What is the semantic wave?
A tidal wave of four Internet growth stages.

The semantic wave embraces four stages of internet growth. The first stage, Web 1.0, was about connecting information and getting on the net. Web 2.0 is about connecting people — putting the “I” in user interface, and the “we” into Webs of social participation. The next stage, Web 3.0, is starting now. It is about representing meanings, connecting knowledge, and putting these to work in ways that make our experience of internet more relevant, useful, and enjoyable. Web 4.0 will come later. It is about connecting intelligences in a ubiquitous Web where both people and things reason and communicate together.

How is Web 3.0 different from previous stages of internet evolution?
Knowledge computing drives new value creation and solves problems of scale and complexity.

The basic shift occurring in Web 3.0 is from information-centric to knowledge-centric patterns of computing. Web 3.0 will enable people and machines to connect, evolve, share, and use knowledge on an unprecedented scale and in new ways that make our experience of the internet better.

Web growth continues to accelerate. Dimensions of net expansion include communications bandwidth, numbers of people connected, numbers and kinds of devices that are IP-aware, numbers of systems and applications, quantities of information, and types of media. As the internet expands, needs world-wide are outstripping the capacities and capabilities of current information and communications technologies (ICT) and architectures. Information-centric patterns of computing have reached the limit of what they can provide to cope with problems of scale, complexity, security, mobility, rich media interaction, and autonomic behavior.

Web 3.0 will solve these problems and lay a foundation for the coming ubiquitous Web of connected intelligences. The Web 3.0 solution, simply put, is to give the internet a knowledge space.

What semantic technologies will power Web 3.0?
Digital tools that represent and reason about meanings, theories, and know-how separately from documents, data, and program code.

The key notion of semantic technology is to represent meanings and knowledge (e.g., knowledge of something, knowledge about something, and knowledge how to do something, etc.) separately from content or behavior artifacts, in a digital form that both people and machines can access and interpret. As a platform, Web 3.0 will embrace all semantic technologies and open standards that can be applied on top of the current Web. It is not restricted just to current Semantic Web standards.

Web 3.0 will encompass a broad range of knowledge representation and reasoning capabilities including pattern detection, deep linguistics, ontology and model based inferencing, analogy and reasoning with uncertainties, conflicts, causality, and values. The spectrum of knowledge representation that spans tag collections (or folksonomies); to dictionaries, taxonomies and thesauri; to schemas and conceptual models; to ontologies and theory-based logics, to axiologies (value-based reasoning), and entirely new uses barely tapped. Reasoning requires knowledge representation. We choose more powerful forms of representation to enable more powerful kinds of reasoning and problem solving. 

What are the functions of semantic technologies?
Create, discover, represent, organize, process, manage, reason with, present, share, and utilize meanings and knowledge to accomplish business, personal, and societal purposes.

Key functions of semantic technologies include:

• Represent knowledge and meaning
• Discover, organize, integrate, and interoperate representations and resources at the level of concepts (and relationships) across artifacts and boundaries
• Reason, interpret, infer, and answer using pattern reasoning, deep linguistics, and symbolic inferencing of semantics, facts, theories, and values
• Provision, present, communicate, and act using semantics
• Learn, adapt, evolve, and improve performance with use and scale.

Semantic technologies and information technologies differ in the way they represent knowledge and meanings and how they reason with them. All software technologies start with knowledge representation of some kind. What’s different is how they represent and reason with meanings and facts.

Semantic technologies build “glass boxes.” They organize meanings in ways that are accessible externally at run time, using taxonomies, ontologies and knowledgebases. These structures are relatively easy to modify for new concepts, relationships, properties, constraints and instances. Semantic technologies integrate data, content, applications, and processes via a shared ontology which minimizes costs and effort to develop and maintain.

Information technologies build “black boxes.” They represent meanings using flat files (simple schemas), relational data models (RDBMS), and object-oriented models (OODBMS). These structures are fixed at design time and locked inside program code, making them difficult to modify for new concepts and relationships. Integrating data and processes typically requires point-to-point interfaces and connectors that are costly to develop and maintain since the knowledge required must be hard coded in each connection rather than shared via a common metamodel that a program could interpret for itself.

Semantic technologies reason via associations, logic, constraints, rules, conditions, and axioms that are represented in a knowledge model, or ontology, separately from application code. So, different reasoners can access the same knowledge. Declarative structure allows reasoning in multiple directions, and in ways that may not have been foreseen by the system designer. For example the same knowledgebase can be used to answer questions about how, why, and what-if as well as give factual responses.

Also, semantic technologies allow development of programs that can “learn” (infer and create new knowledge) simulate and test, and adapt behavior based on experience. Information technologies reason via fixed algorithms that are embedded in application code. Algorithms are preprogrammed behaviors, like instinct. They perform a rote task. If anything is learned, people must update the logic off-line to create a new version of the program.

What coverage of semantic technologies will you find in
Semantic Exchange?
All sorts of topics, from knowledge representation and reasoning, to semantic user experience, to semantic social computing, to semantic applications, to semantic infrastructure.

Semantic Exchange members are invited to submit questions, identify topics, and contribute to semantic technology pages. Please see the guide for authoring and our policy for intellectual property rights.

Semantic technology topics currently covered are listed at the bottom of this page. 

Semantic technology areas of interest include (but are not limited to):

Knowledge and Reasoning

Knowledge is defined as theory plus information that reduces uncertainty. Topics include philosophical and computational views of knowledge from patterns, to language, to ontology, to logic, to axiology.

• Knowledge representation is the application of values, logic and ontology to the task of constructing computable models of some domain. Topics span the spectrum of progressively more capable forms of knowledge representation that span tag collections (or folksonomies); to dictionaries, taxonomies and thesauri; to schemas and conceptual models; to ontologies and theory-based logics, to axiologies (value- based reasoning), and entirely new uses barely tapped.
• Reasoning is the derivation of inferences and the warranting of conclusions through application of heuristics, rules, analogies, mathematics, logic, and values. Topics of interest span a broad range of knowledge representation and reasoning capabilities including pattern detection and machine learning; deep linguistics; ontology and model based inferencing; and reasoning with uncertainties, conflicts, causality, analogies, and values.
• Standards for knowledge representation and reasoning exist and are emerging. They differ in purpose, syntax, expressiveness and precision. Topics of interest include all semantic technologies and open standards that can be applied on top of the current web. We're interested in current directions in standards for knowledge representation and reasoning, including trends toward multilingualism and transemantic synthesis of diverse languages and standards. Also of interest are  practices to align concepts and resolve semantic differences between domains and communities. 

Semantic user experience

Semantic user experience is the addition intelligence and context-awareness to make the user interface more adaptive, dynamic, advisory, proactive, autonomic, and autonomous, and the resulting experience easier, more useful, and more enjoyable. Topics of interest include identity, context, mobility, and intelligent user interface.

• Identity is information used to prove the individuality of a person as a persisting entity. The trend is towards semantic avatars that enable individual to manage and control their personal information, wherever it is across the net.
• Context is information that characterizes a situation of an entity, object, event, etc. Context awareness is using this knowledge to sense, predict, interpret and respond to a situation.
• Mobility is the new platform for delivering user experience. It demands semantic technologies to enable seamless, self-configuring, customizable, context-aware services, anytime, anywhere.
• Semantic browsers will understand the semantics of data, will broker information, and will automatically interpret metadata. The emerging display landscape will be semantically connected and contextually aware. It will unify displaying and interacting. It will personalize experience. Reality browsing is querying the physical world live and up close from anywhere.
• Intelligent user interface learns, cooperates, and acts autonomously. It is autonomic and capable of flexible, purposeful reasoning action in pursuit of one or more goals. An intelligent UI knows about a variety of things such as system functionality, tasks that users might want to do, ways that information might be presented or provisioned. Also, intelligent UIs know about the user (via user models), which enables tailoring system behavior and communications. Adding intelligence helps users perform tasks, while making working with the computer more helpful, and as invisible as possible. As a result, semantic systems do more for the user, yield more relevant results with less effort, and deliver a more enjoyable experience.

Semantic social computing

Social computing is software and services that support group interaction. Semantic social computing adds an underlying knowledge representation to data, processes, services, and software functionality. Semantic technologies will enrich many categories social applications including instant messaging, email, bookmarking, blogging, social networking, wikis, user driven “communitainment”, and do-it-yourself applications and services.

Semantic applications

Semantic applications put knowledge to work. Technology themes and transitions of interest include:

•  
• From knowledge in paper documents, to digital documents, to knowledge (semantic models), to semantic agents;
• From static and passive functional processes, to active, adaptive, and dynamic processes, to autonomic to autonomous processes;
• From programmer encoded interpretations of meaning and logic at design time, to computer interpretation of meaning and logic at run time;
• From smart program code to smart data;
• From search to knowing; and
• From reasoning with SQL to first order logic, to complex reasoning with uncertainty, conflict, causality, and values for the purposes of discovery, analysis, design, simulation, and decision-making.

Also, Semantic Exchange devotes a section of the site to discussing semantic applications. 

Semantic infrastructure.

Infrastructure is the basic features of a system such as networks, facilities, services, and installations that are needed for the functioning of internet- based communities. Semantic infrastructures adds a knowledge dimension to this underlying structure that provide solutions to problems of integration, interoperability, parallelism, mobility, ubiquity/pervasiveness, scale, complexity, speed, power, cost, performance, autonomics, automation, intelligence, identity, security, ease of programming, and ease of use. Semantic infrastructure topics of interest  include:

• Computing architecture — Is it diverging into declarative (brain) and procedural (sensory organs) lines of development?
• Storage — moving from flat files, to centralized “bases” with relational operators, to federated “spaces” with native semantic operators. Trends toward high-performance semantic processing at scale and representations that support nearly unlimited forms of reasoning.
• Transport — moving from dial-up, to broadband, to video bandwidth. Mobility is the new platform, and semantic technologies are needed to deliver seamless, customizable, context aware services, any time, anywhere.
• Processor — role of semantic technology as processors go parallel, multi-core, multi-threaded, meshed, and specialized 
• Displays — semantic technology in a landscape of interoperable devices of differing characteristics, sizes and capabilities. Boundaries between virtual and real dissolve in planned and unplanned ways. Trends towards immersive experience and reality browsing.
• Semantic development methodologies and practices — diverges from procedural and object-oriented approaches. How does semantic modeling reduce time, cost, and risk to develop solutions.
• Semantic ecosystems — longer term trends towards every thing becoming connected, somewhat intelligent, somewhat self-aware, socially autopoeitic, and autonomically capable of solving problems of complexity, scale, security, trust, and change management.