Our discussion of Big Data at SemTechBiz, begun here, continues:

The Enterprise Linked Data Cloud Needs Semantics, And More

Another exploration of Big Data’s intersection with semantic technology will take place at this session, where Dr. Giovanni Tummarello, senior research fellow at DERI and CTO of SindiceTech, will talk about the former becoming an enabler for the latter to be really useful in enterprises. “A lot of people say it’s via Big Data that semantic technologies like RDF will see a coming of age and clear applications in certain industries,” he says. There’s value to adding data first and understanding it later, and to that end, “semantic technologies give you the most agile tool to deal with data you don’t know, where there’s a lot of diversity, and you don’t know what of it particularly will be useful.”

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The W3C has announced that eleven specifications of SPARQL 1.1 have been published as recommendations. SPARQL is the Semantic Web query language.  We caught up with Lee Feigenbaum, VP Marketing & Technology at Cambridge Semantics Inc. to discuss the significance of this announcement. Feigenbaum is a SPARQL expert who currently serves as the Co-Chair of the W3C’s SPARQL Working Group, leading the design of SPARQL.

Feigenbaum says, “SPARQL 1.1 is a huge leap forward in providing a standard way to access and update Semantic Web data. By reaching W3C Recommendation status, Semantic Web developers, vendors, publishers and consumers have a stable, well-vetted, and interoperable set of standards they can rely on for the foreseeable future.”

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Hadoop is on almost every enterprise’s radar – even if they’re not yet actively engaged with the platform and its advantages for Big Data efforts. Analyst firm IDC earlier this year said the market for software related to the Hadoop and MapReduce programming frameworks for large-scale data analysis will have a compound annual growth rate of more than sixty percent between 2011 and 2016, rising from $77 million to more than $812 million.

Yet, challenges remain to leveraging all the possibilities of Hadoop, an Apache Software Foundation open source project, especially as it relates to empowering the data scientist. Hadoop is composed of two sub-projects: HDFS, a distributed file system built on a cluster of commodity hardware so that data stored in any node can be shared across all the servers, and the MapReduce framework for processing the data stored in those files.

Semantic technology can help solve many of the  challenges, Michael A. Lang Jr., VP, Director of Ontology Engineering Services at Revelytix, Inc., told an audience gathered at the Semantic Technology & Business Conference in New York City yesterday.

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Querying semantic databases isn’t necessarily the most user-friendly thing to do on the planet. Consultancy ABComputing is trying to change that, with its EQL (Entity Query Language) technology.

“We wanted to where possible have it so the syntax was more closely mirrored with SQL than with SPARQL because people understand SQL,” says Martin Bradford, primary developer at the company. “If you build on that knowledge, that helps matters.”

EQL came about from the company’s work on a potential contract that involved semantic technology. Exposure to the world of semantic web technologies and SPARQL in particular led Antonia Bradford, who started the firm a couple of decades ago, to conclude that there had to be a better way of working with RDF data without sacrificing the power inherent in the semantic web.

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(Editor’s Note, June 29: The SparQLed project URL now is available here.)

SindiceTech today released SparQLed, the SindiceTech Assisted SPARQL Editor, as an open source project. SindiceTech, a spinoff company from the DERI Institute, commercializes large-scale, Big Data infrastructures for enterprises dealing with semantic data. It has roots in the semantic web index Sindice, which lets users collect, search, and query semantically marked-up web data (see our story here).

SparQLed also is one of the components of the commercial Sindice Suite for helping large enterprises build private linked data clouds. It is designed to give users all the help they need to write SPARQL queries to extract information from interconnected datasets.

“SPARQL is exciting but it’s difficult to develop and work with,” says Giovanni Tummarello, who led the efforts around the Sindice search and analysis engine and is founder and CEO of SindiceTech.

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The Common Crawl Foundation is on the verge of adding to its Amazon Web Services (AWS) Public Data Set of openly and freely accessible web crawl data. It was back in January that Common Crawl announced the debut of its corpus on AWS (see our story here). Now, a billion new web sites are in the bucket, according to Common Crawl director Lisa Green, adding to the 5 billion web pages already there.

“When are you going to have new data is one of most frequent questions we get,” she says. The answer is that processing is underway now, and she hopes they’ll be ready to go this week.

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Semantic Web Community: I’m disappointed in us!  Or at least in our group marketing prowess.  We have been failing to capitalize on two major trends that everyone has been talking about and that are directly addressable by Semantic Web technologies!  For shame.

I’m talking of course about Big Data and NoSQL.  Given that I’ve already given my take on how Semantic Web technology can help with the Big Data problem on SemanticWeb.com, this time around I’ll tackle NoSQL and the Semantic Web.

After all, we gave up SQL more than a decade ago.  We should be part of the discussion.  Heck, even the XQuery guys got in on the action early!

Check out this Google Trends diagram.

Semantic Web vs. NoSQL on Google Trends

NoSQL came out of nowhere in 2009, and now dominates much of the database conversation on the web.  Document stores like MongoDB and CouchDB, distributed, key-value stores such as Riak and Cassandra, and other weird stores like Hadoop-as-database (never understood that usage myself) now dominate the conversation as the alternative to traditional, SQL databases.

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If you missed last week’s excellent introduction to SPARQL by Bob DuCharme of TopQuadrant and the recently released Learning SPARQL, the recorded webcast is now available.  In this presentation, Bob shows how to create and run SPARQL queries. He also talks about the role that the query language can play in application development. Lastly, he looks at the range of uses people are finding for SPARQL above and beyond querying of RDF data, such as querying relational data, defining rules to enhance data quality, and more…

Watch the webcast here:

http://mediabistro.adobeconnect.com/p8mwns7kdgx/

There were some questions we did not get to during the hour, and Bob has been kind enough to answer these offline.

BONUS Q&A with Bob DuCharme:

Q: Can sparql engines integrate reasoners and reason over the data on the fly? Read more

Microsoft is looking for a Software Development Engineer to join their SQL Server Engine Team in Redmond, WA. According to the post, “Extracting semantics and other forms of data inference at enterprise and web scale and making it easily searchable is really where the game is at today. Microsoft is locking horns with the competition and making big bets to win this challenge. We are building a new Database Search and Semantics data extraction and machine learning team inside of SQL Server (a $3.5 bil. business) with the larger vision of creating a new niche for SQL Server ‘beyond relational’ and extending out to Data Warehousing, in order to add more value to our enterprise product offerings.” Read more

[Editor's Note: With the San Francisco-based Semantic Technology Conference just two weeks away, we are going to feature a few of the the new and important products to be discussed at the conference. We will focus on a few of the products and tools that will be featured at the event. The full conference program is available here.]

At SemTech, representatives from Oracle will demonstrate the semantic aspects of Oracle database in two sessions:

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