Ontology Summit 2014 Kicks Off Tomorrow, Jan 16

The topic for this year's Summit is "Big Data and Semantic Web Meet Applied Ontology". The Summit kicks off with a conference call on Thursday, January 16th, at 9:30am PST/12:30pm EST/5:30pm GMT (call details). Here is a short excerpt from the Summit's "Goals and Objectives":

Since the beginnings of the Semantic Web, ontologies have played key roles in the design and deployment of new semantic technologies. Yet over the years, the level of collaboration between the Semantic Web and Applied Ontology communities has been much less than expected. Within Big Data applications, ontologies appear to have had little impact. This year's Ontology Summit is an opportunity for building bridges between the Semantic Web, Linked Data, Big Data, and Applied Ontology communities.

For those of you not familiar with the Summit, it was started in 2006, and there is a different theme each year. It is sponsored by a set of organizations including NIST, Ontolog, NCOR, NCBO, IAOA & NITRD. The way that the Summit works is by a series of conference calls (every Thursday) and lots of email discussion. It is culminated by a face-to-face meeting in late April.

I highly recommend participating, or even just lurking. It is not necessary (or even possible :-) to attend the call every week and to read every email on the ontolog-forum. (Also, it is not mandatory to come to the face-to-face.) If you have to miss something, slides and transcripts of the conference chats, as well as an email archive, are available online. In addition, at the end of the Summit, a "communique" is prepared that summarizes the discussions and work.

I have lurked on the edges of the Summit since the late 2000s, but finally have the time to actively participate. This year, I am co-championing Track A on "Common, Reusable Semantic Content". This topic is near and dear to my heart since I am a strong proponent of reuse. IMHO, it is always valuable (when it is possible) to build on someone else's good work, and benefit from their learnings, rather than starting from scratch. So, when modeling or designing, I look to find something similar and then extrapolate from, or build on it.

Needless to say, I usually don't take other ontologies "en masse", but pick and choose the semantics, patterns or ideas that make sense. How to do this is one aspect of Track A, and there is more. Here are some excerpts from our "Mission" statement:

Semantic technologies such as ontologies and reasoning play a major role in the Semantic Web and are increasingly being applied to help process and understand information expressed in digital formats. Indeed, the derivation of assured knowledge from the connection of diverse (and linked) data is one of the main themes of Big Data ... One challenge in these efforts is to build and leverage common semantic content thus reducing the burden of new ontology creation while avoiding silos of different ontologies. Examples of such content are whole or partial ontologies, ontology modules, ontological patterns and archetypes, and common theories related to ontologies and their fit to the real world ... Achieving commonality and reuse in a timely manner and with manageable resources remain key ingredients for practical development of quality and interoperable ontologies ... This track will discuss the reuse problem and explore possible solutions. Among these are practical issues like the use of ontology repositories and tools, and the possibility of using basic and common semantic content in smaller, more accessible pieces. The goal is to identify exemplary content and also define the related information to enable use/reuse in semantic applications and services. A secondary goal is to highlight where more work is needed and enable the applied ontology community to further develop semantic content and its related information.

I hope that you can join me!

Andrea

Using the semantic web to fight fires

On Dec 10th, I watched a fascinating webcast from SemanticWeb.com ... "Fighting Fire with the Semantic Web". I won't repeat the content (you can watch the presentation for yourself), but I will provide some of my take-aways.

We all have environments with problems similar to the fire fighters in Amsterdam (except, hopefully, not quite so dangerous or life-threatening). When going to a fire, they have to:

• Navigate the city streets (understand what else is happening with respect to road congestion, construction, etc.)
• Assess the conditions of the building, fire and available/closest resources
• Get different data based on the situation - for example, the relevant data for a forest fire (wind speed) is quite different than data for a structural fire (asbestos levels)
  
• Determine the tasks to be done and in what order
• Work with incomplete data
  
• Use tools that are not targeted to their particular situations (such as commercial GPSs targeted for normal driver needs)
  

And do this all in real-time (or faster, if possible).

In the fire fighting environment, data comes from a variety of locations (emergency infrastructure databases owned by the police, construction road reports, geospatial databases, internal fire department databases and spreadsheets, ...). This all sounds similar to the daily problems that a IT or business person experiences.

So, what did the Amsterdam fire department do? They created a flexible infrastructure based on accessing raw (RDF) data (adopting Tim Berners-Lee's mantra, "raw data now"). They pre-loaded any data that was (relatively) static, and obtained other data in real-time as needed. All the information was accessed and assembled on the fly ... based on the URI of the information. (The URI was used by the back-end system to determine how the data should be displayed to the user.) The goal was to get away from specific applications with their own UIs, system and display requirements, etc. As was stated, "we already have 8 screens in the fire truck... we don't need 9".

Because the environment focused on raw data, in a generic form (RDF triples), any relevant information could be requested and displayed. Since the infrastructure based its processing on the URI, a user could definitely "follow their nose". Taking this one step further, meta-data could also be supplied with a URI and/or a piece of data, for UI/display purposes. (Interestingly enough, I was just working on UI-related meta-data for CA's Unified Service Model, stealing some great insights from our Mainframe 2.0 team. :-)

So, what were the technical lessons that were highlighted:

• Use of RDF triples allowed the data to evolve with no change to the interfaces (except to support radically new kinds of data and their display needs - which should be rare)
• Use of raw data meant that there were no interfaces (and custom coding) with "exclusive" ways to access to data
• Use of common tags allowed quick review of possible data and selection
• All interfaces were data-driven ... meaning that the data/URI defined how it should be displayed
• Use of RDF triples (and URIs) allowed a "follow your nose" approach to locating other, related data
• What was needed could be defined by the user based on their needs (for example, what they already knew) and the situation

How cool is that? And, don't you wish that you had this yesterday?

Andrea

PriceWaterhouseCoopers Spring Technology Forecast (Part 3)

This is the last in a series of posts summarizing the PriceWaterhouseCooper Spring Technology Forecast. I spent a lot of time on the report, since it highlights many important concepts about the Semantic Web and business.

The last featured article in the report is entitled 'A CIO's strategy for rethinking "messy BI"'. The recommendation is to use Linked Data to bring together internal and external information - to help with the "information problem". How does PwC define the "information problem"? As follows ... "there's no way traditional information systems can handle all the sources [of data], many of which are structured differently or not structured at all." The recommendation boils down to creating a shared or upper ontology for information mediation, and then using it for analysis, for helping to create a business ecosystem, and to harmonize business logic and operating models. The two figures below illustrate these concepts.

The article includes a great quote on the information problem, why today's approaches (even metadata) are not enough, and the uses of Semantic Web technologies ... "Think of Linked Data as a type of database join that relies on contextual rules and pattern matching, not strict preset matches. As a user looks to mash up information from varied sources, Linked Data tools identify the semantics and ontologies to help the user fit the pieces together in the context of the exploration. ... Many organizations already recognize the importance of standards for metadata. What many don’t understand is that working to standardize metadata without an ontology is like teaching children to read without a dictionary. Using ontologies to organize the semantic rationalization of the data that flow between business partners is a process improvement over electronic data interchange (EDI) rationalization because it focuses on concepts and metadata, not individual data elements, such as columns in a relational database management system. The ontological approach also keeps the CIO’s office from being dragged into business-unit technical details and squabbling about terms. And linking your ontology to a business partner’s ontology exposes the context semantics that data definitions lack."

PwC suggests taking 2 (non-exclusive) approaches to "explore" the Semantic Web and Linked Data:

• Add the dimension of semantics and ontologies to existing, internal data warehouses and data stores
• Provide tools to help users get at both internal and external Linked Data

And, as with the previous posts, I want to finish with a quote from one of the interviews in the report. This quote comes from Frank Chum of Chevron, and discusses why they are now looking to the Semantic Web and ontologies to advance their business. "Four things are going on here. First, the Semantic Web lets you be more expressive in the business logic, to add more contextual meaning. Second, it lets you be more flexible, so that you don’t have to have everything fully specified before you start building. Then, third, it allows you to do inferencing, so that you can perform discovery on the basis of rules and axioms. Fourth, it improves the interoperability of systems, which allows you to share across the spectrum of the business ecosystem. With all of these, the Semantic Web becomes a very significant piece of technology so that we can probably solve some of the problems we couldn’t solve before. One could consider these enhanced capabilities [from Semantic Web technology] as a “souped up” BI [business intelligence]."

PriceWaterhouseCoopers Spring Technology Forecast (Part 2)

This post continues the review and summarization of PwC's Spring Technology Forecast, focused on the Semantic Web.

The second featured article is Making Semantic Web connections. It discusses the business value of using Linked Data, and includes interesting information from a CEO survey about information gaps (and how the Semantic Web can address these gaps). The article argues that to get adequate information, the business must better utilize its own internal data, as well as data from external sources (such as information from members of the business' ecosystem or the Web). This is depicted in the following two figures from the article ...

I also want to include some quotes from the article - especially since they support what I said in an earlier blog from my days at Microsoft, Question on what "policy-based business" means ... :-)

• Data aren’t created in a vacuum. Data are created or acquired as part of the business processes that define an enterprise. And business processes are driven by the enterprise business model and business strategy, goals, and objectives. These are expressed in natural language, which can be descriptive and persuasive but also can create ambiguities. The nomenclature comprising
• ... the natural language used to describe the business, to design and execute business processes, and to define data elements is often left out of enterprise discussions of performance management and performance improvement.
• ... ontologies can become a vehicle for the deeper collaboration that needs to occur between business units and IT departments. In fact, the success of Linked Data within a business context will depend on the involvement of the business units. The people in the business units are the best people to describe the domain ontology they’re responsible for.
• Traditional integration methods manage the data problem one piece at a time. It is expensive, prone to error, and doesn’t scale. Metadata management gets companies partway there by exploring the definitions, but it still doesn’t reach the level of shared semantics defined in the context of the extended virtual enterprise. Linked Data offers the most value. It creates a context that allows companies to compare their semantics, to decide where to agree on semantics, and to select where to retain distinctive semantics because it creates competitive advantage.
  

As in my last post, I want to reinforce the message and include a quote from one of the interviews. This one comes from Uche Ogbuji of Zepheira ... "... it’s not a matter of top down. It’s modeling from the bottom up. The method is that you want to record as much agreement as you can. You also record the disagreements, but you let them go as long as they’re recorded. You don’t try to hammer them down. In traditional modeling, global consistency of the model is paramount. The semantic technology idea turns that completely on its head, and basically the idea is that global consistency would be great. Everyone would love that, but the reality is that there’s not even global consistency in what people are carrying around in their brains, so there’s no way that that’s going to reflect into the computer. You’re always going to have difficulties and mismatches, and, again, it will turn into a war, because people will realize the political weight of the decisions that are being made. There’s no scope for disagreement in the traditional top-down model. With the bottom-up modeling approach you still have the disagreements, but what you do is you record them."

And, yes, I did say something similar to this in an earlier post on Semantic Web and Business. (Thumbs up :-)

PriceWaterhouseCoopers Spring Technology Forecast (Part 1)

In an earlier post, I mentioned PriceWaterhouseCoopers' spring technology forecast and its discussion of the Semantic Web in business. In this and the following post, I want to overview and highlight several of the articles. Let's start with the first featured article ...

Spinning a data Web overviewed the technologies of the Semantic Web, and discussed how businesses can benefit from developing domain ontologies and then mediating/integrating/querying them across both internal and external data. The value of mediation is summarized in the following figure ...

I like this, since I said something similar in my post on the Semantic Web and Business.

Backing up this thesis, Tom Scott of BBC Earth provided a supporting quote in his interview, Traversing the Giant Global Graph. "... when you start getting either very large volumes or very heterogeneous data sets, then for all intents and purposes, it is impossible for any one person to try to structure that information. It just becomes too big a problem. For one, you don’t have the domain knowledge to do that job. It’s intellectually too difficult. But you can say to each domain expert, model your domain of knowledge— the ontology—and publish the model in the way that both users and machine can interface with it. Once you do that, then you need a way to manage the shared vocabulary by which you describe things, so that when I say “chair,” you know what I mean. When you do that, then you have a way in which enterprises can join this information, without any one person being responsible for the entire model. After this is in place, anyone else can come across that information and follow the graph to extract the data they’re interested in. And that seems to me to be a sane, sensible, central way of handling it."

Continuing on the topic of the Web of Data (aka Linked Data)

There is lots being published about Linked Data. I just saw that the Spring 2009 PriceWaterhouseCooper technology forecast is full of data Web and Semantic web coolness. But, before I jump into the forecast, I would like to give some background on the Linked Data work that is happening in the industry today.

Linking Open Data (LOD) is a W3C project. According to their web site, "The goal of the W3C SWEO Linking Open Data community project is to extend the Web with a data commons by publishing various open data sets as RDF on the Web and by setting RDF links between data items from different data sources.RDF links enable you to navigate from a data item within one data source to related data items within other sources using a Semantic Web browser. RDF links can also be followed by the crawlers of Semantic Web search engines, which may provide sophisticated search and query capabilities over crawled data. As query results are structured data and not just links to HTML pages, they can be used within other applications. ... Collectively, the data sets consist of over 4.7 billion RDF triples, which are interlinked by around 142 million RDF links (May 2009)."

Here is the LOD figure showing what is linked today (actually March 2009):

Just to get a feel for what is included ... let me note that DBpedia (the bigger circle in the left center of the image) provides structured access to Wikipedia's human-oriented data (actually, it provides a SPARQL interface). According to DBpedia's web site, "The DBpedia knowledge base currently describes more than 2.6 million things, including at least 213,000 persons, 328,000 places, 57,000 music albums, 36,000 films, 20,000 companies. The knowledge base consists of 274 million pieces of information (RDF triples). It features labels and short abstracts for these things in 30 different languages; 609,000 links to images and 3,150,000 links to external web pages; 4,878,100 external links into other RDF datasets, 415,000 Wikipedia categories, and 75,000 YAGO categories. The DBpedia knowledge base has several advantages over existing knowledge bases: it covers many domains; it represents real community agreement; it automatically evolve as Wikipedia changes, and it is truly multilingual. The DBpedia knowledge base allows you to ask quite surprising queries against Wikipedia, for instance “Give me all cities in New Jersey with more than 10,000 inhabitants” or “Give me all Italian musicians from the 18th century”. Altogether, the use cases of the DBpedia knowledge base are widespread and range from enterprise knowledge management, over Web search to revolutionizing Wikipedia search."

Going back to Tim Berners-Lee's request for us to imagine what it would be like to have people load and connect knowledge, let's imagine what all this data can do for a business and its decision making processes ....

Web 3.0 and the Web of Data

Web 3.0 is coming up (a lot) in posts on Read-Write Web and in other places. One Read-Write Web posting (The Web of Data, written by Alexander Korth in April of this year) discussed the 3 aspects of the next web (Web 3.0) ... "In the coming years, we will see a revolution in the ability of machines to access, process, and apply information. This revolution will emerge from three distinct areas of activity connected to the Semantic Web: the Web of Data, the Web of Services, and the Web of Identity providers. These webs aim to make semantic knowledge of data accessible, semantic services available and connectable, and semantic knowledge of individuals processable ...".

Tim Berners-Lee focused on the Web of Data in his TED talk on the next Web (recorded in Feb 2009). The talk is only a little longer than 15 minutes in length, and I highly recommend it. The key points are that we are now moving from a document-centric approach to storing information, to making raw data available and processable. That raw data is "linked data" - data about things (identified by URIs), including other interesting information (as RDF triples) and highlighting the relationships between the things. It is important to note that this is not about making data available through specific APIs or anticipated/pre-programmed queries on a "pretty" web site - but about making the "unadulterated data" available for machine understanding and new uses. It is about sharing and adding to data, making connections and relationships in novel ways, and bridging disciplines.

If you think about business and an enterprise, think about how powerful this would be - to capture knowledge, share it via social networking technologies, allow update and addition to the knowledge within the enterprise (again using the social networking tools of today), and to bridge disciplines and knowledge using the Semantic web mining and matching technologies. Overall, we improve the ability of the enterprise to capture and access its knowledge, and increase the captured knowledge. In the talk, Tim Berners-Lee asks people to imagine the "incredible resource" of "people doing their bit to produce a little bit, and it all connecting."

Just imagine ....