Reasoning is the task of deriving implicit facts from a set of given explicit facts. These facts can be expressed in OWL 2 ontologies and stored RDF triplestores. For example, the following fact: “a Student is a Person,” can be expressed in an ontology, while the fact: “Bob is a Student,” can be stored in a triplestore. A reasoner is a software application that is able to reason. For example, a reasoner is able to infer the following implicit fact: “Bob is a Person.”

Reasoning Tasks

Reasoning tasks considered in OWL 2 are: ontology consistency, class satisfiability, classification, instance checking, and conjunctive query answering.

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OWL, the Web Ontology Language has been standardized by W3C as a powerful language to represent knowledge (i.e. ontologies) on the Web. OWL has two functionalities. The first functionality is to express knowledge in an unambiguous way. This is accomplished by representing knowledge as set of concepts within a particular domain and the relationship between these concepts. If we only take into account this functionality, then the goal is very similar to that of UML or Entity-Relationship diagrams. The second functionality is to be able to draw conclusions from the knowledge that has been expressed. In other words, be able to infer implicit knowledge from the explicit knowledge. We call this reasoning and this is what distinguishes OWL from UML or other modeling languages.

OWL evolved from several proposals and became a standard in 2004. This was subsequently extended in 2008 by a second standard version, OWL 2. With OWL, you have the possibility of expressing all kinds of knowledge. The basic building blocks of an ontology are concepts (a.k.a classes) and the relationships between the classes (a.k.a properties).  For example, if we were to create an ontology about a university, the classes would include Student, Professor, Courses while the properties would be isEnrolled, because a Student is enrolled in a Course, and isTaughtBy, because a Professor teaches a Course.

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Triplestores are Database Management Systems (DBMS) for data modeled using RDF. Unlike Relational Database Management Systems (RDBMS), which store data in relations (or tables) and are queried using SQL, triplestores store RDF triples and are queried using SPARQL.

A key feature of many triplestores is the ability to do inference. It is important to note that a DBMS typically offers the capacity to deal with concurrency, security, logging, recovery, and updates, in addition to loading and storing data. Not all Triplestores offer all these capabilities (yet).

Triplestore Implementations

Triplestores can be broadly classified in three types categories: Native triplestores, RDBMS-backed triplestores and NoSQL triplestores. Read more

If you are learning about the Semantic Web, one of the things you will hear is that the Semantic Web assumes the Open World. In this post, I will clarify the distinction between the Open World Assumption and the Closed World Assumption.

The Closed World Assumption (CWA) is the assumption that what is not known to be true must be false.

The Open World Assumption (OWA) is the opposite. In other words, it is the assumption that what is not known to be true is simply unknown.

Consider the following statement: “Juan is a citizen of the USA.” Now, what if we were to ask “Is Juan a citizen of Colombia?” Under a CWA, the answer is no. Under the OWA, it is I don’t know.

When do CWA and OWA apply?

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Big Graph Data Panelists (L to R): Mike Stonebraker, John Giannandrea, Bryan Thompson, Tim Berners- Lee, Frank van Harmelen

Last week, the 11th International Semantic Web Conference (ISWC 2012) took place in Boston. It was an exciting week to learn about the advances of the Semantic Web and current applications.

The first two days, Sunday November 11 and Monday November 12, consisted of 18 workshops and 8 tutorials. The following three days (Tuesday November 13 – Thursday November 15) consisted of keynotes, presentation of academic and in-use papers, the Big Graph Data Panel and industry presentations. It is basically impossible to attend all the interesting presentations. Therefore, I am going to try my best to summarize and offer links to everything that I can.

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SKOS, which stands for Simple Knowledge Organization System, is a W3C standard, based on other Semantic Web standards (RDF and OWL), that provides a way to represent controlled vocabularies, taxonomies and thesauri. Specifically, SKOS itself is an OWL ontology and it can be written out in any RDF syntax.

Before we dive into SKOS, what is the difference between Controlled Vocabulary, Taxonomy and Thesaurus?

A controlled vocabulary is a list of terms which a community or organization has agreed upon. For example: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday are the days of the week.

A taxonomy is a controlled vocabulary organized in a hierarchy. For example, we can have the terms Computer, Tablet and Laptop and the concepts Tablet and Laptop are subclasses of Computer because a Tablet and Laptop are types of Computers.

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Yesterday, the W3C announced the advancement to Proposed Recommendations of two Relational Database to RDF (RDB2RDF) documents: 1) R2RML: RDB to RDF Mapping Language and 2) A Direct Mapping of Relational Data to RDF. Additionally, two Working Group Notes were also published: R2RML and Direct Mapping Test Cases and RDB2RDF Implementation Report.

Given that a vast amount of data in enterprises and on the web resides in Relational Databases, it is paramount to have methods that expose relational data as RDF, in order for Semantic Web applications to interact with Relational Databases. The R2RML and Direct Mapping standards bridges this gap. Direct Mapping is an automatic default mapping and R2RML is a mapping language where users can customize the mappings. With these two standards, we will now be able to see more and more relational data in the Linked Data cloud and part of Semantic Web applications.

A little bit of RDB2RDF history

Tim Berners-Lee wrote a Design Issue on “Relational Database on the Semantic Web” dating back initially to 1998. During the 2000′s, several tools, such as R2O, D2RQ, Virtuoso RDF Views, Triplify, Ultrawrap, were built that would expose Relational Databases as RDF and even allow SPARQL to be executed directly on the relational database.

In October 2007, the W3C organized a workshop to discuss the interest of mapping relational databases to RDF: RDF Access to Relational Databases. The outcome of this workshop was the formation of the RDB2RDF Incubator Group in 2008. The objective of this group was to classify existing approaches to map relational databases to RDF and to then further decide if a standard was necessary. The Incubator Group had a face-to-face meeting in October 2008. The Incubator Group concluded its work with two deliverables: a Survey of Current Approaches for Mapping of Relational Databases to RDF and the RDB2RDF XG Final Report. The conclusion was to recommend the formation of a Working Group to standardize a mapping language.

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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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Remember how search engines can show nice snippets in their search results thanks to the structured data that webmasters embedded in the HTML of their webpages (RDFa, schema.org, etc)? Additionally, Facebook gains insight about user’s interest through structured data on webpages (i.e. Open Graph Protocol). Now there is a new kid on the block: Twitter.

Twitter Cards

Twitter recently introduced Twitter Cards, a way to “attach media experiences to Tweets that link to your content.” By adding structured data embedded in the HTML of your webpage, “users who Tweet links to your content will have a ‘card’ added to the Tweet that’s visible to all of their followers.” Basically, Twitter will now have a bit more of information about your webpage in order to know how to make a nice snippet in a tweet.

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This year was the 21st World Wide Web Conference located in Lyon, France. This conference is a unique forum for discussion about how the Web is evolving. There were hundreds of talks over 3 days. Let me summarize some Semantic Web presentations I was able to attend.

NautiLOD

Programmers daily use the wget tool to specify and retrieve data on the Web. However, wget is limited since it cannot dig into the semantics of Web data to do the job. What if you were to add semantics to wget? This is the question that Valeria Fionda, Claudio Gutierrez and Giuseppe Pirró asked themselves. They took that question to the next level: imagine a semantic wget on top of Linked Data. They wanted to create a language to declaratively specify portions of the Web of Data, define routes and instruct agents that can do things for you on the Web. All this by exploiting the semantics of information (RDF data) found in online data sources. For example, find all the Wikipedia pages of directors that have been influenced by Stanley Kubrick and send them to my email; retrieving information about David Lynch from different information providers only gives a hint of what can be done. The researchers developed a simple, generic declarative language, NautiLOD and implemented it in swget (semantic wget). swget comes in two flavors: a simple command line tool (to give the Web back to users) and a GUI. This is not a fantasy anymore. Check it our for yourself (http://swget.wordpress.com).

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