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Semantic Web Company and Ontotext partner to advance enterprise knowledge graphs

Ontotext (OT) and Semantic Web Company (SWC) announced a strategic partnership to meet the requirements of enterprise architects such as deployment, monitoring, resilience, and interoperability with other enterprise IT systems and security. Users will be able to work with a feature-rich toolset to manage a graph composed of billions of edges that is hosted in data centers around the world. The companies have implemented an integration of the PoolParty Semantic SuiteTM v.8 with the GraphDB and Ontotext Platform, which offers benefits for numerous use cases:

  • GraphDB powering PoolParty: Most of the knowledge graph management tools out there bundle open-source solutions that are good at managing thousands of concepts, whereas PoolParty bundled with GraphDB manages millions of concepts and entities—without extra deployment overheads.
  • PoolParty linked to high-availability GraphDB cluster: GraphDB can now be used as an external store for PoolParty, which offers a combination of performance, scalability and resilience. This is particularly relevant for organizations intent on developing tailor-made knowledge graph platforms integrated into their existing data and content management infrastructure.
  • Dynamic text analysis using big knowledge graphs: PoolParty can be used to edit big knowledge graphs in order to tune the behavior of Ontotext’s text analysis pipelines, which employ vast amounts of domain knowledge to boost precision. This way the power and comprehensiveness of generic off-the-shelf natural language processing (NLP) pipelines can be custom-tailored to an enterprise.
  • GraphQL benefits for PoolParty: Application developers can now access the knowledge graph via GraphQL to build end-user applications or integrate knowledge graph services with the functionality of existing systems. Ontotext Platform uses semantic business objects, defined by subject matter experts and business analysts, to generate GraphQL interfaces and transform them into SPARQL.,

Introduction to Semantic Technology

Ten years ago I had a belief that a meta-data approach to managing enterprise information was a valid way to go. The various structures, relationships and complexities of IT systems led to disjointed information. By relating the information elements to each other, rather than synchronizing the information together, we _might_ stand a chance.

At the same time a new set of standards was emerging, standards to describe, relate and query a new information model, based on meta-data, these became know as the Semantic Web, outlined in a Scientific American article ( ) in 2001.

Fast forward to 2008 – where are we with this vision. Some part of me is thrilled, another part disappointed. We have adoption of these standards and this approach at use in everyday information management situations. Major software companies and startup’s alike are implementing Semantic Technology in their offerings and products. However, I am disappointed that we still find it hard to communicate what this semantic technology means and how valuable it is. Most technologists I meet glaze over at the mention of the Semantic Web or any of it’s standards, yet when asked if they think RSS is significant, praise it’s contributions.

Over a series of posts to this blog, I would like to try and explain, share and show some of the value of Semantic Technology and why one should be looking at it.

Let’s start with what is Semantic Technology and what are the standards that define it’s openness. To quote Wikipedia “In software, semantic technology encodes meanings separately from data and content files, and separately from application code.” This abstraction is a core tenant and value provided by a Semantic approach to information management. The idea that our database or programming patterns do no restrict the form or boundaries of our information is a large shift from traditional IT solutions. The idea that our business logic should not be tied to the code that implements it, nor the information that it operates on is all provided through this semantic representation. So firstly ABSTRACTION is a key definition.

The benefit of this is that systems, machines, solutions, whatever term you wish to use can interact with each other – share, understand and reason, without having been explicitly programmed to understand each other.

With this you get to better manage CHANGE. Your content and systems can evole or change with the changes managed through the Semantic Technology layer.

So what makes up Semantic Technology, one sees the word in a number of soltuions or technologies, are they all created equal.

In my view, Semantic Technology can only truly claim to be so, if it is based on and implements the standards laid out through the (W3C) World Wide Web Consortium standards process.

The vision of the Semantic Web and the standards required to support it continue to expand, but the anchor standards have been laid out for a while.

RDF – The model and syntax for describing information. It is important to understand that with the RDF standards there are multiple things defined to create this standard – the model (or data model) , the syntax (how it is written/serialized) and the formal semantics (or logic described by the use of rdf). In 2004, the original RDF specification was revised and published as 6 separate documents, each covering an important area of the standard.

RDF-S – Provides a typing system for RDF and the basic constructs for expressing Ontologies and relationships within the meta data structure.

OWL – To quote the W3C paper, this facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF-S by providing additional vocabulary along with a formal semantics.

SPARQL – While anyone with a Semantic Technology solution invented there own query language (why was this never there one in the first place!), SPARQL, pronounced “sparkle” is the w3c standardization of one. It is HUGE for Semantic Technology and makes all the effort with the other three standards worthwhile.

These standards are quite a pile to sift through, understanding the capabilities embodied in them takes significant effort, but it is the role of technologists in this arena to remove that need for you to understand them. It is our job to provide tools, solutions and capabilities that leverage the these standards bringing semantic technology to life and deliver the power defined within them.

But that is the subject of another post. So what does this all mean in real life? In my next post I will layout a concrete example using product information as an example.


W3C Opens Data on the Web with SPARQL

W3C (The World Wide Web Consortium) announced the publication of SPARQL, the key standard for opening up data on the Semantic Web. With SPARQL query technology, pronounced “sparkle,” people can focus on what they want to know rather than on the database technology or data format used behind the scenes to store the data. Because SPARQL queries express high-level goals, it is easier to extend them to unanticipated data sources, or even to port them to new applications. Many successful query languages exist, including standards such as SQL and XQuery. These were primarily designed for queries limited to a single product, format, type of information, or local data store. Traditionally, it has been necessary to formulate the same high-level query differently depending on application or the specific arrangement chosen for the relational database. And when querying multiple data sources it has been necessary to write logic to merge the results. These limitations have imposed higher developer costs and created barriers to incorporating new data sources. The goal of the Semantic Web is to enable people to share, merge, and reuse data globally. SPARQL is designed for use at the scale of the Web, and thus enables queries over distributed data sources, independent of format. Because SPARQL has no tie to a specific database format, it can be used to take advantage of “Web 2.0” data and mash it up with other Semantic Web resources. Furthermore, because disparate data sources may not have the same ‘shape’ or share the same properties, SPARQL is designed to query non-uniform data. The SPARQL specification defines a query language and a protocol and works with the other core Semantic Web technologies from W3C: Resource Description Framework (RDF) for representing data; RDF Schema; Web Ontology Language (OWL) for building vocabularies; and Gleaning Resource Descriptions from Dialects of Languages (GRDDL), for automatically extracting Semantic Web data from documents. SPARQL also makes use of other W3C standards found in Web services implementations, such as Web Services Description Language (WSDL).

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