Inferencing (introduction)

Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychologyartificial intelligence researchers develop automated inference systems to emulate human inference.  A constituent to the means through which semantic web technology supports ‘artificial intelligence’ functionality is by way of Semantic Inferencing.

Semantic Inferencing makes use of available structured data, formatted through the use of ontologies, to support the means through which assumed conclusions can be presented with a probabilistic degree of certainty.

The more data-points made available in connection to a specified form of query, the greater systems are able to improve the probability of query responses being correct.  Semantic inferencing is an important constituent to the broader eco-system of ‘semantic web tools’.

Semantic Web (An Intro)

Around the year 2000, a concept called the ‘semantic web‘ was brought together and has continued to evolve since.  By 2007, the use of semantic web technology had grown to a point where tim berners-lee wrote an article about the ‘Giant Global Graph‘ that had been forged through the use of it.

W3C technology road map. In the end, all W3C activities are in service to the top-level goal of reaching the semantic Web’s full potential. Arrows indicate “how” things are implemented; following them in reverse indicates “why” they exist (or should) IEEE INTERNET COMPUTING JULY • AUGUST 2001 PG: 13


Historically, it is noted that a constituent of the origins for these technologies were indeed born out of DARPA Agent Markup Language.

The ‘semantic web’ ecosystem of technologies has an array of different names and technical constituents which have developed overtime.

Critically, semantic web employs the use of RDF in an array of different serialisation formats.  Almost any form of data can be converted into RDF.

Once data is stored in an RDF format,  it can therefore be employed by systems that provide the means to query data structured in this format. The means through which this is done is most-often by way of a family of query language services denoted moreover as sparql.

Sparql family solutions include (but are not limited to) sparql-mm that provides support for multimedia, Sparql-FED that provides the means to query multiple end-points.

Somewhere around 2009 a rebranding attempt for Semantic Web (“SemWeb”) & RDF; to the term ‘Linked Data‘ was started to be made.  Whilst the implication and extensive nature of technology use is not well-known, it is indeed the case that the vast majority of web services contain RDF and are therefore constituent elements of the broader ‘semantic web’.

One of the ways this can be better understood is by reviewing the means through which ontologies are currently used and/or installing relevent plugins that also provide the necessary tools, to make it easier to see the ‘web of (structured) data’.


Introduction to Ontologies

On the Semantic Web, vocabularies define the concepts and relationships (also referred to as “terms”) used to describe and represent an area of concern. Vocabularies are used to classify the terms that can be used in a particular application, characterize possible relationships, and define possible constraints on using those terms. In practice, vocabularies can be very complex (with several thousands of terms) or very simple (describing one or two concepts only).

There is no clear division between what is referred to as “vocabularies” and “ontologies”. The trend is to use the word “ontology” for more complex, and possibly quite formal collection of terms, whereas “vocabulary” is used when such strict formalism is not necessarily used or only in a very loose sense. Vocabularies are the basic building blocks for inference techniques on the Semantic Web.


The most commonly used Ontologies are schemaorg which is most notably used for powering search, alongside Open Graph Protocol, which is used to support the means through which web-content can be reposted on facebook.

Beyond these two notable examples, an array of public ontologies can be found through sites such as The Linked Open Data Cloud site.


Verifiable Claims (An Introduction)

An important part of human ‘identity’ is the way claims are made about a person, and in relation to a person.  Claims related ‘instruments’ are used throughout society, as to be relied upon in association to many interactions.

The W3 community group ‘credentials‘ was established to support works designed to deliver outcomes required in this area.

Part of related works produced include the open-badge version 2 specification which can be found here.

These works make use of RDF and URIs to support the development and use of claims made between an authority of some sort, and what may be called ‘the data subject’;  For instance,

A person has a bankcard that supports their needs to make payments.  The banking card is owned by the financial institution providing the financial instrument or ‘card’.  The purpose of it being provided to the person, is to support their means to use the card to make use of their bank-account.


A Birth Certificate is issued most-often, by a government. The ‘subject’ of that document is the person whom the certificate provides evidence about in relation to their birth.  The information presented by a birth certificate includes statements about whether or not the person is over a certain age (ie: over 18 or over 21), where they were born / nationality, who their parents were, etc.

A Postage Stamp

A postage stamp is applied to a item that is sent through the post.  The stamp, and related markings made by the postage service provider assists in verifying that envelope (and its contents) have been sent through the mail system at a particular point in time, etc.


RDF based ‘verifiable claims’ provide the means to employ 3rd party, claims made in relation to people; as a constituent of the semantics employed in running, processing and subsequently presenting a query.