Rdf Converter

I cannot say it is the best tool and generally I would rmend a custom script in XSLT or your favorite programming language. But that said did you try this michaelbrunnbauer s

Here an extract from ConverterToRdf - W3C Wiki s CSV (Comma-Separated Values) See also Flat Files s#Flat_files and TSV s#TSV An RDF Extension is available for Google Refine . It can convert Excel CSV and other tabular data to RDF. The schema mapping can be defined in a graphical UI. RDF123 has Windows and Linux applications to download a Java application and servlet. XLWrap wraps spreadsheets (including cross tables) to arbitrary RDF graphs; supports Excel streamed processing local loading expressions similar to Excel Calc custom functions usage via API or SPARQL endpoint csv2rdf4lod uses declarative RDF enhancement parameters to specify how to transform tabular data into well-structured well-connected RDF. The tool uses identifiers for source italic organization dataset italic and version italic to establish default namespaces for all URIs created and provides VoID and provenance metadata as part of the conversion output. Tarql is amand-line application that converts CSV to RDF with a user-defined mapping. The mapping is written in standard SPARQL 1.1.

You can take a look at the Virtuoso Sponger Middleware that includes RDF Transformation Cartridges (Drivers) for more than 75 different data formats and APIs. 1 URIBurner Live Instance of the Virtuoso Sponger Middleware Service which you also get as part of your Virtuoso installation.

check out the group at the World Wide Web Consortium topic tid 36546 working on the RDB2RDF 1 standard. There are already tools supporting the R2R mappiing language for example Spyder 2. The RDB2RDF WG wiki should also contain an overview of existing tools for RDF-izing relational data (for example D2R 3 and Triplify 4) 1

Just trying to point the way and provide basic con with a survey of basic techniques in this answer. RDF stores are triple or quad stores. Both s store data as machine-readable graphs that include subjects predicates and objects. Quad stores as I understand them include the time dimension.n Subjects are entities nodes in the graphs. Predicates (the verbs) are relationships between the entities. the edges of the graphs. s are also entities. The time dimension consists of timestamps of instance data. If you're designing your own data collection process and generating your own data you'd have the luxury of disambiguating the entities during the process of tagging and RDFing the instance data as it's generated including unambiguous timestamping for data streams. But with datasets that others have generated you'repelled to work with what you have and use a blend of probabilistic methods (machine-generated guesswork) and (to the extent you have the time and resources) human disambiguation to add useful structure to the data. Maybe you leverage crowdsourced disambiguation methods such as DBpedia derived from Wikipedia or Wikidata depending on your data. It's an imperfect situation and the goals and objectives you're setting for your data should factor that imperfection in. Entity and relationship extraction services do exist and have improved over the years but they're hardly magic bullets. here's a sample extraction of a Wikipedia bio of Shakespeare I just generated nSource The Bluemix IBM Watson developer site IBM Watson Developer Cloud Find the entities 215 For data in batches conversion would consist of the followingn Extract the entities What is the best entity extraction API + service? question qid 238493 +-service Transforming the tagged data into RDF via a framework such as Silk GraphDB or similar The Linked Data Integration Framework GraphDB FAQ - GraphDB6 - Onto Wiki +FAQ ordered-list Stream processing of timestamped data requires a different and even less mature approach See RDF Stream Model s Libraries such as in Python exist for helping with the conversion too Connecting Python's RDFLib and Stardog s These might help cut to the chase. People much deeper than I am in the technology will have developed a more detailed and streamlined methodology for their purposes. I would follow people such as Daniel Koller user 1162136 on Quora or Twitter who are doing these sorts of things Resource Description Framework What is the best practice for versioning RDF information? question qid 99616 Other RDF-related answers on Quora will help you surface relevant vendors and SMEs to follow for example What is the best way to store and query with RDF OWL datasets having 1 Billion plus triples? question qid 2443669 Keep in mind that some cognitiveputing platforms specialize in ingesting less structured data and doing the transformations alluded to above with their own proprietary methods that may or may not include some variant of RDF or an equivalent. See Say Hello to IBM Watson n Digital Reasoning Other analytics platforms that ingest unstructured data may include non-RDF graph analytics such as Business Intelligence Reporting Tool | Q . These on their own can be provide a quick and flexible method of discovery. Here's why the RDFing process associated with the traditional semantic web is not sufficient for cognitiveputing The Real Trouble With Cognitive Computing However the semantic web continues to evolve. For more nascent adaptive approaches to semantic data modeling and intelligent system development generally Mike Bergman's posts are well worth reading AI3Adaptive Information Keep in mind that everyone working in this area will have a bias that reflects their focus and preferences.

Apache has a tool called Jena which can generate RDF files and Ontologies. Try that. It should work.