Humanizing Big Data with Alteryx

In my last post, I described Teradata Unified Data Architecture™ product for big data. In today's post, I will describe Teradata partner Alteryx which provides innovative technology that can help you to get the maximum business value from your analytics using the Teradata Unified Data Architecture.™

Companies can extract the highest value from big data by combining all relevant data sources in their analysis. Alteryx makes it easy to create workflows that combine and blend data from relevant sources, bringing new and ad hoc sources of data into the Teradata Unified Data Architecture™ for rapid analysis. Analysts can collect data within this environment using connectors and SQL-H interfaces for optimal processing.

Create Business Analytics in an Easy-to-Use Workflow Environment

Using the design canvas and step-by-step, workflow-based environment of Alteryx, you can create analytics and analytic applications. With a single click, you can put those applications and answers to critical business questions in the hands of those who need them most. And when business conditions and underlying data change, Alteryx helps you iterate your analytic applications quickly and easily, without waiting for an IT organization or expensive statistical specialists.

Base Your Decisions on the Foresight of Accessible Predictive Analytics

Alteryx helps you make critical business decisions based on forward-looking, predictive analytics rather than past performance or simple guesswork. By embedding predictive analytics tools based on the R open source statistical language or any of the in-database analytic capabilities, Alteryx makes powerful statistical techniques accessible to everyone in your organization through a simple drag-and-drop interface.

Understand Where and Why Things Happen: Location Matters

Whether you are building a hyper-local marketing and merchandizing strategy or trying to understand the value of social media investments, location matters. Traditionally, this type of insight has been in the hands of a few geo specialists focused on mapping and trade areas. With Alteryx, you can put location specific intelligence in the hands of every decision maker.

With the rise of location-enabled devices such as smart phones and tablets, consumer and business interactions increasingly include a location data-point. This makes spatial analysis more critical than ever before. Alteryx provides powerful geospatial and location intelligence tools as part of any analytic workflow. You can visualize where events are taking place and make location-specific decisions.

Alteryx can push custom spatial queries into the Teradata Database to leverage its processing power and eliminate data movement. You can enrich your spatial data within the Teradata system using any or all of these functions provided by Alteryx:

• geocoding of data;
• drive-time analytics;
• trade area creation;
• spatial and demographic analysis;
• spatial and predictive analysis;
• mapping.

Alteryx simplifies the previously complex tasks of predictive and spatial analytics, so every employee in your organization can make critical business decisions based on real, verifiable facts.

Deliver the Right Data for the Right Question To The Right Person

To answer today’s complex business questions, you need to access your sources of insight in a single environment.That is why Alteryx allows you to bring together data from virtually any data source, whether structured, unstructured or cloud data, into an analytic application. Using Alteryx, you can extend the reach of business insight by publishing applications that let your business users run in-database analytics and get fast answers to their pressing business questions.

Teradata and Alteryx: Powerful Insights for Business Users

To exploit the opportunities of all their data, organizations need flexible data architectures as well as sophisticated analytic tools. Analysts need to rapidly gather, make sense of and derive insights from all the relevant data to make faster, more accurate strategic decisions. But given the variety of potential data sources, it is difficult for any single tool to be most effective at capturing, storing and exploring data. Using the Teradata Unified Data Architecture™ with Alteryx enables you to explore data from multiple sources, as well as the ability to deploy the insights derived from the data.

You can create sophisticated analytics, taking advantage of new, multi-structured data sources to deliver the most ROI. The combined solution:

• integrates and addresses both structured and emerging multi-structured data Leverages the Teradata Integrated Data Warehouse, Teradata Aster Discovery platform and Hadoop to optimal advantage;
• creates both in-database and cross-platform analytics quickly without requiring specialized SQL, MapReduce or R programming skills;
• lets you combine the capabilities of the Alteryx environment with routines developed in other analytical tools within a single analytical workflow;
• easily deploys analytics to the appropriate users beyond the analyst community.

The combined solution of Alteryx and the Teradata Unified Data Architecture™ provides an IT-friendly environment that supports the need to analyze data found inside and outside the data warehouse. Analysts and business users can leverage powerful engines to create and execute integrated applications. This kind of analysis is only possible with an environment that can bring together routines created by separate tools and running on different platforms.

Enhancing the Teradata Unified Data Architecture™ with the speed and agility of Alteryx creates a powerful environment for traditional and self-service analytics using integrated data and massively parallel processing platforms. It delivers:

• a complete solution for the full life-cycle of strategic and big data analytics, from transforming, enriching and loading data to designing analytic workflows and putting easy-to-use analytic applications in the hands of business users;
• improved ability to manage and extract value from structured and multi-structured data;
• ability for business analysts to create data labs and perform predictive and spatial analytics on the Teradata data warehouse and Teradata Aster discovery platforms;
• faster analytical processing within applications using in-database analytics in Teradata and SQL MapReduce functions in Teradata Aster.

The Alteryx solution helps customers with the Teradata Unified Data Architecture™ achieve these benefits by providing the following:

• robust set of analytical functions;
• access to a rich catalog of horizontal and industry-specific analytic applications in the
• Alteryx Analytics Gallery;
• syndicated household, demographic, firmographic, map and Census data to enrich existing sources;
• native data integration and in-database analytical support for Teradata data warehouse and Teradata Aster capabilities;
• ability to leverage Teradata SQL-H™ for accessing Hadoop data from Aster or Teradata Database platforms.

Use Case: Predicting and Preventing Customer Leave

Problem

A global communication service provider is interested in preventing customers leaving by identifying at-risk customers and providing special offers that reduce the likelihood of leaving in a profitable way. To do this, they need predictive analytics.

Solution

Teradata and Alteryx deliver an end-to-end analytic workflow process from data consumption and analysis to application deployment. Alteryx integrates and loads call detail records from diverse sources along with customer data from the Teradata warehouse into the Aster database to create a complete, rich data set for iterative analysis. You can run iterative discovery analysis to determine the key indicators behind customer leaving and loyalty. These key indicators are captured as repeatable applications to enrich the data warehouse with leaving and loyalty scores. In addition, the discovery analysis is captured and deployed to the business users as a parameterized application for further iterative analysis.

Key Solution Components

• Aster Discovery platform for deep analytics and segmentation;
• Teradata data warehouse to operate and deploy insights and enriched data across the enterprise;
• Alteryx for the user workflow engine to orchestrate data blending and analytics.

Benefits

• Ability to identify key customers that are likely move candidates;
• determine problem spots on the network (cell sites, network elements) that are driving move;
• discover other key reasons for move (performance, competitive offers);
• discover which offers have prevented churn for similar customers in the past;
• identify which offers will work and evaluate a least-cost offer to prevent move;
• ability to make offers to keep customers from leaving;
• deeper understanding of customer behavior.

Teradata - Analytics for Big Data

Successful companies know that analytics is the key to winning customer loyalty, optimizing business processes and beating their competitors. 

By integrating data from multiple parts of the organization to enable cross-functional analysis and a 360-degree view of the customer, businesses can make the best possible decisions. With more data and more sophisticated analytics, you can realize even greater business value.

Today businesses can tap new sources of data for business analytics, including web, social, audio/video, text, sensor data and machine-generated data. But with these new opportunities come new challenges.

For example, structured data (from databases) fits easily into a relational database model with SQL-based analytics. Other semi-structured or unstructured data may require non-SQL analytics, which are difficult for business users and analysts who require SQL access and

iterative analytics.

Another challenge is identifying the nuggets of valuable data from among and between multiple data sources. Analysts need to run iterations of analysis quickly against differing data sets, using familiar tools and languages. Data discovery can be especially challenging if data is stored on multiple systems employing different technologies.

Finally, there is the challenge of simply handling all the data. New data sources often generate data at extremely high frequencies and volumes. Organizations need to capture, refine and store the data long enough to determine which data to keep, all at an affordable price.

To exploit the competitive opportunities buried in data from diverse sources, you need a strong analytic foundation capable of handling large volumes of data efficiently. Specifically, you need to address the following three capabilities:

Data Warehousing - integrated and shared data environments for managing the business and delivering strategic and operational analytics to the extended organization.

Data Discovery - discovery analytics to rapidly explore and unlock insights from big data using a variety of analytic techniques accessible to mainstream business analysts.

Data Staging - a platform for loading, storing and refining data in preparation for analytics.

Teradata Unified Data Architecture™ product includes a Teradata data warehouse platform and the Teradata Aster discovery platform for analytics, as well as open-source Apache Hadoop for data management and storage as needed.

Data Warehousing

The Teradata Active Enterprise Data Warehouse is the foundation of the integrated data warehouse solution. This appliance works well for smaller data warehouses or application-specific data marts.

Data Discovery

For data discovery, the Teradata platform uses patented SQL-MapReduce® on the Aster Big Analytics Appliance, providing pre-packaged analytics and applications for data-driven discovery. Mainstream business users can easily access this insight using familiar SQL-based interfaces and leading business intelligence (BI) tools. If you are performing discovery on structured data, a partitioned data lab in the data warehouse is the recommended solution.

Data Staging

Hadoop is an effective, low-cost technology for loading, storing and refining data within the unified architecture. However, Hadoop is not designed as an analytic platform.

The Teradata Data Warehouse Appliance and the Teradata Extreme Data Appliance offer cost-effective storage and analytics for structured data. The Teradata Unified Data Architecture™ integrates these components into a cohesive, integrated data platform that delivers the following capabilities:

• unified management of both structured and unstructured data at optimal cost;
• powerful analytics spanning SQL and MapReduce analytics;
• seamless integration with the existing data warehouse environment and user skillset.

The Teradata Unified Data Architecture™ handles all types of data and diverse analytics for both business and technical users while providing an engineered, integrated and fully supported solution.

conceptClassifier for SharePoint

conceptClassifier for SharePoint is the enterprise automatic semantic metadata generation and taxonomy management solution. It is based on an open architecture with all APIs based on XML and Web Services. conceptClassifier for SharePoint supports all versions of SharePoint, SharePoint Online, Office 365, and OneDrive for Business.

Incorporating industry recognized Smart Content Framework™ and intelligent metadata enabled solutions, conceptClassifier for SharePoint provides a complete solution to manage unstructured and semi-structured data regardless of where it resides.

Utilizing unique compound term processing technology, conceptClassifier for SharePoint natively integrates with SharePoint and solves a variety of business challenges through concept identification capabilities.

Key Features

• Tag content across the enterprise with conceptual metadata leveraging valuable legacy data.
• Classify consistent meaningful conceptual metadata to enterprise content, preventing incorrect meta tagging.
• Migrate tagged and classified content intelligently to locations both within and outside of SharePoint.
• Retrieve precise information from across the enterprise when and how it is needed.
• Protect sensitive information from exposure with intelligent tagging.
• Preserve information in accordance with records guidelines by identifying documents of record and eliminating inconsistent end user tagging.

Components

conceptClassifier

Both automated and manual classification is supported to one or more term sets within the Term Store and across content hubs.

conceptTaxonomyManager

This is an advanced enterprise class, easy-to-use taxonomy and term set development and management tool. It integrates natively with the SharePoint Term Store reading and writing in real-time ensuring that the taxonomy/term set definition is maintained in only one place, the SharePoint Term Store. Designed for use by Subject Matter Experts, the Term Store and/or taxonomy is easily developed, tested, and refined.

Term Set Migration tools are also a component of conceptTaxonomyManager that enable term sets to be developed on one server (e.g. on-premise server) and then migrated to another server (e.g. Office 365 server) in an incremental fashion and preserving all GUIDs. This is a key requirement in migration.

conceptSearch Compound Term Processing Engine

Licensed for the sole use of building and refining the taxonomy/term set, the engine provides automatic semantic metadata generation that extracts multi-word terms or concepts along with keywords and acronyms. conceptSearch is an enterprise search engine and is sold as a separate product.

SharePoint Feature Set

Provides SharePoint integration and an additional multi-value pick-list browse taxonomy control enabling users to combine free text and taxonomy browse searching.

Products

These are base platform and optional products that are needed to solve your particular business process challenge and leverage your SharePoint investment.

Search Engine Integration

This functionality is provided via conceptClassifier for SharePoint to integrate with any Microsoft search engine being used within SharePoint. conceptClassifier for SharePoint also supports integration with most non-SharePoint search engines and can perform on the fly classification with search engines calling the classify API.

Search engine support includes SharePoint, the former FAST products, Solr, Google Search Appliance, Autonomy, and IBM Vivisimo. If the FAST Pipeline Stage is required, this is sold as a separate product.

Intelligent Document Classification

This functionality is provided via conceptClassifier for SharePoint, to classify documents based upon concepts and multi-word terms that form a concept. Automatic and/or manual classification is included.

Content managers with the appropriate security can also classify content in real time. Content can be classified not only from within SharePoint but also from diverse repositories including File Shares, Exchange Public Folders, and websites. All content can be classified on the fly and classified to one or more taxonomies.

Taxonomy Management and Term Store Integration

With the Term Store functionality in SharePoint, organizations can develop a metadata model using out-of-the-box SharePoint capabilities. conceptClassifier for SharePoint provides native integration with the term store and the Managed Metadata Service application, where changes in the term store will be automatically available in the taxonomy component, and any changes in the taxonomy component will be immediately available in the term store.

A compelling advantage is the ability to consistently apply semantic metadata to content and auto-classify it to the Term Store metadata model. This solves the challenges of applying the metadata to a large number of documents and eliminates the need for end users to correctly tag content. Utilizing the taxonomy component, the taxonomies can be tested, validated, and managed, which is not a function provided by SharePoint.

Intelligent Migration

Using conceptClassifier for SharePoint, an intelligent approach to migration can be achieved. As content is migrated, it is analyzed for organizationally defined descriptors and vocabularies, which will automatically classify the content to taxonomies, or optionally the SharePoint Term Store, and automatically apply organizationally defined workflows to process the content to the appropriate repository for review and disposition.

Intelligent Records Management

The ability to intelligently identify, tag, and route documents of record to either a staging library and/or a records management solution is a key component to driving and managing an effective information governance strategy. Taxonomy management, automatic declaration of documents of record, auto-classification, and semantic metadata generation are provided via conceptClassifier for SharePoint and conceptTaxonomyWorkflow.

Data Privacy

Fully customizable to identify unique or industry standard descriptors, content is automatically meta-tagged and classified to the appropriate node(s) in the taxonomy based upon the presence of the descriptors, phrases, or keywords from within the content.

Once tagged and classified the content can be managed in accordance with regulatory or government guidelines. The identification of potential information security exposures includes the proactive identification and protection of unknown privacy exposures before they occur, as well as monitoring in real time organizationally defined vocabulary and descriptors in content as it is created or ingested. Taxonomy, classification, and metadata generation are provided via conceptClassifier for SharePoint.

eDiscovery, Litigation Support, and FOIA Requests

Taxonomy, classification, and metadata generation are provided via conceptClassifier for SharePoint. This is highly useful when relevance, identification of related concepts, vocabulary normalization are required to reduce time and improve quality of search results.

Text Analytics

Taxonomy, classification, and metadata generation are provided via the conceptClassifier for SharePoint. A third party business intelligence or reporting tool is required to view the data in the desired format. This is useful to cleanse the data sources before using text analytics to remove content noise, irrelevant content, and identify any unknown privacy exposures or records that were never processed.

Social Networking

Taxonomy, classification, and metadata generation are provided via conceptClassifier for SharePoint. Integration with social networking tools can be accomplished if the tools are available in .NET or via SharePoint functionality. This is useful to provide structure to social networking applications and provide significantly more granularity in relevant information being retrieved.

Business Process Workflow

conceptTaxonomyWorkflow serves as a strategic tool managing migration activities and content type application across multiple SharePoint and non-SharePoint farms and is platform agnostic. This add-on component delivers value specifically in migration, data privacy, and records management, or in any application or business process that requires workflow capabilities.

conceptTaxonomyWorkflow is required to apply action on a document, optionally automatically apply a content type and route to the appropriate repository for disposition.

An additional add-on product, conceptContentTypeUpdater is deployed at the site collection level, can be used by site administrators, and will change the SharePoint content type based on results from pre-defined workflows and is used only in the SharePoint environment.

Where does conceptClassifier for SharePoint fill the gaps?

• SharePoint has no ability to automatically create and store classification metadata.
• SharePoint has no taxonomy management tools to manage, test, and validate taxonomies based on the Term Store.
• SharePoint has no auto-classification capabilities.
• SharePoint has no ability to generate semantic metadata and surface it to search engines to improve search results.
• SharePoint has no ability to automatically tag content with vocabulary or retention codes for records management.
• SharePoint has no ability to automatically update the content type for records management or privacy protection and route to the appropriate repository.
• SharePoint has no ability to provide intelligent migration capabilities based on the semantic metadata within content, identify previously undeclared documents of record, unidentified privacy exposures, or information that should be archived or deleted.
• SharePoint has no ability to provide granular and structured identification of people, content recommendations, and organizational knowledge assets.

Leveraging Your SharePoint Investment

When evaluating a technology purchase and the on-going investment required to deploy, customize, and maintain, the costs can scale quickly. Because conceptClassifier for SharePoint is an enterprise infrastructure component, you can leverage your investment through:

• Native real-time read/write with the term store.
• Ability to implement workflow and automatic content type updating.
• Reduce IT Staff requirements to support diverse applications.
• Reduce costs associated with the purchase of multiple, stand-alone applications
• Deploy once, utilize multiple times.
• Rapidly integrated with any SharePoint or any .Net application.
• Used by Subject Matter Experts, not IT staff, does not require outside resources to manage and maintain.
• Eliminate unproductive and manual end user tagging and the support required by business units and IT.
• Reduce hardware expansion costs due to scalability and performance features.
• Deployable as an on-premise, cloud, or hybrid solution.

Leveraging Your Business Investment

The real value of your investment includes both technology and the demonstrable ROI that can be generated from improving business processes. conceptClassifier for SharePoint has been deployed to solve individual or multiple challenges including:

• Enables concept based searching regardless of search engine.
• Reduces organizational costs associated with data exposures, remediation, litigation, fines and sanctions.
• Eliminates manual metadata tagging and human inconsistencies that prohibit accurate metadata generation.
• Prevents the portability and electronic transmission of secured assets.
• Assists in the migration of content by identifying records as well as content that should have been archived, contains sensitive information, or should be deleted.
• Protects record integrity throughout the individual document lifecycle.
• Creates virtual centralization through the ability to link disparate on-premise and off-premise content repositories.
• Ensures compliance with industry and government mandates enabling rapid implementation to address regulatory changes.

Benefits

The combination of the Smart Content Framework™, conceptClassifier for SharePoint, and the deployment of intelligent metadata enabled solutions result in a comprehensive and complete approach to SharePoint enterprise metadata management. Specific benefits are:

• Eliminate manual tagging.
• Improve enterprise search.
• Facilitate records management.
• Detect and automatically secure unknown privacy exposures.
• Intelligently migrate content.
• Enhance eDiscovery, litigation support, and FOIA requests.
• Enable text analytics.
• Provide structure to Enterprise 2.0.

Dublin Core Metadata Applications - Web Ontology Language (OWL)

In my last post, I described one of the most used applications of Dublin Core Metadata - RDF. In today's post, I will describe second most used applications of Dublin Core Metadata - Web Ontology Language (OWL).

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontology. Ontology is a formal way to describe taxonomy and classification networks, essentially defining the structure of knowledge for various domains: the nouns represent classes of objects and the verbs represent relations between the objects.

An ontology defines the terms used to describe and represent an area of knowledge. Ontologies are used by people, databases, and applications that need to share domain information (a domain is just a specific subject area or area of knowledge, like medicine, tool manufacturing, real estate, automobile repair, financial management, etc.). Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them. They encode knowledge in a domain and also knowledge that spans domains. In this way, they make that knowledge reusable.

Ontology resembles class hierarchies. It is meant to represent information on the Internet and are expected to be evolving almost constantly. Ontologies are typically very flexible as they are coming from all sorts of data sources.

The OWL languages are characterized by formal semantics. They are built upon a W3C XML standard for RDF objects. I described RDF in my previous post.

The data described by an ontology in the OWL family is interpreted as a set of "individuals" and a set of "property assertions" which relate these individuals to each other. An ontology consists of a set of axioms which place constraints on sets of individuals (called "classes") and the types of relationships permitted between them. These axioms provide semantics by allowing systems to infer additional information based on the data explicitly provided.

OWL ontologies can import other ontologies, adding information from the imported ontology to the current ontology.

For example: an ontology describing families might include axioms stating that a "hasMother" property is only present between two individuals when "hasParent" is also present, and individuals of class "HasTypeOBlood" are never related via "hasParent" to members of "HasTypeABBlood" class. If it is stated that the individual Harriet is related via "hasMother" to the individual Sue, and that Harriet is a member of the "HasTypeOBlood" class, then it can be inferred that Sue is not a member of "HasTypeABBlood".

The W3C-endorsed OWL specification includes the definition of three variants of OWL, with different levels of expressiveness. These are OWL Lite, OWL DL and OWL Full

OWL Lite

OWL Lite was originally intended to support those users primarily needing a classification hierarchy and simple constraints. It is not widely used.

OWL DL

OWL DL includes all OWL language constructs, but they can be used only under certain restrictions (for example, number restrictions may not be placed upon properties which are declared to be transitive). OWL DL is so named due to its correspondence with description logic, a field of research that has studied the logics that form the formal foundation of OWL.

OWL Full

OWL Full is based on a different semantics from OWL Lite or OWL DL, and was designed to preserve some compatibility with RDF Schema. For example, in OWL Full a class can be treated simultaneously as a collection of individuals and as an individual in its own right; this is not permitted in OWL DL. OWL Full allows an ontology to augment the meaning of the pre-defined (RDF or OWL) vocabulary.

OWL Full is intended to be compatible with RDF Schema (RDFS), and to be capable of augmenting the meanings of existing Resource Description Framework (RDF) vocabulary. This interpretation provides the meaning of RDF and RDFS vocabulary. So, the meaning of OWL Full ontologies are defined by extension of the RDFS meaning, and OWL Full is a semantic extension of RDF.

Every OWL ontology must be identified by an URI. For example: Ontology(). The languages in the OWL family use the open world assumption. Under the open world assumption, if a statement cannot be proven to be true with current knowledge, we cannot draw the conclusion that the statement is false.

Languages in the OWL family are capable of creating classes, properties, defining instances and its operations.

Instances

An instance is an object. It corresponds to a description logic individual.

Classes

A class is a collection of objects. It corresponds to a description logic (DL) concept. A class may contain individuals, instances of the class. A class may have any number of instances. An instance may belong to none, one or more classes. A class may be a subclass of another, inheriting characteristics from its parent superclass.

Class and their members can be defined in OWL either by extension or by intension. An individual can be explicitly assigned a class by a Class assertion, for example we can add a statement Queen Elizabeth is a(an instance of) human, or by a class expression with ClassExpression statements of every instance of the human class who has a female value to is an instance of the woman class.

Properties

A property is a directed binary relation that specifies class characteristics. It corresponds to a description logic role. They are attributes of instances and sometimes act as data values or link to other instances. Properties may possess logical capabilities such as being transitive, symmetric, inverse and functional. Properties may also have domains and ranges.

Datatype Properties

Datatype properties are relations between instances of classes and RDF literals or XML schema datatypes. For example, modelName (String datatype) is the property of Manufacturer class. They are formulated using owl:DatatypeProperty type.

Object Properties

Object properties are relations between instances of two classes. For example, ownedBy may be an object type property of the Vehicle class and may have a range which is the class Person. They are formulated using owl:ObjectProperty.

Operators

Languages in the OWL family support various operations on classes such as union, intersection and complement. They also allow class enumeration, cardinality, and disjointness.

Metaclasses

Metaclasses are classes of classes. They are allowed in OWL full or with a feature called class/instance punning.

Syntax

The OWL family of languages supports a variety of syntaxes. It is useful to distinguish high level syntaxes aimed at specification from exchange syntaxes more suitable for general use.

High Level

These are close to the ontology structure of languages in the OWL family.

OWL Abstract Syntax

This high level syntax is used to specify the OWL ontology structure and semantics.

The OWL abstract syntax presents an ontology as a sequence of annotations, axioms and facts. Annotations carry machine and human oriented metadata. Information about the classes, properties and individuals that compose the ontology is contained in axioms and facts only. Each class, property and individual is either anonymous or identified by an URI reference. Facts state data either about an individual or about a pair of individual identifiers (that the objects identified are distinct or the same). Axioms specify the characteristics of classes and properties.

OWL2 Functional Syntax

This syntax closely follows the structure of an OWL2 ontology. It is used by OWL2 to specify semantics, mappings to exchange syntaxes and profiles

OWL2 XML Syntax

OWL2 specifies an XML serialization that closely models the structure of an OWL2 ontology.

Manchester Syntax

The Manchester Syntax is a compact, human readable syntax with a style close to frame languages. Variations are available for OWL and OWL2. Not all OWL and OWL2 ontologies can be expressed in this syntax.

OWL is playing an important role in an increasing number and range of applications, and is the focus of research into tools, reasoning techniques, formal foundations and language extensions.