Tuesday, August 4, 2009
Before discussing the connection(s) between electronic health records (EHR) and semantic interoperability directly, I’d like to spend a little time talking about Semantic Web interoperability (not necessarily the same thing as the interoperability of present and future EHS systems).
In general, semantics is the study of meaning. Semantic Web (also called Web 3.0) technologies help separate meanings from data, document content, or application code, using technologies based on open standards. If a computer understands the semantics of a document, it doesn't just interpret the series of characters that make up that document: it understands the document's meaning. See my July 20 post for a brief introduction to this material.
Benefits of the Semantic Web to the World Wide Web
The World Wide Web is the biggest repository of information ever created, with growing contents in various languages and fields of knowledge. Search engines might help you find content containing specific words, but that content might not be exactly what you want. What is lacking? The search is based on the contents of pages and not the semantic meaning of the page's contents or information about the page.
Once the Semantic Web exists, it can provide the ability to tag all content on the Web, describe what each piece of information is about and give semantic meaning to the content item. Thus, search engines become more effective than they are now, and users can find the precise information they are hunting. Organizations that provide various services can tag those services with meaning (service-oriented architectures -- SOA -- are discussed in my June 20 post and mentioned again below, this time in the context of semantics); using Web-based software agents, you can dynamically find these services on the fly and use them to your benefit or in collaboration with other services (See http://www.oracle.com/technology/pub/articles/matjaz_bpel1.html for a discussion of the orchestration and choreography of Web services).
The use of words to refer to concepts (the meanings of the words used) is very sensitive to the context and the purpose of these words.
An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts. It may be used to reason about the properties of that domain, and may be used to define the domain. The word “reason” is an important part of this story and will come up again in a later post, when I talk about Protégé, a free, open source ontology editor and knowledge-base framework.
A domain ontology (or domain-specific ontology) models a specific domain, or part of the world (e.g., healthcare, banking or politics). It represents the particular meanings of terms as they apply to that domain. For example the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "video card" meaning.
For each domain of human knowledge, an ontology must be constructed, partly by hand and partly with the aid of dialog-driven ontology construction tools (to be discussed in an upcoming post).
Ontologies are not knowledge nor are they information. They are meta-information. In other words, ontologies are information about information. In the context of the Semantic Web, they encode, using an ontology language (to be discussed in an upcoming post), the relationships between the various terms within the information. Those relationships, which may be thought of as the axioms (basic assumptions), together with the rules governing the inference process, both enable as well as constrain the interpretation (and well-formed use) of those terms by the Info Agents (to be discussed in an upcoming post) to reason new conclusions based on existing information, i.e. to think. In other words, theorems (formal deductive propositions that are provable based on the axioms and the rules of inference) may be generated by the software, thus allowing formal deductive reasoning at the machine level. And given that an ontology, as described here, is a statement of Logic Theory, two or more independent Info Agents processing the same domain-specific ontology will be able to collaborate and deduce an answer to a query, without being driven by the same software.
When an organization adapts an ontology-driven approach, it can capture and represent its total knowledge in a language-neutral form and deploy the knowledge in a central repository that provides the same semantic meaning across applications.
Semantics are the future of service-oriented integration
To properly model and manage a service-oriented architecture (SOA), enterprise architects must maintain active representations of the services available to the enterprise. Specifically, to discover and organize their services, the architects must use best practices that model and assemble services using metadata, encapsulate business logic in metadata for dynamic binding, and manage with metadata. Ontologies provide a very powerful and flexible way to aggregate, visualize, and normalize this service metadata layer.
Note: When delivering services as part of a service inventory, there is a constant risk that services will be created with overlapping functional boundaries, making it difficult to enable wide-spread reuse. Normalization addresses this problem. When services are delivered with complementary and well-aligned boundaries, normalization across the inventory is attained. Note also how the quantity of required services is reduced.
Semantic technologies provide an abstraction layer above existing IT technologies, one that enables the bridging and interconnection of data, content, and processes across business and IT silos.
For advanced IT readers
For a rather technical discussions of this topic, see the video Artificial Neural Network based Techniques for Semantic Data Interoperability below and parts of my article Using Neural Networks and OLAP Tools to Make Business Decisions to which there is a link in the bibliography at the very bottom of this blog.
To be continued …