Posted:October 24, 2011

UMBEL Vocabulary and Reference Concept OntologyNew Portal Update Leverages the Open Semantic Framework

UMBEL, the Upper Mapping and Binding Exchange Layer, is an upper ontology of about 28,000 reference concepts and a vocabulary designed for domain ontologies and ontology mapping [1]. When we first released UMBEL in mid-2008 it was accompanied by a number of Web services and a SPARQL endpoint, and general APIs. In fact, these were the first Web services developed for release by Structured Dynamics. They were the prototypes for what later became the structWSF Web services framework, which incorporated many lessons learned and better practices.

By the time that the structWSF framework had evolved with many additions to comprise the Open Semantic Framework (OSF), those original UMBEL Web services had become quite dated. Thus, upon the last major update to UMBEL to version 1.0 back in February of this year, we removed these dated services.

Like what I earlier mentioned about the cobbler’s children being the last to get new shoes, it has taken us a bit to upgrade the UMBEL services. However, I am pleased to announce we have now completed the transition of UMBEL’s earlier services to use the OSF framework, and specifically the structWSF platform-independent services. As a result, there are both upgraded existing services and some exciting new ones. We will now be using UMBEL as one of our showcases for these expanding OSF features. We will be elaborating upon these features throughout this series, some parts of which will appear on Fred Giasson’s blog.

In this first part, we provide a broad overview of the new UMBEL OSF implementation. We also begin to foretell some of the parts to come that will describe some of these features in more detail.

The Overall Portal

The new UMBEL portal is a fairly classic example of an OSF installation. The content management system hosting the system is Drupal, supplemented with a standard set of third-party modules and our own conStruct semantic technology modules. The theme is a stripped-down modification of the popular Pixture Reloaded theme:

Like other vocabulary sites, the UMBEL portal contains specifications and links to community resources and downloads. It also has some specialty links not shown on typical standards sites.

Much Better Vocabulary Access and Management

The site now most prominently features our structOntology editing and maintenance tool. Built on the OWL API, the same as Protégé 4, structOntology provides the advantage of enabling edits and management of ontologies directly within the applications in which they are used. This is far superior to needing to fire up an external ontology manager and then to re-import the changed ontology. structOntology also has an arguably simpler interface and operation than other ontology management alternatives:

For the UMBEL site, the standard view of using structOntology is read-only. In a subsequent part we will also discuss structOntology’s full editing and maintenance mode.

Improved Discovery and Navigation

Like all standard OSF installations, there are two superior means for discovery and navigation of the information space:  search and the relation browser.

Search uses the integration of RDF and inferencing with full-text, faceted search using Solr. This has been Structured Dynamics’ standard search function for some time, as Fred initially described in April 2009. It is a very effective way for finding new and related concepts within the UMBEL structure.

The relation browser is what is used for casual navigation and discovery. Any concept found via search or other means within the system can have the browser invoked by clicking on its browser icon []. When done, the standard relation browser appears:

The relation browser is highly configurable, as shown by some of our exemplar installations. Note in this case that the More details … link brings you to a detailed concept view, such as this example:

These various tools provide great means for discovery and navigation within the 28,000 concepts in the UMBEL reference space.

Newly Released Web Services and SPARQL Endpoints

We are also now providing updated endpoints for Ontology: Read, Search, Crud: Read, SPARQL and Scones. These will be described with access and query examples in a later part.

Some Cool New Sandboxes

We will also be discussing our OBIE (ontology-based information extraction) and entity tagger, scones, and export and ontology edit and management functions in subsequent posts.

Looking Ahead to Remaining Parts

We anticipate eight or nine more parts in this series explaining most of these options in greater detail. We hope to post a couple per week or so over the coming month. We will conclude with a discussion of next pending UMBEL releases.

UMBEL small logo

This is the first of a multi-part series on the newly updated UMBEL services. Other articles in this series so far are:


[1] See further the general Wikipedia description of UMBEL or its specification on the official UMBEL Web site.

Posted by AI3's author, Mike Bergman Posted on October 24, 2011 at 10:35 am in Structured Dynamics, UMBEL | Comments (1)
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Posted:October 17, 2011

Structured DynamicsToday’s Post is a Testimony to the Value of Vacations

My partner, Fred Giasson, today posted the second part of his series on open source. Since returning from a well-earned vacation a few weeks back — after more than three years without a break — Fred has been writing and developing up a storm. As someone said to me last week, “Fred’s on fire!” I could not agree more.

I think Fred’s post speaks for itself as to why and how Structured Dynamics has made a conscious choice to embrace open source. The major reason he puts forth — to bootstrap the company without the need for external investment — is unusual in itself. But one thing he is silent about is why this is a compelling reason. I’ll comment on that.

Fred and I have both worked for others dependent on their capital for our ventures (a few more times in my case). Capital is great for expansion and operations, but it can be deadly when visions requiring patience are in play. Structured Dynamics is only now a bit more than halfway through its five-year plan. While semantics technologies are exciting with a world of upside potential, they have also been incubated in academic labs with (as yet) a general lack of practical deployment. The promise is there, but often the delivery and maturation have been lacking. We are committed to play a visible role in correcting that.

The approach Fred outlines was not perhaps easily available to new startups a decade ago. But now, with open source and the Internet, costs of entry and ongoing development have dropped markedly. Yet, surprisingly, the idea of financing a startup via revenues is still not talked about sufficiently — let alone often used as an actual basis for building a company.

I’ve been fortunate to be able to partner with a young, world-class technologist whose maturity exceeds that of individuals many years his senior. He understands that in order to achieve important visions that the stewardship of those ideas can not be left to venture capitalists committed solely or mostly to gaming terms or near-term returns. We’re placing our bets on the paying customer and our own judgment.

So, it is great to see Fred continue his phenomenal development productivity since he returned from Hawaii. The benefit of his vacation is that we are also now getting his insights on his blog again.

Posted:October 11, 2011

Structured Dynamics The Need to Enforce Periodic Checkups on Web Properties

Face it, we all get busy and begin to overlook our own needs while we work for others on our jobs. The parable of the cobbler’s children going without shoes says it all.  It means that the shoemaker spends so much time looking after his customers’ needs that he neglects the needs of his own children.

We see the same phenomena in relation to our own personal assets, home repairs and cleaning, and a myriad of chores and background requirements. One way we can overcome these neglects is by scheduling annual or periodic checkups or activities. Spring cleaning is one such effort, as is annual asset portfolio re-balancing or doctor’s appointments or 10,000 mile vehicle servicing.

One of the cobbler’s chores for Structured Dynamics is the periodic care and feeding of our various Web sites. This has actually proven to be a non-trivial exercise, as our properties have grown to exceed 1400 static Web pages across some 30 diverse Web addresses and properties. As our client and code base expands, this exercise is increasingly demanding.

Taking advantage of a small break in the action, we have just completed another one of these reviews and revisions. Interestingly, as I was going through the various sites, I saw that date stamps for prior revisions tended to all occur in the September and October time frame. Last September, for example, SD went through a major redesign and new logo. Apparently, without consciously realizing it, we have been doing our own Web attic cleaning in the Fall.

Thus, as a way to formalize this process for us internally, I thought I’d briefly outline the Web site changes that we have cobbled together for this year. I suspect we’ll be doing another spiffing come Fall 2012.

Rationalizing the Properties

It is kind of frightening to realize that we have allowed our Web properties to grow to about 30 individual sites. This accretion happens gradually: a new initiative or capability arises that seems to warrant its own Web site. Yet each site carries with it a need to develop and maintain, as well as to explain its role and use in the Structured Dynamics information space.

Exclusive of internal development sites or ones dedicated to specific customers, here is the roster of existing SD properties that we have needed to rationalize:

Note that all properties with strike outs have now either been retired or consolidated with other properties. We have reduced the property count by 10, or by a third. Additional consolidations will be forthcoming.

Providing a Consistent Entry to the Various Properties

With the growth of our various Web properties and the diversity of the initiatives behind them, Fred and I have grown increasingly frustrated that our site visitors lacked a consistent way to access and understand these projects. Across all properties, Structured Dynamics has about 6,000 daily visitors or RSS tracking feeds.

Providing a consistent context of what these properties mean and their relation to one another is further compounded by the sheer size of our properties. Excluding dynamically generated pages (such as from search, demonstration of our semantic components, or use of the relation browser), we have on the order of 1400 static Web pages across all properties and blogs. Users may enter our information space via any of these entry points.

The answer to how to provide a consistent context on any Web page throughout our properties resides in the nifty JavaScript popup Fred recently described for his own blog. What we realized is that we could adapt this widget to provide a single overview of SD’s resources, and then add that widget to all of our properties such that it appears as a small tab at the bottom (sometimes side) of all property pages.

Then, when the tab SD Resource tab is clicked, the following popup appears:

So, whenever you are on one of our properties, look for the tab (generally) at the lower right corner of every Web page. That will take you to the common entry point across Structured Dynamics’ Web properties.

Updating the Properties

In this process we also went through some of our existing sites and made content, narrative and navigation changes consistent with this rationalization and consistent entry point. These updates were not nearly as extensive as the full re-designs from one year ago.

New Shoe Designs

With a constant stream of new initiatives and new understandings, it will remain a challenge for us to describe our various products and services. An even greater challenge will be to provide coherent descriptions of how all of these initiatives fit together consistent with our overall vision. One attempt at that is our new Overview page. Meanwhile, of course, we will occasionally be offering new Web goodies and sites as developments warrant. These will need to get integrated into this picture as well.

We think we have taken an itty-bitty step to improving this process with the SD Resources tab widget. Nonetheless, I’m sure that we will continue to craft new shoes to try to find ones that are still yet more comfortable and attractive. Thing is, we may have to wait another year before we get around to it again.

Posted by AI3's author, Mike Bergman Posted on October 11, 2011 at 3:26 am in Structured Dynamics | Comments (4)
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Posted:September 26, 2011

OWL - Web Ontology LanguageDocumenting the Emerging Ecosystem Around OWL 2

We have been touting the importance of OWL 2 as the language of choice for federating and reasoning over RDF and ontologies. An absolutely essential enabler of the OWL 2 language is version 3 of the OWL API (actually, version 3.2.4 at the time of this writing), a Java-based framework for accessing and managing the language. Protégé 4, the most popular open source ontology editor and integrated development environment (IDE), for example, is built around the OWL API.

As we laid out a bit more than a year ago, now codified on our TechWiki as the Normative Landscape of Ontology Tools (especially the second figure), we see the OWL API as the essential pivot point for all forms of ontology tools moving forward.

We have attempted to assemble a definitive and comprehensive list of all known tools presently based around version 3 of the OWL API. (We have surely missed some and welcome comments to this post that identify missing ones; we promise to add them and keep tracking them.) Herein is a listing of the 30 or so known OWL API-based tools:

  • Protégé 4 is a free, open source ontology editor and knowledge-base framework based on OWL 2 and centered on the OWL API
  • CEL, FaCT++, HermiT, Pellet, and Racer Pro reasoners provide OWL API wrappers and are also available as reasoner plugins to Protégé 4
  • There is also a FaCT++ port to Java that is also implementing the OWLReasoner and is available as a plugin for Protégé 4.1; it is at version 0.9 with user feedback welcomed
  • structOntology is an open source ontology editor and manager supporting Structured DynamicsconStruct implementation of the Open Semantic Framework (OSF) in Drupal; more information is provided here
  • TrOWL is a Tractable reasoning infrastructure for OWL 2. TrOWL supports both standard TBox and ABox reasoning, as well as conjunctive query answering
  • SKOSEd is a SKOS editor for Protege; just recently made compatible with Protégé 4.1
Please let us know of any missing OWL API tools that should be added to this list by submitting a comment to this post. We will keep this listing current.
  • Populus is a semantic spreadsheet framework using RightField and OPPL for creating OWL ontologies
  • Bubastis is a tool for detecting asserted logical differences between two ontologies, such as between versions. A stand alone version of the tool is also available for download from the EFO tools page. Bubastis is powered by the OWL API
  • Tab2OWL and its download is a Java tool for importing classes into an already existing OWL file. The script uses the OWL API to read in a tab delimited file of class details and create OWL classes from these rows, adding them to an existing ontology
  • S-Match is a semantic matching framework, which provides several semantic matching algorithms and facilities for developing new ones. Currently S-Match contains implementations of the original S-Match semantic matching algorithm, as well as minimal semantic matching algorithm and structure preserving semantic matching algorithm
  • The Alignment API is an API and implementation for expressing and sharing ontology alignments. It uses an RDF format for expressing alignments in a uniform way. Its four main interfaces (Alignment, Cell, Relation and Evaluator) provides these services: storing, finding, and sharing alignments; piping alignment algorithms (improving an existing alignment); manipulating (thresholding and hardening); generating processing output; and comparing alignments
  • The OWLlink API is a Java interface and implementation of the OWLlink protocol on top of the Java-based OWL API. The OWLlink API enables OWL API-based applications to access remote reasoners (so-called OWLlink servers), and it turns any OWL API aware reasoner into an OWLlink server
  • OPPL2 (ontology pre-processing language) is an abstract formalism that allows for manipulating ontologies written in OWL. It is 100% based on the Manchester OWL Syntax; a query language based on OWL (logical) axioms and variables; a scripting language that allows the addition/removal of OWL (logical) axioms. It is available as an Protégé 4.1 plug-in
  • OPPL Patterns It is available as an Protégé 4.1 plug-in
  • Posh (Prolog OWL Shell) is a command line utility that wraps the Thea OWL library to allow for advanced querying and processing of ontologies, combining the power of Prolog and OWL reasoning
  • POPL (Prolog Ontology Processing Language) allows you to write expressive ontology rewrite rules in a high-level declarative fashion using a syntax similar to Manchester syntax
  • OWLTools (aka OWL2LS – OWL2 Life Sciences) is a convenience Java API on top of the OWL API. Code is available here
  • LexOWL is a plug-in for Protégé 4. In order to add more powerful functionality (e.g., inferencing, editing) to the existing infrastructure and align LexGrid more closely with various Semantic Web technologies, the LexOWL plugin for Protégé 4 provides a way for representing the ontologies modeled within the LexGrid environment in OWL. A source for downloading this tool has not been found
  • Apero, a Protégé plug-in that is an ontology debugging tool based on the use of anti-patterns; see http://www.emcl-study.eu/fileadmin/master_theses/thesis_tahwil.pdf
  • DReW is a prototype DL reasoner over LDL+ ontologies and a prototype reasoner for dl-programs over LDL+ ontologies under well-founded semantics. It is not well developed or documented; it can be downloaded here
  • The LingInfo, LexOnto, LexInfo and LMF ontologies are available from the project website, as well as a corresponding Java API with an implementation for the commonly used OWL API
  • Thea2 is a Prolog library that provides complete support for querying and processing OWL 2 ontologies directly from within Prolog programs. Thea2 also offers additional capabilities including a bridge to the Java OWL API and translation of ontologies to Description Logic programs
  • GLOW is a visualization for OWL ontologies, based on Hierarchical Edge Bundles. Hierarchical Edge Bundles is a new visually attractive technique for displaying adjacency relations in hierarchical data, such as concept structures formed by `subclass-of’ and `type-of’ relations. The displayed adjacency relations can be selected from an ontology using a set of common configurations, allowing for intuitive discovery of information. It is a visualization library based on OWL API, as well as a plug-in for Protégé
  • ROWLKit is a simple GUI to reason and query over ontologies written in the OWL 2 QL profile of OWL
  • OBDA Plugin (Ontology-based data access) is an add-on for the Protégé ontology editor aimed at transforming Protégé into a fully fledged OBDA model editor. It provides data source and mapping editors, as well as querying facilities that, in conjunction with an OBDA-enabled reasoner, allows you to design and test every aspect of an OBDA system
  • OntoCAT provides high level abstraction for interacting with ontology resources including local ontology files in standard OWL and OBO formats (via OWL API)
  • SemaRule Navigator is an Eclipse-based toolkit of multiple semWeb tools, built around the OWL API, organized into a pipeline-like system (appears quite complicated)
  • OWLDB (alias Mnemosyne) is a storage system based on object-relational mappings utilising the OWL-API for the W3C Web Ontology Language OWL
  • Finally, for a periodically updated list of “official” extensions, see https://owlapi.svn.sourceforge.net/svnroot/owlapi/v3/branches/owlextensions/.

Addendum

Ignazio Palmisano also graciously suggested these additional sources:

which also further leads to this additional listing:

It is not clear if all of these offer OWL 2 support, let along work with the current OWL API.

Posted by AI3's author, Mike Bergman Posted on September 26, 2011 at 2:52 am in Ontologies, Semantic Web Tools | Comments (5)
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Posted:September 12, 2011

Judgment for Semantic TechnologiesFive Unique Advantages for the Enterprise

There have been some notable attempts of late to make elevator pitches [1] for semantic technologies, as well as Lee Feigenbaum’s recent series on Are We Asking the Wrong Question? about semantic technologies [2]. Some have attempted to downplay semantic Web connotations entirely and to replace the pitch with Linked Data (capitalized). These are part of a history of various ways to try to make a business case around semantic approaches [3].

What all of these attempts have in common is a view — an angst, if you will — that somehow semantic approaches have not fulfilled their promise. Marketing has failed semantic approaches. Killer apps have not appeared. The public has not embraced the semantic Web consonant with its destiny. Academics and researchers can not make the semantic argument like entrepreneurs can.

Such hand wringing, I believe, is misplaced on two grounds. First, if one looks to end user apps that solely distinguish themselves by the sizzle they offer, semantic technologies are clearly not essential. There are very effective mash-up and data-intensive sites such as many of the investment sites (Fidelity, TDAmeritrade, Morningstar, among many), real estate sites (Trulia, Zillow, among many), community data sites (American FactFinder, CensusScope, City-Data.com, among many), shopping sites (Amazon, Kayak, among many), data visualization sites (Tableau, Factual, among many), etc. , etc., that work well, are intuitive and integrate much disparate information. For the most part, these sites rely on conventional relational database backends and have little semantic grounding. Effective data-intensive sites do not require semantics per se [4].

Second, despite common perceptions, semantics are in fact becoming pervasive components of many common and conventional Web sites. We see natural language processing (NLP) and extraction technologies becoming common for most search services. Google and Bing sprinkle semantic results and characterizations across their standard search results. Recommendation engines and targeted ad technologies now routinely use semantic approaches. Ontologies are creeping into the commercial spaces once occupied by taxonomies and controlled vocabularies. Semantics-based suggestion systems are now the common technology used. A surprising number of smartphone apps have semantics at their core.

So, I agree with Lee Feigenbaum that we are asking the wrong question. But I would also add that we are not even looking in the right places when we try to understand the role and place of semantic technologies.

The unwise attempt to supplant the idea of semantic technologies with linked data is only furthering this confusion. Linked data is merely a means for publishing and exposing structured data. While linked data can lead to easier automatic consumption of data, it is not necessary to effective semantic approaches and is actually a burden on data publishers [5]. While that burden may be willingly taken by publishers because of its consumption advantages, linked data is by no means an essential precursor to semantic approaches. None of the unique advantages for semantic technologies noted below rely on or need to be preceded by linked data. In semantic speak, linked data is not the same as semantic technologies.

The essential thing to know about semantic technologies is that they are a conceptual and logical foundation to how information is modeled and interrelated. In these senses, semantic technologies are infrastructural and groundings, not applications per se. There is a mindset and worldview associated with the use of semantic technologies that is far more essential to understand than linked data techniques and is certainly more fundamental than elevator pitches or “killer apps.”

Five Unique Advantages

Thus, the argument for semantic technologies needs to be grounded in their foundations. It is within the five unique advantages of semantic technologies described below that the benefits to enterprises ultimately reside.

#1: Modern, Back-end Data Federation

The RDF data model — and its ability to represent the simplest of data up through complicated domain schema and vocabularies via the OWL ontology language — means that any existing schema or structure can be represented. Because of this expressiveness and flexibility, any extant data source or schema can be represented via RDF and its extensions. This breadth means that a common representation for any existing schema may be expressed. That expressiveness, in turn, means that any and all data representations can be described in a canonical way.

A shared, canonical representation of all existing schema and data types means that all of that information can now be federated and interrelated. The canonical means of federating information via the RDF data model is the foundational benefit of semantic technologies. Further, the practice of giving URIs as unique identifiers to all of the constituent items in this approach makes it perfectly suitable to today’s reality of distributed data accessible via the Web [6].

#2: Universal Solvent for Structure

I have stated many times that I have not met a form of structured data I did not like [7]. Any extant data structure or format can be represented as RDF. RDF can readily express information contained within structured (conventional databases), semi-structured (Web page or XML data streams), or unstructured (documents and images) information sources. Indeed, the use of ontologies and entity instance records in RDF is a powerful basis for driving the extraction systems now common for tagging unstructured sources.

(One of the disservices perpetuated by an insistence on linked data is to undercut this representational flexibility of RDF. Since most linked data is merely communicating value-attribute pairs for instance data, virtually any common data format can be used as the transmittal form.)

The ease of representing any existing data format or structure and the ability to extract meaningful structure from unstructured sources makes RDF a “universal solvent” for any and all information. Thus, with only minor conversion or extraction penalties, all information in its extant form can be staged and related together via RDF.

#3: Adaptive, Resilient Schema

A singular difference between semantic technologies (as we practice them) and conventional relational data systems is the use of an open world approach [8]. The relational model is a paradigm where the information must be complete and it must be described by a schema defined in advance. The relational model assumes that the only objects and relationships that exist in the domain are those that are explicitly represented in the database. This makes the closed world of relational systems a very poor choice when attempting to combine information from multiple sources, to deal with uncertainty or incompleteness in the world, or to try to integrate internal, proprietary information with external data.

Semantic technologies, on the other hand, allow domains to be captured and modeled in an incremental manner. As new knowledge is gained or new integrations occur, the underlying schema can be added to and modified without affecting the information that already exists in the system. This adaptability is generally the biggest source of economic benefits to the enterprise from semantic technologies. It is also a benefit that enables experimentation and lowers risk.

#4: Unmatched Productivity

Having all information in a canonical form means that generic tools and applications can be designed to work against that form. That, in turn, leads to user productivity and developer productivity. New datasets, structure and relationships can be added at any time to the system, but how the tools that manipulate that information behave remains unchanged.

User productivity arises from only needing to learn and master a limited number of toolsets. The relationships in the constituent datasets are modeled at the schema (that is, ontology) level. Since manipulation of the information at the user interface level consists of generic paradigms regarding the selection, view or modification of the simple constructs of datasets, types and instances, adding or changing out new data does not change the interface behavior whatsoever. The same bases for manipulating information can be applied no matter the datasets, the types of things within them, or the relationships between things. The behavior of semantic technology applications is very much akin to having generic mashups.

Developer productivity results from leveraging generic interfaces and APIs and not bespoke ones that change every time a new dataset is added to the system. In this regard, ontology-driven applications [9] arising from a properly designed semantic technology framework also work on the simple constructs of datasets, types and instances. The resulting generalization enables the developer to focus on creating logical “packages” of functionality (mapping, viewing, editing, filtering, etc.) designed to operate at the construct level, and not the level of the atomic data.

#5: Natural, Connected Knowledge Systems

All of these factors combine to enable more and disparate information to be assembled and related to one another. That, in turn, supports the idea of capturing entire knowledge domains, which can then be expanded and shifted in direction and emphasis at will. These combinations begin to finally achieve knowledge capture and representation in its desired form.

Any kind of information, any relationship between information, and any perspective on that information can be captured and modeled. When done, the information remains amenable to inspection and manipulation through a set of generic tools. Rather simple and direct converters can move that canonical information to other external forms for use by existing external tools. Similarly, external information in its various forms can be readily converted to the internal canonical form.

These capabilities are the direct opposite to today’s information silos. From its very foundations, semantic technologies are perfectly suited to capture the natural connections and nature of relevant knowledge systems.

A Summary of Advantages Greater than the Parts

There are no other IT approaches available to the enterprise that can come close to matching these unique advantages. The ideal of total information integration, both public and private, with the potential for incremental changes to how that information is captured, manipulated and combined, is exciting. And, it is achievable today.

With semantic technologies, more can be done with less and done faster. It can be done with less risk. And, it can be implemented on a pay-as-you-benefit basis [10] responsive to the current economic climate.

But awareness of this reality is not yet widespread. This lack of awareness is the result of a couple of factors. One factor is that semantic technologies are relatively new and embody a different mindset. Enterprises are only beginning to get acquainted with these potentials. Semantic technologies require both new concepts to be learned, and old prejudices and practices to be questioned.

A second factor is the semantic community itself. The early idea of autonomic agents and the heavy AI emphasis of the initial semantic Web advocacy now feels dated and premature at best. Then, the community hardly improved matters with its shift in emphasis to linked data, which is merely a technique and which completely overlooks the advantages noted above.

However, none of this likely matters. The five unique advantages for enterprises from semantic technologies are real and demonstrable today. While my crystal ball is cloudy as to how fast these realities will become understood and widely embraced, I have no question they will be. The foundational benefits of semantic technologies are compelling.

I think I’ll take this to the bank while others ride the elevator.


[1] This series was called for by Eric Franzon of SemanticWeb.com. Contributions to date have been provided by Sandro Hawke, David Wood, and Mark Montgomery.
[2] See Lee Feigenbaum, 2011. “Why Semantic Web Technologies: Are We Asking the Wrong Question?,” TechnicaLee Speaking blog, August 22, 2011; see http://www.thefigtrees.net/lee/blog/2011/08/why_semantic_web_technologies.html, and its follow up on “The Magic Crank,” August 29, 2011; see http://www.thefigtrees.net/lee/blog/2011/08/the_magic_crank.html. For a further perspective on this issue from Lee’s firm, Cambridge Semantics, see Sean Martin, 2010. “Taking the Tech Out of SemTech,” presentation at the 2010 Semantic Technology Conference, June 23, 2010. See http://www.slideshare.net/LeeFeigenbaum/taking-the-tech-out-of-semtech.
[3] See, for example, Jeff Pollock, 2008. “A Semantic Web Business Case,” Oracle Corporation; see http://www.w3.org/2001/sw/sweo/public/BusinessCase/BusinessCase.pdf.
[4] Indeed, many semantics-based sites are disappointingly ugly with data and triples and URIs shoved in the user’s face rather than sizzle.
[5] Linked data and its linking predicates are also all too often misused or misapplied, leading to poor quality of integrations. See, for example, M.K. Bergman and F. Giasson, 2009. “When Linked Data Rules Fail,” AI3:::Adaptive Innovation blog, November 16, 2009. See http://www.mkbergman.com/846/when-linked-data-rules-fail/.
[6] Greater elaboration on all of these advantages is provided in M. K. Bergman, 2009. “Advantages and Myths of RDF,” AI3:::Adaptive Innovation blog, April 8, 2009. See http://www.mkbergman.com/483/advantages-and-myths-of-rdf/.
[7] See M.K. Bergman, 2009. “‘Structs’: Naïve Data Formats and the ABox,” AI3:::Adaptive Innovation blog, January 22, 2009. See http://www.mkbergman.com/471/structs-naive-data-formats-and-the-abox/.
[8] A considerable expansion on this theme is provided in M.K. Bergman, 2009. “‘The Open World Assumption: Elephant in the Room,” AI3:::Adaptive Innovation blog, December 21, 2009. See http://www.mkbergman.com/852/the-open-world-assumption-elephant-in-the-room/.
[9] For a full expansion on this topic, see M.K. Bergman, 2011. “Ontology-driven Apps Using Generic Applications,” AI3:::Adaptive Innovation blog, March 7, 2011. See http://www.mkbergman.com/948/ontology-driven-apps-using-generic-applications/.
[10] See M.K. Bergman, 2010. “‘Pay as You Benefit’: A New Enterprise IT Strategy,” AI3:::Adaptive Innovation blog, July 12, 2010. See http://www.mkbergman.com/896/pay-as-you-benefit-a-new-enterprise-it-strategy/.

Posted by AI3's author, Mike Bergman Posted on September 12, 2011 at 3:11 am in Linked Data, Semantic Enterprise, Semantic Web | Comments (4)
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