The winner of the Semantic Web Challenge at November’s International Semantic Web Conference (ISWC) was EventMedia Live, a web-based environment that exploits real-time connections to event and media sources to deliver rich content describing events that are associated with media, and interlinked with the Linked Data cloud.

This week, it will begin a one-year effort under a European Commission-funded project to align its work with the Nokia Maps database of places, so that mobile users of the app can quickly get pictures of these venues that were taken by users with EventMedia’s help.

A project of EURECOM, a consortium combining seven European universities and nine international industrial partners, EventMedia Live has its origins in the “mismatch between those sites specializing in announcing upcoming events and those other sites where users share photos, videos and document those events,” explains Raphaël Troncy, assistant professor at the EURECOM: School of Engineering & Research Center, Multimedia Communications, and one of the project’s leaders.

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There has always been a misconception between the relationship of RDF and XML. The main difference: XML is a syntax while RDF is a data model.

RDF has several syntaxes (Turtle, N3, etc) and XML is one of those (known as RDF/XML). Actually, RDF/XML is the only W3C standard syntax for RDF (Currently, there is Last Call on Turtle, a new W3C standard syntax for RDF). Therefore, comparing XML and RDF is like comparing apples with oranges. What can be compared is their data models. The RDF data model is a graph while the XML data model is a tree.

Comparing RDF with XML

Joshua Tauberer has an excellent comparison between RDF and XML, which I recommend. Two advantages of RDF are highlighted: flexibility of the data model and use of URIs as global unique identifiers.

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Clark & Parsia’s Stardog lightweight RDF database is moving into release candidate 1.0 mode just in time for next week’s upcoming Semantic Technology & Business Conference in San Francisco next week. The product’s been stable and useable for awhile now, but a 1.0 nomenclature still carries weight with a good number of IT buyers.

The focus for the product, says cofounder and managing principal Kendall Clark, is to be optimized for what he says is the fat part of the market – and that’s not the part that is dealing with a trillion RDF triples. “Most people and organizations don’t need to scale to trillions of anything,” though scaling up, and up, and up, is where most of Clark & Parsia’s competitors have focused their attention, he says. “We’ve seen a significant percentage of what people are doing with semantic technology and most applications are not at a billion triples today.” Take as an example Clark & Parsia’s customer, NASA, which built an expertise location system based on semantic technology that today is still not more than 20 million triples. “You might say that’s a little toy but not if you are at NASA and need defined experts, it is a real, valuable thing and we see this all the time,” he says.

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A paper entitled  “Recovering Semantics of Tables on the Web” was presented at  the 37^th Conference on Very Large Databases in Seattle, WA . The paper’s authors included 6 Google engineers along with professor Petros Venetis of Stanford University and Gengxin Miao of UC Santa Barbara. The paper summarizes an approach for recovering the semantics of tables with additional annotations other than what the author of a table has provided. The paper is of interest to developers working on the semantic web because it gives insight into how programmers can use semantic data (database of triples) and Open Information Extraction (OIE) to enhance unstructured data on the web. In addition they compare how a  “maximum-likelihood” model, used to assign class labels to tables, compares to a “database of triples” approach. The authors show that their method for labeling tables is capable of labeling “an order of magnitude more tables on the web than is possible using Wikipedia/YAGO and many more than freebase.”

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Ian Davis recently sketched out an “alternate way of thinking about RDF, one that is resource-oriented rather than triple-oriented.” He writes, “The Resource Description Framework (RDF) is a framework for representing information in the Web. RDF has a simple data model that is easy for applications to process and manipulate. The data model is independent of any specific serialization syntax. RDF provides information about resources through Resource Descriptions. A Resource Description consists of: (1) A subject resource identified by a URI. (2) Zero or more relationships to other resources. (3) Zero or more properties having literal values.” Read more

A new article by Jesse Weaver corrects two misconceptions about the semantic web. The first is that the semantic web strives to create “one ontology to rule them all.” The article states, “To my knowledge, nobody has ever claimed that there should be ‘one ontology to rule them all.’ Instead, what is regularly promoted is ontology reuse and/or integration. For example, the FOAF ontology is widely used in the semantic web to describe persons; why create your own ontology when you can reuse a well-established one? Integration of ontologies allows for conciliation of perspectives, causing data that use these ontologies to become meaningfully related. Admittedly, there are some rather large, comprehensive ontologies out there, and there are some very popular and pervasive ones, too. However, there is no standard or recommendation that requires publishers of RDF data to comply with any particular ontology. You could even ignore the RDF vocabulary if you so please (yes, even rdf:type).” Read more

SPARQL is the standardized query language for RDF, the same way SQL is the standardized query language for relational databases. If this is the first time you look at SPARQL, but you’re familiar with SQL, you will see some similarities because it shares several keywords such as SELECT, WHERE, etc. It also has new keywords that you have never seen if you come from a SQL world such as OPTIONAL, FILTER and much more.

Recall that RDF is a triple comprised of a subject, predicate and object. A SPARQL query consists of a set of triples where the subject, predicate and/or object can consist of variables. The idea is to match the triples in the SPARQL query with the existing RDF triples and find solutions to the variables. A SPARQL query is executed on a RDF dataset, which can be a native RDF database, or on a Relational Database to RDF (RDB2RDF) system, such as Ultrawrap.  These databases have SPARQL endpoints which accept queries and return results via HTTP.

A basic example

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Bob DuCharme recently discussed the value of linked data in US law. DuCharme notes, “At a recent W3C Government Linked Data Working Group working group meeting, I started thinking more about the role in linked data of laws that are published online. To summarize, you don’t want to publish the laws themselves as triples, because they’re a bad fit for the triples data model, but as online resources relevant to a lot of issues out there, they make an excellent set of resources to point to, although you may not always get the granularity you want.” Read more