IBM's Watson and Nuance's Dragon Medical

Earlier this week, after the Watson computer defeated human champions in a three-day match on the popular TV show Jeopardy, its creators and a Massachusetts software company prepared to sell a medical version of the smart machine.

IBM has an agreement with Nuance Communications Inc. of Burlington to sell Watson-based products to health care providers. Nuance already offers voice-recognition software for a variety of applications. The companies are pitching forthcoming technology that will, among other things, allow medical providers to describe symptoms orally and get diagnostic information in return.

Watson technology will enable Nuance to add artificial intelligence to its offerings. And that's where this blog comes in. I've been talking about these two topics, albeit separately, for some time now.

Watson's ability to analyze the meaning and context of human language, and quickly process information to find precise answers can assist decision makers, such as physicians and nurses, unlock important knowledge and facts buried within huge volumes of information, and offer answers they may not have considered to help validate their own ideas or hypotheses.

For example, a doctor considering a patient’s diagnosis could use Watson’s analytics technology, in conjunction with Nuance’s voice and clinical language understanding solutions, to rapidly consider all the related texts, reference materials, prior cases, and latest knowledge in journals and medical literature to gain evidence from many more potential sources than previously possible, thereby helping the medical professional to confidently determine the most likely diagnosis and treatment options.

For more information about the Watson computing system and the Jeopardy! challenge, click here.

On earlier posts to this blog, I discussed the semantic Web, ontologies, and speech recognition software from Nuance (and Adobe):

See, for example, my November 7, 2009 post "Vagueness, Logic and Ontology: Fuzzy Ontologies"

In traditional ontology theory, concepts and roles are crisp sets. However, there is a great deal of fuzziness in the real world.

For example, one may be interested in finding “a very strong flavored red wine” or in reasoning with concepts such as “a cold place”, “an expensive item”, “a fast motorcycle”, etc.

A possible solution to handling uncertain data is to incorporate fuzzy logic into ontologies. Unfortunately, these fuzzy ontologies have shortcomings – reasoners for fuzzy ontologies are not yet so polished as those for crisp (aka traditional) ontologies.

See, for example, my May 30, 2009 post on "Speech Recognition Software"

The trivial example of one-to-one translation given there can be extended to one-to-many translation: That is, in a matter of seconds, you could “program” Dragon Medical to type out a whole sentence in response to your speaking just a single word or code into the microphone. And, vice versa: you could “program” Dragon Medical to type out just a single word or code in response to your speaking a whole sentence into the microphone.

Does the Semantic Web need Ontologies?

The answer to the question "Does the Semantic Web need Ontologies?" is "yes" according to most but "no" according to some. For a discussion of this question, click here for the view of one individual who is convinced that ontologies are a luxury, not a necessity, plus the comments of others.

Ontology-Based Software Application Development -- Java and .NET

Consider the following scenario: A programmer needs to read data from a database via the JDBC interface. The system administrator of the organization provides user name and password, which obviously need to be used in the process. Then, the programmer

1. Searches the entire API for a method call (or calls), which takes a database user name as an input parameter.

2. Has to understand how various API calls should be sequenced in order to go from the connection information all the way to actually receiving data from the database.

If the APIs are not semantically rich (i.e., they contain only syntactic information, which the programmers have to read and interpret), understanding, learning and using an API can be a very time consuming task.

For a discussion of how the application of ideas from the areas of "Knowledge Management" and "Knowledge Representation" -- The enrichment of purely syntactic information of APIs with semantic information -- will allow the computer to perform certain tasks that normally the human programmer has to perform, see

http://www.aifb.uni-karlsruhe.de/WBS/aeb/smartapi/smartapi.pdf

A similar semantification of Web services (Ontology-enabled Services) is being widely discussed and implemented today.




See, for example,

http://www.cs.vu.nl/~maksym/pap/Onto-SOA-WAI.pdf

and

http://www.computer.org/portal/web/csdl/doi/10.1109/AICT-ICIW.2006.141

A number of my earlier post have been about Protégé , the popular ontology development tool, and OWL, one of the main ontology languages. To continue that discussion, see

http://www.sandsoft.com/edoc2004/KnublauchMDSW2004.pdf

which discusses a realistic application scenario -- some initial thoughts on a software architecture and a development methodology for Web services and agents for the Semantic Web. Their architecture is driven by formal domain models (ontologies).

Central to their design is Jena, a Java framework for building Semantic Web applications. It provides a programmatic environment for RDF, RDFS and OWL, SPARQL and includes a rule-based inference engine.

Jena is open source and grown out of work with the HP Labs Semantic Web Programme.

For more on Jena, see

http://jena.sourceforge.net/documentation.html

Jena is a programming toolkit that uses the Java programming language. While there are a few command-line tools to help you perform some key tasks using Jena, mostly you use Jena by writing Java programs.

But, .NET developers have similar resources. See, for example

http://www.ic.uff.br/~esteban/files/sbgames09_Alex.pdf

for a development environment using Microsoft Visual Studio, the base language C#, and the graphical library XNA. Protégé has been used for designing the ontology, and the application uses the OwlDotNetApi library.

This 2009 work demonstrates a step-by-step implementation, from the definition of an ontological knowledge base to the implementation of the main classes of a strategy game. It aims at serving as a basic reference for developers interested in starting .NET development of ontology-based applications.

Vagueness, Logic, and Ontology: Fuzzy Ontologies

In traditional ontology theory, concepts and roles are crisp sets. However, there is a great deal of fuzziness in the real world.

For example, one may be interested in finding “a very strong flavored red wine” or in reasoning with concepts such as “a cold place”, “an expensive item”, “a fast motorcycle”, etc.

A possible solution to handling uncertain data is to incorporate fuzzy logic into ontologies. Unfortunately, these fuzzy ontologies have shortcomings – reasoners for fuzzy ontologies are not yet so polished as those for crisp (aka traditional) ontologies.

Possible use of a fuzzy ontology

When performing a query on a document, it is a usual practice to extend the set of concepts already present in the query with other ones which can be derived from an ontology. Typically, given a concept, its parents and children can also be added to the query and then searched in the document.

Extending queries

A possible use of fuzzy ontology is to extend queries with, besides children and parents, instances of concepts which satisfy to a certain degree the query. Here’s an example. You are given a clothes ontology and a query looking for “a very long and black coat.” In the ontology there are two instances of coat: X which has property “long” with value 0.7 and Y which has property “long” with value 0.3. Thus, it is natural to extend the original query adding, not only parents and children of the concept “coat”, but also the instance X, because \long = 0.7 can be interpreted as “very long”. On the other hand, the instance Y is not added to the extended query since \long = 0.3 does not mean “very long”.

Mathematical representation of a fuzzy concept

The fuzzy concept “Young_Person” is defined as follow:



The linguistic term Young may be defined by a trapezoidal function as shown graphically in the next figure, its mathematical representation.



{click on the image above for larger view}


Representation of a fuzzy ontology in Protégé




Fuzzy Protégé for Fuzzy Ontology Models

A good deal of work has been conducted to build tools for the creation of fuzzy ontologies.

Fuzzy Protégé is a semi-automatic collaborative tool for the construction of fuzzy ontology models, built as a Protégé 3.3.1 tab plug-in. For more information on this plug-in, click the following link.

http://protege.stanford.edu/conference/2009/abstracts/S10P2Ghorbel.pdf





Fuzzy OWL 2

The prior post to this blog introduced Web Ontology Language 2 (OWL 2), a new version of a standard for representing knowledge on the Web that had been announced by W3C just that day.

Fuzzy OWL2 Ontology is an OWL ontology to represent fuzzy extensions of the OWL and OWL 2 languages. For more information on this subject, click the following link.

http://webdiis.unizar.es/~fbobillo/papers/ISMIS2009presentation.pdf


Vagueness, Logic, and Ontology

Some people are clearly bald (Picasso), some are clearly hairy (the count of Montecristo), and some are borderline cases. Achille C. Varzi, Department of Philosophy, Columbia University, New York, starts here and presents a very interesting discussion on Vagueness, Logic, and Ontology in an easy-to-read paper reached by clicking the following link.

http://www.columbia.edu/~av72/papers/Dialogue_2001.pdf


Studies in Fuzziness and Soft Computing

http://www.springer.com/engineering/book/978-3-540-71257-2?cm_mmc=Google-_-Book%20Search-_-Springer-_-0

Electronic medical records systems are not classified as medical devices -- This may have serious consequences.

This is an interim post. The promised post on ontologies that benefit from fuzzy or probability-based logic is coming.

"We wouldn't want to go back, but Electronic Health Records (EHR) are still in need of significant improvement."

-Christine Sinsky, an internist in Dubuque, Iowa, whose practice implemented electronic records six years ago.

More than one in five hospital medication errors reported last year -- 27,969 out of 133,662 -- were caused at least partly by computers, according to data submitted by 379 hospitals to Quantros Inc., a health-care information company. Paper-based errors have caused 10,954 errors, the data showed.

Between 2006 and 2008, computer errors also contributed to 31 deaths or serious injuries -- twice as many as were caused by paper errors, although numbers of these serious cases were decreasing, Quantros said.

Legal experts say it is impossible to know how often health IT mishaps occur. Electronic medical records are not classified as medical devices, so hospitals are not required to report problems. In fact, many health IT contracts do not allow hospitals to discuss computer flaws, according to Sharona Hoffman, a professor of law and bioethics at Case Western Reserve University in Cleveland.

"Doctors who report problems can lose their jobs," Hoffman said. "Hospitals don't have any incentive to do so and may be in breach of contract if they do. That sort of secrecy puts the patient at risk."

Click here to see the complete Washington Post article

"Bernstein at Harvard" -- Segue to Probability, Semantic Amiguity, etc.

My prior post was about Watson, a tool for accessing multiple and usually heterogeneous online ontologies, but never once mentioned the word "probability." However, the recognition of uncertainty in the real world is sometimes needed within description logics, the underpinning of ontologies like OWL. Because of its importance, I'll take up the topic of uncertain domain knowledge in my next post.

For the present, however, I've embed here the 4 1/2 minute video "Bernstein at Harvard," which includes many of the terms - e.g., probability, meaning, semantic ambiguity, meta, language, and knowledge - that I'll use in my upcoming rant. Hope you find this segue as relevant as I do.





Note: Semantic ambiguity arises when a word or concept has an inherently diffuse meaning based on widespread or informal usage. This is often the case, for example, with idiomatic expressions whose definitions are rarely or never well-defined, and are presented in the context of a larger argument that invites a conclusion.

For example, “You could do with a new automobile. How about a test drive?” The clause “You could do with” presents a statement with such wide possible interpretation as to be essentially meaningless. Lexical ambiguity is contrasted with semantic ambiguity. The former represents a choice between a finite number of known and meaningful context-dependent interpretations. The latter represents a choice between any number of possible interpretations, none of which may have a standard agreed-upon meaning. This form of ambiguity is closely related to vagueness.

Querying Semantic Data & Ontology - Assisted Querying of Relational Data --- SQL

My September 11 post discussed the i2b2 suite of applications, which has at its base a collection of database tables – with a star schema format - developed from the ground up to represent ontologies. In the present post, I’ll continue this discussion, only for the case where external ontologies are used. I’ll illustrate this latter option with two examples: querying semantic data & ontology-assisted querying of relational data, both using SQL.

Some organizations are using semantic approaches to create an information model (the ontology) based on data schema taken from a particular organization or industry. Individual application database schema are mapped to a standard information model in order to make the meaning of the concepts in different, application-specific data schema explicit and relate them to each other. The resulting information architecture provides a unified view of the data sources in the organization.

As shown in the figure below, application users can query these semantic (metadata) models, which comprise RDF data or ontologies. Standard ontologies reconcile queries needing access to heterogeneous data sources and application-specific schema. This results in solutions that have the power to address problems such as:

* data integration across a heterogeneous, expanding set of sources,
* racking provenance information, and
* modeling probabilistic data and schema.

The product focused on in this post – chosen in part by the toss of a coin – is the latest database from Oracle, 11g, and not competitors like SQL Server. Oracle, it should be mentioned, can deploy on any server platform (Unix, Linux, or Windows) whereas Microsoft SQL Server can deploy only on Windows Server.

In Oracle 11g, RDF triples based on a graph data model are persistent, indexed, and queried, similar to other object-relational data types. I’ll have more to say on RDF/OWL data and ontologies in future posts. For now, the links found earlier in this paragraph serve as an introduction.

As shown in this figure, the Oracle 11g database contains semantic data and ontologies (RDF/OWL models), as well as traditional relational data.

The Oracle Database 11g semantic database features enable:

* Storage, Loading, and DML access to RDF/OWL data and ontologies
* Inference using OWL and RDFS semantics and also user-defined rules
* Querying of RDF/OWL data and ontologies using SPARQL-like graph patterns
* Ontology-assisted querying of enterprise (relational) data

Query Semantic Data in Oracle Database

RDF/OWL data can be queried using SQL. The Oracle SEM_MATCH table function, which can be embedded in a SQL query, has the ability to search for an arbitrary pattern against the RDF/OWL models, and optionally, data inferred using RDFS, OWL, and user-defined rules. The SEM_MATCH function meets most of the requirements identified by W3C SPARQL standard for graph queries. Support for virtual models, a view-like feature for combining models and optionally corresponding entailments from a UNION or UNION ALL operation, can be used in a SEM_MATCH query. New in release 11.2 of the Oracle database, the SPARQL FILTER, UNION, and OPTIONAL keywords are supported in the SEM_MATCH table function.

{click on the image above for larger view}

Ontology-assisted Query for Relational Data

Queries can extract more semantically complete results from relational data by associating relational data with ontologies that organize the domain knowledge of the relational data.

As shown in the next example, Oracle 11g performs this task by associating an ontology with the data and using the new SEM_RELATED operator (and optionally its SEM_DISTANCE ancillary operator). The new SEM_INDEXTYPE index type improves performance for semantic queries.

{click on the image above for larger view}

For an in-depth treatment of the SEM_MATCH table function, the SEM_RELATED operator, and related topics, consult the Oracle Database Semantic Technologies Developer's Guide.

Native Inferencing using OWL, RDFS, and user-defined rules

In addition to simply storing, and querying an ontology, the latest Oracle database can perform a number of other important tasks, including but not limited to drawing inferences and reasoning. The ability to draw inferences from existing data using the precision and rigor of mathematical logic (e.g., Description Logic) is probably the most important property that distinguishes semantic data from others. New Oracle Database 11g enhancements include a native inference engine for efficient and scalable inferencing using major subsets of OWL. This OWL inferencing engine makes the existing native inferencing for RDF, RDFS, and user-defined rules (used for additional specialized inferencing capabilities) more efficient and scalable. Inferencing may also be done using any combinations of these various entailment regimes. In addition, through the Oracle Jena Adaptor (downloadable from the Oracle Semantic Technologies page), you can integrate with external reasoners such as Pellet (see my August 24 post below for an introduction to Pellet).

Semantic Interoperability -- Part III Ontologies -- Prelude to Electronic Health Records (EHR)

This post is a continuation of the introduction to ontologies that I posted on August 4 and July 20.

Readers new to this subject might also find the 2 ½ minute video What Is Web 3.0, Anyway? worthwhile.

An ontology is an explicit specification of a conceptualization (defined earlier), that is to say, a formal representation of a knowledge domain. Usually an ontology consists of: (i) classes, which represent the concepts of the domain (for example, in an ontology about the domain of Telecommunications, as in the listing below, a possible concept could be "Phone"); (ii) properties, to establish relationships between the concepts (for example, a "Phone" concept could have as property the "Company"; (iii) instances, with concrete examples associated with every concept (for example, "Siemens" could be an instance of the "Company” concept); and (iv) axioms, which are restrictions applicable to certain elements of the ontology, necessary to specify completely the knowledge domain (for example, in the ontology about telecommunications, it could define a restriction to indicate that in this domain a "Phone" must have always, at least, a "Company").

Ontologies can be stored using XML-based markup languages such as OWL (Ontology Web Language), which facilitates their reuse in different semantic platforms to annotate and search resources. These languages allow us to define tags in order to represent the different ontology elements. The listing below shows an extract of a OWL file containing the Telecommunications example ontology that has been created using the Protegé tool. As you can observe, in this language, the concepts are delimited by the Class tag, the properties by the ObjectProperty tag, the instances by the tag corresponding to the associate class (in the example, the class Company has as instance "Siemens"), and the axioms with tags like Restriction or subClassOf (this one is used in the example for representing that "Cellphone" is a type of "Phone").



Content of a OWL file - a fragment of an
Ontology about Telecommunications
{click to enlarge}

Today one of the main uses of ontologies is to support the Semantic Web (aka Web 3.0), especially for annotating Web resources and facilitating the localization of these annotated resources when users formulate queries to semantic search engines. For this purpose, in the previous example of Telecommunications, an ontology has included two annotations as instances of the “Phone” and “Cellphone” classes which correspond to two documents (“Gigaset3015Classic.pdf” and “MobileC55.pdf”, respectively) located in a hypothetical Web server (“http://www.telecosiemens.com”).

The Reality

Researchers have written much about the potential benefits of using ontologies, and most of us regard them as central building blocks of the Semantic Web and other semantic systems. Unfortunately, the number and quality of actual, “non-toy” ontologies available on the Web today is remarkably low. This implies that the Semantic Web community has yet to build practically useful ontologies for a lot of relevant domains in order to make the Semantic Web a reality.

In striking contrast to the data within a stand-alone document, publications have yet to benefit from the opportunities offered by cyber infrastructure. While the means of distributing publications has vastly improved, publishers have done little else to capitalize on the electronic medium. In particular, semantic information describing the content of these publications is generally sorely lacking, as is the integration of this information with data in public repositories.

The Reasoner

One of the key features of ontologies is that they can be processed by a reasoner. One of the main services offered by a reasoner is to test whether or not one class is a subclass of another class. By performing such tests on all of the classes in an ontology it is possible for a reasoner to compute the inferred ontology class hierarchy. Another standard service that is offered by reasoners is consistency checking. Based on the description (conditions) of a class, the reasoner can check whether or not it is possible for the class to have any instances. A class is deemed to be inconsistent if it cannot possibly have any instances.

Reasoning with Protégé 4.0

Reasoning with your ontology is one of the most commonly performed activities and the ontology editor Protege 4.0 comes with 2 built-in reasoners, FaCT++ and Pellet. To classify your ontology, open the Reasoner menu and select one of the available reasoners. FaCT++ will automatically classify your ontology. Pellet requires that you select classify. Once you have done this, the class hierarchy on the Entites tab changes to show the inferred class hierarchy. Unsatisfiable classes appear in red under Nothing, and everything else appears in the hierarchy under their inferred superclasses. The asserted class hierarchy is still available, stacked under the asserted one, as shown in the next screenshot.



Screenshot of an inferred class hierarchy
{click to enlarge}

Instructions for getting started with the OWL editor in Protege 4:
http://protegewiki.stanford.edu/index.php/Protege4GettingStarted

A Practical Guide To Building OWL Ontologies:
http://www.co-ode.org/resources/tutorials/ProtegeOWLTutorial-p4.0.pdf

The Microsoft Word Add-in For Ontology Recognition – An Introduction

There are other tools (e.g., The Microsoft Word Add-in For Ontology Recognition to name one) that might be suitable for your needs. It’s a MS Word 2007 add-in that enables the annotation of Word documents based on terms that appear in ontologies.

This Word Add-in For Ontology Recognition is a free Microsoft download. With it, as shown in the figures below, you select and then download one or more ontologies which are thereafter available automatically from within your Word document.

The Microsoft Word Add-in For Ontology Recognition – An Overview

This add-in enables authors who use Microsoft Word for content creation to incorporate semantic knowledge into the content. This add-in should simplify the development and validation of ontologies, by making ontologies more accessible to a wide audience of authors and by enabling semantic content to be integrated in the authoring experience, capturing the author’s intent and knowledge at the source, and facilitating downstream discoverability.

The goal of the add-in is to assist authors in writing a manuscript that is easily integrated with existing and pending electronic resources. The major aims of this project are to add semantic information as XML mark-up to the manuscript using ontologies and controlled vocabularies (from the National Center for Biomedical Ontology) and identifiers from major biological databases, and to integrate manuscript content with existing public data repositories.

As part of the publishing workflow and archiving process, the terms added by the add-in, providing the semantic information, can be extracted from Word files, as they are stored as custom XML tags as part of the content. The semantic knowledge can then be preserved as the documented is converted to other formats, such as HTML or the XML format from the National Library of Medicine, which is commonly used for archiving.

The full benefit of semantic-rich content will result from an end-to-end approach to the preservation of semantics and metadata through the publishing pipeline, starting with capturing knowledge from the subject experts, the authors, and enabling this knowledge to be preserved when published, as well as made available to search engines and presented to people consuming the content.

The Microsoft Word Add-in For Ontology Recognition – Screen Shots

{click to enlarge}

The Word Add-in For Ontology Recognition User’s Guide for the Semantic Mark-up and XML Formatting of Scholarly Articles is a good place to start for further information on this tool.

Semantic Tagging

When a word or set of words is tagged by the add-in, the word is wrapped with some tags that associate it with the ontology term. The example below shows the word "disease" being tagged with Human Disease ontology.

{click to enlarge}

If the Word file (docx) is to be transformed to other formats, this set of tags would need to be processed using xslt or other technologies. Note that there are other CodePlex projects available which implement transformations of docx files to other formats, which one can start from.

Ontology Add-in for Microsoft Office Word 2007 Video

Electronic Health Records (EHR) – Semantic Interoperability – Part 2

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).

Ontologies

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 …

Electronic Health Records (EHR) - Semantic Interoperability - Part 1

To press a suit means one thing to a tailor and another thing to a lawyer.

A free radical means one thing to a chemist but meant another thing to members of the House Un-American Activities Committee (HUAC) during the 1950’s.

And, medication for pain and pain medication don’t always mean the same thing. The controversy surrounding the recent death of Michael Jackson illustrates this last point.

In the examples above, as in clinical terminology, words can take on different meanings depending on factors like time or place (i.e., context).

Furthermore, clinicians and organizations use different clinical terms that mean the same thing. For example, the terms heart attack, myocardial infarction, and MI may mean the same thing to a cardiologist, but, to a computer, they are all different. There is a need to exchange clinical information consistently between different health care providers, care settings, researchers and others (semantic interoperability), and because medical information is recorded differently from place to place (on paper or electronically), a comprehensive, unified medical terminology system is needed as part of the information infrastructure.

Interoperability

Interoperability is the ability of two parties, either human or machine, to exchange data or information.

First, syntactic interoperability guarantees the exchange of the structure of the data, but carries no assurance that the meaning will be interpreted identically by all parties. Web pages built with HTML or XML are good examples of machine-to-machine syntactic interoperability because a properly structured page can be read by any machine with a Web browser. The meaning of the page to a particular machine may vary substantially; however, this is not usually considered a problem because the semantics of a page are meant to be interpreted by human viewers.

Next, human or semantic interoperability guarantees that the meaning of a structure is unambiguously exchanged between humans. Documents such as progress notes, referrals, consults, and others achieve semantic interoperability at a clinician-to-clinician level by relying on common medical vocabularies.

Finally, computable semantic interoperability requires that the meaning of data be unambiguously exchanged from machine to machine (as shown in the figure below). This does not necessarily mean that all machines need to process the received data the same way, but rather that each machine will make its processing decisions based on the same meaning.



Words and Meanings

The meanings of words change, sometimes rapidly. But a formal language such as used in an ontology -- a rigorous and exhaustive organization of some knowledge domain that is usually hierarchical and contains all the relevant entities and their relations -- can encode the meanings (semantics) of concepts in a form that does not change. In order to determine what is the meaning of a particular word (or term in a database, for example), it is necessary to label each fixed concept representation in an ontology with the word(s) or term(s) that may refer to that concept.

When multiple words refer to the same (fixed) concept, in language this is called synonymy; when one word is used to refer to more than one concept, that is called ambiguity. Ambiguity and synonymy are among the factors that make computer understanding of language very difficult. The use of words to refer to concepts (the meanings of the words used) is very sensitive to the context and the purpose of any use for many human-readable terms.

The use of ontologies in supporting semantic interoperability is to provide a fixed set of concepts whose meanings and relations are stable and can be agreed to by users. When a word used in some interoperability context changes its meaning, then to preserve interoperability it is necessary to change the pointer to the ontology element(s) that specifies the meaning of that word.

There are a number of tools for the programmatic handling (i.e., creating, querying, etc.) of ontologies. The visual representation of ontologies is an important contribution of these tools.



IBM Integrated ontology Development Toolkit (formerly named IBM Semantics Toolkit) is one of many toolkits designed for storage, manipulation, query, and inference of ontologies and corresponding instances.

An upcoming post will discuss the role and value of semantic technology in service-oriented architectures (SOA).