ontosense

a semantic technology that makes it easy to add semantic meaning to search queries and content

Ontosense introduces loosely-coupled semantic referencing using common website URLs as semantic reference targets.  It is proposed as a replacement of tightly-coupled semantic referencing of controlled vocabularies/definitions repositories.

Figure 1. The ontosense query UI allows searching based on attributes (ex: "NCQ" above) that are not defined/inexistent in the target search database. These attributes can be either selected from URL suggestions or typed manually, and finally joined into the ontosense search structure by an internal or 3rd party ontosense semantic service provider.

:: Ontosense provides benefits to the content consumers by increasing the quality of semantic interpretation of content through collaboration between the content creators and search engines (private/internal and public search engines).


Here is a good podcast about Ontosense based on the info from this webpage and from the Ontosense Whitepaper PDF file:

Ontosense Podcast.wav

Here is a summary by Google Gemini on the Ontosense Whitepaper:

"OntoSense is a semantic mediator model designed to enable software systems to understand and process content created by people. It aims to bridge the gap between human-created content and software interpretation by establishing semantic relationships between software applications. 

The core of OntoSense is a semantic knowledge base (OntoSense KB) that stores information using web page URLs as semantic references, enhancing the accuracy and relevance of information processing. OntoSense also introduces a novel query interface, the OntoSense Semantic Query Guidance System, which allows users to formulate structured queries using URLs, improving the precision of search results.

The OntoSense model is designed to be adaptable and scalable, incorporating machine learning techniques to continuously update and refine its knowledge base. It also includes mechanisms for ranking the confidence and relevance of information, ensuring that the most reliable and pertinent data is prioritized.

In essence, OntoSense aims to revolutionize the way software systems interact with human-generated content by introducing a semantic layer that enables deeper understanding and more accurate processing. This has the potential to significantly improve information retrieval, filtering, and overall communication between software applications and their users."


:: All content creators are willing to make their content more accessible and correctly interpreted by search engines. Currently, search engines try to understand all the available content all by themselves. How can the content creators help increase the quality of the semantic interpretation of the content and queries? Until now, the only way for a content creator to annotate his content in order to suggest a semantic interpretation was by referencing vocabularies / taxonomies / ontologies. These semantic targets are hard to choose by any individual - finding and pointing to specific RDF repositories and resources is not as ubiqtuous as pointing a common website page URL. Furthermore, vocabularies and RDF repositories can never be recognized as "single point of reference" by search engines.

:: The solution proposed by ontosense is to allow content creators to easily choose as semantic reference target one or multiple common website URLs as semantic targets to indicate the semantic interpretation of a portion of content (word, paragraph, page, database column, database table, selected image area etc.). Thus ontosense proposes loosely-coupled semantic referencing using common website URLs as semantic reference targets, as opposed to tightly-coupled semantic referencing of controlled vocabularies.

:: Everyone benefits from using ontosense:

Quick overview of the ontosense query UI

The meaning of any content can be explained directly by the content creator by making one or more references to previously known concepts.

This is the basis for the ontosense annotation system, that is designed to overcome the limitations of referencing specific vocabularies defined by individuals, companies and other entities/organisations. Instead of limiting to referencing dictionaries and ontologies, ontosense semantic references target commonly known, trusted URLs (like Wikipedia URLs) to indicate the intended meaning of the content.

The Ontosense Query UI, presented in Figure 1, can be used for two purposes:

Hello CONTENT CREATORS!

Use ontosense tags to directly indicate the meaning of your content, right from the content creation moment!

In addition to/instead of simple words ( #table #furniture), use an already well known/well trusted URL like #https://en.wikipedia.org/wiki/Table_(furniture) 

You can ontosense-tag numbers, words, small portions of text, paragraphs, whole messages and/or whole pages using ontosense content block markers { } followed by one or more relevant ontosense tags (#URL). Ontosense-tag your info with well known URLs, not (just) simple words/letter combinations. Include the most known and representative URLs, URLs that others would also use to point the respective meaning.

Ontosense tagging (#URL) can also be applied to images, as you may ontosense-tag multiple areas of an image to indicate the intended meaning of each area. Actually, you may ontosense tag any portion of any kind of content (audio, video, image, text etc.)

The Ontosense Whitepaper includes more details regarding and the proposed Semantic Links (#URLs or "ontosense tags") in addition to the omnipresent Navigational Link (the standard HTML link).

Sample ontosense annotation of a text

Below are some examples of using ontosense tags to indicate the meaning of a text by relating the content to concepts represented using widely known, common website URLs. Ontosense-enabled content editors and content browsers/viewers would never show the annotations visibly as below, the annotations are made clearly visible below just for the purpose of this explanation.  


{

The height of this {table}#https://en.wikipedia.org/wiki/Table_(furniture) is

{

45#https://en.wikipedia.org/wiki/Value_(mathematics)

{

cm#https://en.wikipedia.org/wiki/Centimetre

}#https://en.wikipedia.org/wiki/Units_of_measurement

}#https://en.wikipedia.org/wiki/Height 

making it

{

ideal for keeping

{

in front of your couch#https://en.wikipedia.org/wiki/Couch

}#https://en.wikipedia.org/wiki/Position 

}#https://en.wiktionary.org/wiki/beneficial.

It has a round#https://en.wikipedia.org/wiki/Shape shape with a diameter of 

{

50#https://en.wikipedia.org/wiki/Value_(mathematics)

{

cm#https://en.wikipedia.org/wiki/Centimetre

}#https://en.wikipedia.org/wiki/Units_of_measurement

}#https://en.wikipedia.org/wiki/Diameter

}#https://en.wikipedia.org/wiki/Table_(furniture)#https://www.ikea.com/us/en/cat/tables-desks-fu004/


Hello future SEMANTIC SERVICE PROVIDERS!

Hello CONTENT PROVIDERS!

Hello CONTENT CONSUMERS!

Interested in the details? Read the Ontosense Whitepaper .


Interested to implement?


Contact: