Why Data Professionals Should Pay Attention to Semantic Technologies

by John Biderman

I will be attending the 2011 Semantic Technology Conference (“SemTech”) in June on my own “vacation” time in order to further my knowledge of what is going on in this very interesting and exciting arena.  I went to this conference in 2006 when it was only a few years old and had about 800 attendees.  This year there will be around 1,200, I’m told, and it is particularly interesting to see the greatly increased number of case studies and real-world applications as compared to five years ago, when the event was more academic and futuristic – more about the promise than the reality.  The reality is here: semantic technologies are burgeoning.  It is my belief that all data professionals (modelers, metadata managers, taxonomists, data governance practitioners, information architects, etc.) should stay abreast of these developments and get schooled in the technologies, not just for career development purposes but because successful development of semantically aware systems depends on the disciplines of information analysis and modeling that we’ve been applying in the relational world for decades.  In other words, the semantic world needs us.

Semantics, of course, have been implicit in information systems since the very first columnar data set was instantiated – made somewhat more explicit when column headings were introduced, and even more explicit when relationships, cardinalities and constraints were declared in RDBMS metadata.  All these things wrapped some real-world meaning around data, but nothing like the formal representation of knowledge domains that happens in ontologies.  I am an avowed proponent of enterprise data models (see previous post) for a host of beneficial reasons, but I also believe that enterprise ontologies will become more important for representing the interrelationships and inherent polyhierarchies of enterprise information.  In fact, if the ontology is granular enough, the data model can be derived from the ontology.  (This is precisely the methodology the firm Semantic Arts inventively follows for designing Service Oriented Architectures: the atomic information that supports a business process is modeled in an ontology, and they extract the XML structures for SOA canonical messages from that model.)

The realm of semantic applications is going to be increasingly extensive, for example in data-integration programs, rule-based systems, analytics (think inferencing for BI).  Semantically aware search capability will change the nature of information retrieval in organizations.  We will have ontologies sitting on top of relational databases, to allow semantic search to operate on both structured and unstructured information – in other words, to query by meaning rather than by value, and to do so across enterprise information assets.

Semantic technologies are all about the metadata, which in turn is all about vocabulary, relationships, and meaning.  Thus the clear relevance of the data professional’s craft in the world of semantic systems.  A whole organization concerned with Semantic Data Management has evolved in the last year. A very interesting article by Brian Schulte describes how semantic modeling will have an important role in Master Data Management.

In other words, folks, this stuff is real and it will be coming on like gangbusters over the next several years.  If it is mostly new to you, an excellent introduction is Dave McComb’s highly readable book, Semantics in Business Systems (2003, Morgan Kaufmann). (The publisher put it out in their “Savvy Manager’s Guide” series, but don’t let that throw you off.)  It traces semantics from the early RDBMS days right through to the Semantic Web.  For a more advanced look at the underlying information modeling technologies, check out Semantic Web for the Working Ontologist by Dean Allemang and James Hendler (2008, Morgan Kaufmann).  For a purely layman’s prognostication about where the Semantic Web will be taking business and commerce in the next decades, read Pull: The Power of the Semantic Web to Transform Your Business by David Siegel (2009, Portfolio Hardcover).

Better still, consider taking a class in ontology modeling and/or RDF data stores.  If you have experience or familiarity with Object Role Modeling (ORM), the move to ontologies will not be that great a leap – it involves the same declarative subject-predicate-object triples.

My message is that we should all get and stay educated about semantic technology.  It is going to change our careers while leveraging our fundamental disciplines.

I will be attending the 2011 Semantic Technology Conference (“SemTech”) in June on my own 

“vacation” time in order to further my knowledge of what is going on in this very interesting and

exciting arena.  I went to this conference in 2006 when it was only a few years old and had about

800 attendees.  This year there will be around 1,200, I’m told, and it is particularly interesting

to see the greatly increased number of case studies and real-world applications as compared to five

years ago, when the event was more academic and futuristic – more about the promise than the

reality.  The reality is here: semantic technologies are burgeoning.  It is my belief that all data

professionals (modelers, metadata managers, taxonomists, data governance practitioners, information

architects, etc.) should stay abreast of these developments and get schooled in the technologies,

not just for career development purposes but because successful development of semantically aware

systems depends on the disciplines of information analysis and modeling that we’ve been applying in

the relational world for decades.  In other words, the semantic world needs us.

Semantics, of course, have been implicit in information systems since the very first columnar data

set was instantiated – made somewhat more explicit when column headings were introduced, and even

more explicit when relationships, cardinalities and constraints were declared in RDBMS metadata.

All these things wrapped some real-world meaning around data, but nothing like the formal

representation of knowledge domains that happens in ontologies.  I am an avowed proponent of

enterprise data models (see previous post) for a host of beneficial reasons, but I also believe

that enterprise ontologies will become more important for representing the interrelationships and

inherent polyhierarchies of enterprise information.  In fact, if the ontology is granular enough,

the data model can be derived from the ontology.  (This is precisely the methodology the firm

Semantic Arts inventively follows for designing Service Oriented Architectures: the atomic

information that supports a business process is modeled in an ontology, and they extract the XML

structures for SOA canonical messages from that model.)

The realm of semantic applications is going to be increasingly extensive, for example in data

integration programs, rule-based systems, analytics (think inferencing for BI).  Semantically aware

search capability will change the nature of information retrieval in organizations.  We will have

ontologies sitting on top of relational databases, to allow semantic search to operate on both

structured and unstructured information – in other words, to query by meaning rather than by value,

and to do so across enterprise information assets.

Semantic technologies are all about the metadata, which in turn is all about vocabulary,

relationships, and meaning.  Thus the clear relevance of the data professional’s craft in the world

of semantic systems.  A whole organization concerned with Semantic Data Management has evolved in

the last year. A very interesting article by Brian Schulte describes how semantic modeling will

have an important role in Master Data Management.

In other words, folks, this stuff is real and it will be coming on like gangbusters over the next

several years.  If it is mostly new to you, an excellent introduction is Dave McComb’s highly

readable book, Semantics in Business Systems (2003, Morgan Kaufmann). (The publisher put it out in

their “Savvy Manager’s Guide” series, but don’t let that throw you off.)  It traces semantics from

the early RDBMS days right through to the Semantic Web.  For a more advanced look at the underlying

information modeling technologies, check out Semantic Web for the Working Ontologist by Dean

Allemang and James Hendler (2008, Morgan Kaufmann).  For a purely layman’s prognostication about

where the Semantic Web will be taking business and commerce in the next decades, read Pull: The

Power of the Semantic Web to Transform Your Business by David Siegel (2009, Portfolio Hardcover).

Better still, consider taking a class in ontology modeling and/or RDF data stores.  If you have

experience or familiarity with Object Relationship Modeling, the move to ontologies will not be

that great a leap – it involves the same declarative subject-predicate-object triples.

My message is that we should all get and stay educated about semantic technology.  It is going to

change our careers while leveraging our fundamental disciplines.

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John Biderman

John Biderman has over 20 years of experience in application development, database modeling, systems integration, and enterprise information architecture. He has consulted to Fortune 500 clients in the US, UK, and Asia. At Harvard Pilgrim Health Care (a New England-based not-for-profit health plan) he works in the areas of data architecture standards and policies, data integration, logical data modeling, enterprise SOA message architecture, metadata capture, data quality interventions, engaging the business in data stewardship processes, and project leadership. 

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