Photo courtesy: Flickr/rvaphotodude

Last month at its MarkLogic World 2013 conference, the enterprise NoSQL database platform provider talked semantics as it related to its MarkLogic Server technology that ingests, manages and searches structured, semi-structured, and unstructured data (see our story here). The vendor late last week was scheduled to provide an early access release of MarkLogic 7, formally due by year’s end, to some dozens of initial users.

“People see a convergence of search and semantics,” Stephen Buxton, Director, Product Management, recently told The Semantic Web Blog. To that end, a lot of the vendor’s customers have deployed MarkLogic technology as well as specialized triple stores, but what they really want, he says, is an integrated approach, “a single database that does both individually and both together,” he says. “We see the future of search as semantics and the future of semantics as search, and they are very much converging.” At its recent conference, Buxton says the company demonstrated a MarkLogic app it built to function like Google’s Knowledge Graph to provide an idea of the kinds of things the enterprise might do with both search and semantics together.

Following up on the comments made by MarkLogic CEO Gary Bloom at his keynote address at the conference, Buxton explained that, “the function in MarkLogic we are working on in engineering is a way to store and manage triples in the MarkLogic database natively, right alongside structured and unstructured information – a specialized triples index so queries are very fast, and so you can do SPARQL queries in MarkLogic. So, with MarkLogic 7 we will have a world-class triple store and world-beating information store – no one else does documents, values and triples in combination the way MarkLogic 7 will.”

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Enterprise NoSQL database platform provider MarkLogic has come into some cash: a $25 million round of growth capital from investors including Sequoia Capital, Tenaya Capital, Northgate Capital, CEO Gary Bloom and other corporate executives. Yesterday, at the company’s MarkLogic World 2013 conference, Bloom also prepared the audience to hear more today from company executives about MarkLogic’s next steps in semantics for its MarkLogic Server technology that ingests, manages and searches structured, semi-structured, and unstructured data.

“The way to think about this is that when we look at semantics, we didn’t … say we just want to check a box on semantics,” Bloom said, by working with partners on some low-hanging fruit – although it will be collaborating with them on various semantic enrichment capabilities. “We think semantics is critical technology, and more interesting I believe is that it is a critical technology that is both a search technology as well as a database technology.” Others in the marketplace will focus on changing their search engines to do semantics, but optimum results won’t come if all that’s being done is layering in semantics at the search level, he said.

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“We want to help the world make sense of data and we think graphs are the best way of doing that.”

That’s the word from Emil Eifrem, CEO of Neo Technology, which makes the open-source Neo4j NoSQL graph database. He’s not talking in terms of RDF-centric solutions, even though he says he’s 100 percent in agreement with the vision of the semantic web and machine readability. “The world is a graph,” Eifrem says, “and RDF is a great way of connecting things. I’m all in agreement there.” The problem, in his opinion, is that execution on the software end there has been lacking.

“This comes down to usability,” he says, and the average developer, he believes, finds the semantic web-oriented tools largely incomprehensible. Eifrem says he’s speaking from real-world experiences, having worked directly with RDF and taught classes on the semantic web layers. Where it took a week to get students up to speed on things like Jena and Sesame, they ‘get’ the property graph and graph databases in half-a-day, he says. Neo4j stores data in nodes connected by directed, typed relationships with properties on both – also known as a property graph.

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A couple of years back, The Semantic Web Blog visited with Vijay Bulusu to gain some insight into how pharma giant Pfizer Inc. was moving forward with semantic technology (see article here). At last week’s Semantic Technology and Business Conference in New York City, Bulusu, director, informatics and innovation at Pfizer, provided additional perspective on the issue – first, during the presentation on Using Linked Semantic Data in Biomedical Research and Pharmaceuticals (see coverage of that here), and then in a follow-up conversation.

A struggle for pharma companies, Bulusu notes, sits in driving standards for data that exists across system silos, so it is broadly applicable across groups. A transaction like creating a batch of materials, doing analytical testing on it and enabling clinical trial releases is the work of multiple groups of people in departments like R&D entering data across different systems.

The foundational layer needed to support data aggregation in a persistent graph semantic database and visualization with collaborative, semantic knowledge maps “is all about data already in transactional, silo’d systems,” Bulusu says. “We want to make sure that across those systems, key data is entered consistently for entities.” That means limiting them to selecting via a drop-down list from a vocabulary that is consistently managed and published from a single source to all these transaction systems, so the same entity is called by the same name as it traverses systems to support analytics and other requirements. That, he says, “is where we directly impact the day-to-day operational work of users.”

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Last week The Semantic Web Blog continued its coverage of Google’s Knowledge Graph with the news of its worldwide launch for English-language users. This week we’ve learned about a paper submitted to the 1^st International Workshop on Knowledge Extraction and Consolidation from Social Media (KECSM2012), which takes place in November in Boston, about work underway on the topic of crowd-sourcing an Open Knowledge Graph API.

The paper, authored by Thomas Steiner of the Universitat Politècnica de Catalunya in Barcelona and Stefan Mirea of Jacobs University, Bremen, Germany, and currently pending review, proposes that the crowd step in where Google has so far failed to tread when it comes to creating an interface to the Knowledge Graph of more than 500 million objects – landmarks, celebrities, cities, sports teams, buildings, movies, celestial objects, works of art, and more – and 3.5 billion facts about and relationships between them. There is no publicly available list of all those objects, and, say the authors, even if there were, “it would not be practicable (nor allowed by the terms and conditions of Google) to crawl it.” Hence, the crowd-sourcing approach.

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The Digital Enterprise Research Institute (DERI) is kicking off a project with Fujitsu Laboratories Ltd. in Japan to build a large-scale RDF store in the cloud capable of processing hundreds of billions of triples. The idea, says DERI research fellow Dr. Michael Hausenblas, “is to build up a platform that allows you to process and convert any kind of data” — from relational databases to LDAP record-based, directory-like data, but also streaming sources of data, such as sensors and even the Twitter firehose.

The project has defined eight different potential enterprise use cases for such a platform, ranging from knowledge-sharing in health care and life science to dashboards in financial services informed by XBRL data. “Once the platform is there we will implement at least a couple of these use cases on business requirements, and essentially we are going to see which are the most promising for business units,” Hausenblas says.

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A fascinating project has been undertaken by the Partners of the Pan-Canadian Documentary Heritage Network (PCDHN): It’s a proof-of-concept showcase of using Linked Open Data visualizations for “Out of the Trenches.” This is a look at the First World War from the Canadian perspective: war songs, postcards, newspapers, photos, films, and these resources’ intersection with Canadian soldiers who fought in the war.

These digital resources from organizations such as McGill University, the Universities of Alberta, Calgary and Saskatchewan, and the Bibliothèque et Archives nationales du Québec have been linked through existing metadata provided in formats ranging from spreadsheets to MODS XML to RDF. Rather than reduce the metadata to a common subset, the approach was to maximize its use by moving to the “web of data” concept, so that the resources can be combined in different and unexpected ways, according to the proof-of-concept final report that was issued on the project.

The premise was to expose the metadata for these resources using RDF XML and existing published ontologies such as the Event Ontology, the Dublin Core Ontology and the Biographical Ontology, elements sets, vocabularies and resources like the Geonames geographical database to maximize discovery by the user community and contribute to the Semantic Web.

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Yesterday The Semantic Web Blog discussed how personalized mobile assistance came up on the lists of a bright future in the eyes of semantic web experts (see here). Sharing that vision is the team at Vital.AI, the NYC-startup founded by Marc Hadfield.  Its Thrive.AI app, also a contender at SemTech’s Startup Competition, is a personalized semantic shopping agent for the iPad, but the underlying Vital.AI platform on which it is built provides an integrated suite of components for a variety of knowledge-centric, intelligence-rich, Big Data-driven applications.

The e-commerce agent, Hadfield told attendees at SemTech in San Francisco last week, was the company’s own foray into figuring out what it needed to add to the platform to make it easier to build apps that bring semantic technologies and Big Data together.

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Coming in June from start-up Meronymy is a new RDF enterprise database management system, the Meronymy SPARQL Database Server. The company, founded by Inge Henriksen, began life because of the need he saw for a high-performance and more scalable RDF database server.

The idea to focus on a database server exclusively oriented to Linked Data and the Semantic Web came as a result of Henriksen’s work over the last decade as an IT consultant implementing many semantic solutions for customers in sectors such as government and education. “One issue that always came up was performance,” he explains, especially when performing more advanced SPARQL queries against triple stores using filters, for example.

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For all of you Java developers out there, Clark&Parsia has released an initial integration between Stardog and Spring. The Stardog website states, “Spring is a platform to build and run enterprise applications in Java. Stardog’s Spring support makes life easier for enterprise developers who need to work with Semantic Web technology—including RDF, SPARQL, and OWL—by way of Stardog. The Spring for Stardog source code is available on Github. A more feature-full version of will be available in Stardog Enterprise Edition.” Read more