What is Semantics?
Semantics is the study of meaning. By creating a common understanding of the meaning of things, semantics helps us better understand each other. Common meaning helps people understand each other despite different experiences or points of view. Common meaning helps computer systems more accurately interpret what people mean. Common meaning enables disparate IT systems – data sources and applications – to interface more efficiently and productively.
What is an Ontology?
An ontology defines all of the elements involved in a business ecosystem and organizes them by their relationship to each other. The benefits of building an ontology are:
- Everyone agrees on a common set of terms used to describe things
- Different systems – databases and applications – can communicate with each other without having to directly connect to each other.
An Ontology is a set of formal concept definitions.
An Enterprise Ontology is an Ontology of the key concepts that organize and structure an Organization’s information systems. Having an Enterprise Ontology provides a unifying whole that makes system integration bearable.
An Enterprise Ontology is like a data dictionary or a controlled vocabulary, however it is different in a couple of key regards. A data dictionary, or a controlled vocabulary, or even a taxonomy, relies on humans to read the definitions and place items into the right categories. An ontology is a series of rules about class (concept) membership that uses relationships to set up the inclusion criteria. This has several benefits, one of the main ones being that a system (an inference engine) can assign individuals to classes consistently and automatically.
By building the ontology in application neutral terminology it can fill the role of “common denominator” between the many existing and potential data sources you have within your enterprise. Best practice in ontology building favors building an Enterprise Ontology with the fewest concepts needed to promote interoperability, and this in turns allows it to fill the role of “least common denominator”
Building an Enterprise Ontology is the jumping off point for a number of Semantic Technology initiatives. We’ll only mention in passing here the variety of those initiatives (we invite you to poke around our web site to find out more) . We believe that Semantic Technology will change the way we implement systems in three major areas:
- Harvest – Most of the information used to run most large organizations comes from their “applications” (their ERP or EHR or Case Management or whatever internal application). Getting new information is a matter of building screens in these applications and (usually) paying your employees to enter data, such that you can later extract it for other purposes. Semantic Technology introduces approaches to harvest data not only from internal apps, but from Social Media, unstructured data and the vast and growing sets of publicly available data waiting to be integrated.
- Organize – Relational, and even Object Oriented, technology, impose a rigid, pre-defined structure and set of constraints on what data can be stored and how it is organized. Semantic Technology replaces this with a flexible data structure that can be changed without converting the underlying data. It is so flexible that not all the users of a data set need to share the same schema (they need to share some part of the schema, otherwise there is no basis for sharing, but they don’t need to be in lockstep, each can extend the model independently). Further the semantic approach promotes the idea that the information is at least partially “self-organizing.” Using URIs (Web based Uniform Resource Identifiers) and graph-based databases allows these systems to infer new information from existing information and then use that new information in the dynamic assembly of data structures.
- Consume — Finally we think semantic technology is going to change the way we consume information. It is already changing the nature of work flow-oriented systems (ask us about BeInformed). It is changing data analytics. It is the third “V” in Big Data (“Variety”). Semantic Based mashups are changing the nature of presentation. Semantic based Search Engine Optimization (SEO) is changing internal and external search.
Given all that, how does one get started?
Well you can do it yourself. We’ve been working in this space for nearly ten years and have been observing clients take on a DIY approach, and while there have been some successes, in general we see people recapitulating many of the twists and turns that we have worked through over the last decade.
You can engage some of our competitors (contact us and we’d be happy to give you a list). But, let us warn you ahead of time: most of our competitors are selling products, and as such their “solutions” are going to favor the scope of the problem that their tools address. Nothing wrong with that, but you should know going in, that this is a likely bias. And, in our opinion, our competitors are just not as good at this as we are. Now it may come to pass that you need to go with one of our competitors (we are a relatively small shop and we can’t always handle all the requests we get) and if so, we wish you all the best…
If you do decide that you’d like to engage us, we’d suggest a good place to get started would be with an Enterprise Ontology. If you’d like to get an idea, for your budgeting purposes, what this might entail, click here, and you’ll go through a process where we help you clarify a scope such that we can estimate from it. Don’t worry about being descended on by some over eager sales types, we don’t have any sales people. We recognize that these things have their own timetables and we will be answering questions and helping you decide what to do next. We recognize that these days “selling” is far less effective than helping clients do their own research and supporting your buying process.
That said, there are three pretty predictable next steps:
- Ask us to outline what it would cost to build an Enterprise Ontology for your organization (you’d be surprised it is far less than the effort to build and Enterprise Data Model or equivalent)
- gist – as a byproduct of our work with many Enterprise Ontologies over the last decade we have built and made publicly available “gist” which is an upper ontology for business systems. We use it in all our work and we have made it publicly available via a Create Commons Share Alike license (you can use it for any purpose provided you acknowledge where you got it)
- Training – if you’d like to learn more about the language and technology behind this (either through public courses or in house) check out of offerings in training.
How is Semantic Technology different from Artificial Intelligence?
Artificial Intelligence (AI) is a 50+ year old academic discipline that provided many technologies that are now in commercial use. Two things comprise the core of semantic technology. The first stems from AI research in knowledge representation and reasoning done in the 70s and 80s and includes ontology representation languages such as OWL and inference engines like Fact++. The second relates to data representation and querying using triple stores, RDF and SPARQL, which are largely unrelated to AI. A broad definition of semantic technology includes a variety of other technologies that emerged from AI. These include machine learning, natural language processing, intelligent agents and to a lesser extent speech recognition and planning. Areas of AI not usually associated with semantic technology include creativity, vision and robotics.
How Does Semantics Use Inference to Build Knowledge?
Semantics organizes data into well-defined categories with clearly defined relationships. Classifying information in this way enables humans and machines to read, understand and infer knowledge based on its classification. For example, if we see a red breasted bird outside our window in April, our general knowledge leads us to identify it as a robin. Once it is properly categorized, we can infer a lot more information about the robin then just its name.
We know for example that it is a bird; it flies; it sings a song; it spends its winter somewhere else and the fact that it has showed up means that good weather is on its way.
We know this other information because the robin has been correctly identified within the schematic of our general knowledge about birds, a higher classification; seasons, a related classification, etc.
This is a simple example of by correctly classifying information into a predefined structure we can infer new knowledge. In a semantic model, once the relationships are set up, a computer can classify data appropriately, analyze it based on the predetermined relationships and then infer new knowledge based on this analysis.
What is Semantic Agreement?
The primary challenge in building an ontology is getting people to agree about what they really mean when they describe the concepts that define their business. Gaining semantic agreement is the process of helping people understand exactly what they mean when they express themselves.
Semantic technologists accomplish this because they define terms and relationships independent from the context of how they are applied or the IT systems that store the information, so they can build pure and consistent definitions across disciplines.
Why is Semantic Agreement Important?
Semantic agreement is important because it is enables disparate computer systems to communicate directly with each other. If one application defines a customer as someone who has placed an order and another application defines the customer as someone who might place an order, then the two applications cannot pass information back and forth because they are talking about two different people. In a traditional IT approach, the only way the two applications will be able to pass information back and forth is through a systems integration patch. Building these patches costs time and money because it requires the owners of the two systems need to negotiate a common meaning and write incremental code to ensure that the information is passed back and forth correctly. In a semantic enabled IT environment, all the concepts that mean the same thing are defined by a common meaning, so the different applications are able to communicate with each other without having to write systems integration code.
What is the Difference Between a Taxonomy and Ontology?
A taxonomy is a set of definitions that are organized by a hierarchy that starts at the most general description of something and gets more defined and specific as you go down the hierarchy of terms. For example, a red-tailed hawk could be represented in a common language taxonomy as follows:
- Red Tailed Hawk
An ontology describes a concept both by its position in a hierarchy of common factors like the above description of the red-tailed hawk but also by its relationships to other concepts. For example, the red-tailed hawk would also be associated with the concept of predators or animals that live in trees.
The richness of the relationships described in an ontology is what makes it a powerful tool for modeling complex business ecosystems.
What is the Difference Between a Logical Data Model and Ontology?
The purpose of an ontology is to model the business. It is independent from the computer systems, e.g. legacy or future applications and databases. Its purpose is to use formal logic and common terms to describe the business, in a way that both humans and machines can understand. Ontologies use OWL axioms to describe classes and properties that are shared across multiple lines of business so concepts can be defined by their relationships, making them extensible to increasing levels of detail as required. Good ontologies are ‘fractal’ in nature, meaning that the common abstractions create an organizing structure that easily expands to accommodate the complex information management requirements of the business. The purpose of a logical model is to describe the structure of the data required for a particular application or service. Typically, a logical model shows all the entities, relationships and attributes required for a proposed application. It only includes data relevant to the particular application in question. Ideally logical models are derived from the ontology which ensures consistent meaning and naming across future information systems.
How can an Ontology Link Computer Systems Together?
Since an ontology is separate from any IT structure, it is not limited by the constraints required by specific software or hardware. The ontology exists as a common reference point for any IT system to access. Thanks to this independence, it can serve as a common ground for different:
- database structures, such as relational and hierarchical,
- applications, such as an SAP ERP system and a cloud-hosted e-market,
- devices, such as an iPad or cell phone.
The benefit of the semantic approach is that you can link the legacy IT systems that are the backbone of most business to exciting new IT solutions, like cloud computing and mobile delivery.
What are 5 Business Benefits of Semantic Technology Solutions?
Semantic technology helps us:
- Find more relevant and useful information
- Because it enables us to search information from disparate sources (federated search) and automatically refine our searches (faceted search).
- Better understand what is happening
- Because it enables us to use the relationships between concepts to predict and interpret change.
- Build more transparent systems and communications
- Because it is based on common meanings and mutual understanding of the key concepts and relationships that govern our business ecosystems.
- Increase our effectiveness, efficiency and strategic advantage
- Because it enables us to make changes to our information systems more quickly and easily.
- Become more perceptive, intelligent and collaborative
- Because it enables us to ask questions we couldn’t ask before.
How Can Semantic Technology Enable Dynamic Workflow?
Semantic-driven dynamic workflow systems are a new way to organize, document and support knowledge management. They include two key things:
- A consistent, comprehensive and rigorous definition of an ecosystem that defines all its elements and the relationships between elements. It is like a map.
- A set of tools that use this model to:
- Gather and deliver ad hoc, relevant data.
- Generate a list of actions – tasks, decisions, communications, etc. – based on the current situation.
- Facilitate and document interactions in the ecosystem.
These tools work like a GPS system that uses the map to adjust its recommendations based on human interactions This new approach to workflow management enables organizations to respond faster, make better decisions and increase productivity.
Why Do Organizations Need Semantic-Driven, Dynamic Workflow Systems?
A business ecosystem is a series of interconnected systems that is constantly changing. People need flexible, accurate and timely information and tools to positively impact their ecosystems. Then they need to see how their actions impact the systems’ energy and flow. Semantic-driven, dynamic workflow systems enable users to access information from non-integrated sources, set up rules to monitor this information and initiate workflow procedures when the dynamics of the relationship between two concepts change. It also supports the definition or roles and responsibilities to ensure that this automated process is managed appropriately and securely. Organizational benefits to implementing semantic-driven, dynamic workflow systems include:
- Improved management of complexity
- Better access to accurate and timely information
- Improved insight and decision making
- Proactive management of risk and opportunity
- Increased organizational responsiveness to change
- Better understanding of the interlocking systems that influence the health of the business ecosystem