. But semantic publishing is nothing like tagging. This concept goes far beyond that, which makes it all the rage among the modern age online publications.
Why media companies advocate semantic publishing?
Online media and publishing organizations are leveraging the power of semantic publishing for a quite a time now. They are going through a transformation. Instantaneous circulation of information over the internet (which is almost free) is one of the major driving force for this transformation. The others include:
Creating more compelling content through semantic publishing
The added value provided by semantic publishing certainly drives engagement. the visitors can find the content more relevant to their searches and have a personalized experience. It boosts your content and the chances of its discovery quickly.
Leveraging semantic publishing for innovative services
Semantically enriched data allows you to pack the data into its API-driven feeds. The structured metadata for the document that takes care of the need and time sensitive issues of the specifically interested groups. These approaches enable the most contextually relevant ads that match your content, ensuring the top line growth of your product.
Semantic Publishing to amplify your editorial productivity
Semantic publishing eliminates most of the manual efforts through a greater degree of automation to tagging, linking and categorizing. The content and editorial team gains the advantage to be more flexible and dynamic. When the content needs to supplied in huge amount, day and night, semantic publishing is the completely legit.
The publishing organisations and media are in the intial stages of semantic publishing transformation. This may not seem chart- topping but the big name in the industry have already switched to semantic publishing and the results are impressive. This might seem like a far-fetched idea. To break the myth, here is how this revolutinizing semantic technology is actually an impressive reality. Take a look at what happens behind the scenes:
The science behind semantic publishing:
There are three basic major semantic technologies involved in the process:
- Text mining
- A semantic database
- A recommendation engine
The role of text mining is to analyze the content, generate metadata and extract new facts. Metadata is anything that characterizes the document. Pre-existing knowledge like the name of people, places, geographic locations along with metadata generated from text mining, is stored in the semantic database. The recommendation engine provides the personalized contextual results as per the search history, behavior and the interlinked data. Text mining also referred to as “semantic annotation” is always on the go. There is a pipeline of text where articles are analyzed, divided and the entities are identified and categorized. The related text or facts, that has been previously loaded in the database are also analyzed by the pipeline so as to resolve the identities that are same but differently referred to. The relation between the entities are identified during the semantic annotation process and the results are indexed.
At ParallelDots, Our API converts textual information to its corresponding document embeddings and the cosine similarity between the two embeddings is scaled to provide the result. Semantic Similarity API provides a score on a range of 0-5 (0-Not similar, 5-Almost same).
This API is currently being used in the publishing industry for solving several use-cases some of which are mentioned below:
- Generating semantically similar posts from archive which provide better click through rate than other related post plugins which rely on keyword matching algorithms,
- Creating personalised recommendations for readers,
- Standaradizing taxonomies of published articles across the whole archive enabling better search and navigation functionality.
How a Semantic-rich document look like?
A semantic rich data contains metadata and the relevant links to the similar documents at a very basic level. This enrichment can vary for different media publishers using semantic publishing. There can be a summary, abstract, quick facts, highlighting tools, suggestions and carefully placed ads, depending on the type of document you are reading. The pictures below will give you a reasonably fair idea of what is being talked about.
Clicking on one of the figures in the first image yields the complete, magnified and detailed image with a link to view it in the same article, as seen in the second image.
Semantic publishing can impel really effective results. News curators can organize and assemble their content better and faster. The reader can a get a personalized experience with highly relevant recommendations. They can also pinpoint what exactly they are looking for. This benefits advertisers as well. The content writers can produce more content faster and have the advantage of being informed beforehand. It now seems safe to say that semantic publishing has revolutionized the media and publications industry throughout.
Analyse how close different text contents are with our semantic analysis API