finally a bnode with a uri

Posts tagged with: linked data

Dynamic Semantic Publishing for any Blog (Part 2: Linked ReadWriteWeb)

A DSP proof of concept using ReadWriteWeb.com data.
The previous post described a generic approach to BBC-style "Dynamic Semantic Publishing", where I wondered if it could be applied to basically any weblog.

During the last days I spent some time on a test evaluation and demo system using data from the popular ReadWriteWeb tech blog. The application is not public (I don't want to upset the content owners and don't have any spare server anyway), but you can watch a screencast (embedded below).

The application I created is a semantic dashboard which generates dynamic entity hubs and allows you to explore RWW data via multiple dimensions. To be honest, I was pretty surprised myself by the dynamics of the data. When I switched back to the official site after using the dashboard for some time, I totally missed the advanced filtering options.



In case you are interested in the technical details, fasten your data seatbelt and read on.

Behind the scenes

As mentioned, the framework is supposed to make it easy for site maintainers and should work with plain HTML as input. Direct access to internal data structures of the source system (database tables, post/author/commenter identifiers etc.) should not be needed. Even RDF experts don't have much experience with side effects of semantic systems directly hooked into running applications. And with RDF encouraging loosely coupled components anyway, it makes sense to keep the semantification on a separate machine.

In order to implement the process, I used Trice (once again), which supports simple agents out of the box. The bot-based approach already worked quite nicely in Talis' FanHubz demonstrator, so I followed this route here, too. For "Linked RWW", I only needed a very small number of bots, though.

Trice Bot Console

Here is a quick re-cap of the proposed dynamic semantic publishing process, followed by a detailed description of the individual components:
  • Index and monitor the archives pages, build a registry of post URLs.
  • Load and parse posts into raw structures (title, author, content, ...).
  • Extract named entities from each post's main content section.
  • Build a site-optimized schema (an "ontology") from the data structures generated so far.
  • Align the extracted data structures with the target ontology.
  • Re-purpose the final dataset (widgets, entity hubs, semantic ads, authoring tools)

Archives indexer and monitor

The archives indexer fetches the by-month archives, extracts all link URLs matching the "YYYY/MM" pattern, and saves them in an ARC Store.

The implementation of this bot was straightforward (less than 100 lines of PHP code, including support for pagination); this is clearly something that can be turned into a standard component for common blog engines very easily. The result is a complete list of archives pages (so far still without any post URLs) which can be accessed through the RDF store's built-in SPARQL API:

Archives triples via SPARQL

A second bot (the archives monitor) receives either a not-yet-crawled index page (if available) or the most current archives page as a starting point. Each post link of that page is then extracted and used to build a registry of post URLs. The monitoring bot is called every 10 minutes and keeps track of new posts.

Post loader and parser

In order to later process post data at a finer granularity than the page level, we have to extract sub-structures such as title, author, publication date, tags, and so on. This is the harder part because most blogs don't use Linked Data-ready HTML in the form of Microdata or RDFa. Luckily, blogs are template-driven and we can use DOM paths to identify individual post sections, similar to how tools like the Dapper Data Mapper work. However, given the flexibility and customization options of modern blog engines, certain extensions are still needed. In the RWW case I needed site-specific code to expand multi-page posts, to extract a machine-friendly publication date, Facebook Likes and Tweetmeme counts, and to generate site-wide identifiers for authors and commenters.

Writing this bot took several hours and almost 500 lines of code (after re-factoring), but the reward is a nicely structured blog database that can already be explored with an off-the-shelf RDF browser. At this stage we could already use the SPARQL API to easily create dynamic widgets such as "related entries" (via tags or categories), "other posts by same author", "most active commenters per category", or "most popular authors" (as shown in the example in the image below).

Raw post structures

Named entity extraction

Now, the next bot can take each post's main content and enhance it with Zemanta and OpenCalais (or any other entity recognition tool that produces RDF). The result of this step is a semantified, but rather messy dataset, with attributes from half a dozen RDF vocabularies.

Schema/Ontology identification

Luckily, RDF was designed for working with multi-source data, and thanks to the SPARQL standard, we can use general purpose software to help us find our way through the enhanced assets. I used a faceted browser to identify the site's main entity types (click on the image below for the full-size version).

RWW through Paggr Prospect

Although spotting inconsistencies (like Richard MacManus appearing multiple times in the "author" facet) is easier with a visual browser, a simple, generic SPARQL query can alternatively do the job, too:

RWW entity types

Specifying the target ontology

The central entity types extracted from RWW posts are Organizations, People, Products, Locations, and Technologies. Together with the initial structures, we can now draft a consolidated RWW target ontology, as illustrated below. Each node gets its own identifier (a URI) and can thus be a bridge to the public Linked Data cloud, for example to import a company's competitor information.

RWW ontology

Aligning the data with the target ontology

In this step, we are again using a software agent and break things down into smaller operations. These sub-tasks require some RDF and Linked Data experience, but basically, we are just manipulating the graph structure, which can be done quite comfortably with a SPARQL 1.1 processor that supports INSERT and DELETE commands. Here are some example operations that I applied to the RWW data:
  • Consolidate author aliases ("richard-macmanus-1 = richard-macmanus-2" etc.).
  • Normalize author tags, Zemanta tags, OpenCalais tags, and OpenCalais "industry terms" to a single "tag" field.
  • Consolidate the various type identifiers into canonical ones.
  • For each untyped entity, retrieve typing and label information from the Linked Data cloud (e.g. DBPedia, Freebase, or Semantic CrunchBase) and try to map them to the target ontology.
  • Try to consolidate "obviously identical" entities (I cheated by merging on labels here and there, but it worked).
Data alignment and QA is an iterative process (and a slightly slippery slope). The quality of public linked data varies, but the cloud is very powerful. Each optimization step adds to the network effects and you constantly discover new consolidation options. I spent just a few hours on the inferencer, after all, the Linked RWW demo is just meant to be a proof of concept.

After this step, we're basically done. From now on, the bots can operate autonomously and we can (finally) build our dynamic semantic publishing apps, like the Paggr Dashboard presented in the video above.

Dynamic RWW Entity Hub

Conclusion

Dynamic Semantic Publishing on mainstream websites is still new, and there are no complete off-the-shelf solutions on the market yet. Many of the individual components needed, however, are available. Additionally, the manual effort to integrate the tools is no longer incalculable research, but is getting closer to predictable "standard" development effort. If you are perhaps interested in a solution similar to the ones described in this post, please get in touch.

Dynamic Semantic Publishing for any Blog (Part 1)

Bringing automated semantic page generation a la BBC to standard web environments.
"Dynamic Semantic Publishing" is a new technical term which was introduced by the BBC's online team a few weeks ago. It describes the idea of utilizing Linked Data technology to automate the aggregation and publication of interrelated content objects. The BBC's World Cup website was the first large mainstream website to use this method. It provides hundreds of automatically generated, topically composed pages for individual football entities (players, teams, groups) and related articles.

Now, the added value of such linked "entity hubs" would clearly be very interesting for other websites and blogs as well. They are multi-dimensional entry points to a site and provide a much better and more user-engaging way to explore content than the usual flat archives pages, which normally don't have dimensions beyond date, tag, and author. Additionally, HTML aggregations with embedded Linked Data identifiers can improve search engine rankings, and they enable semantic ad placement, which are attractive by-products.

Entity hub examples

The architecture used by the BBC is optimized for their internal publishing workflow and thus not necessarily suited for small and medium-scale media outlets. So I've started thinking about a lightweight version of the BBC infrastructure, one that would integrate more easily with typical web server environments and widespread blog engines.

How could a generalized approach to dynamic semantic publishing look like?

We should assume setups where direct access to a blog's database tables is not available. Working with already published posts requires a template detector and custom parsers, but it lowers the entry barrier for blog owners significantly. And content importers can be reused to a large extent when sites are based on standard blog engines such as WordPress or Movable Type.

The graphic below (large version) illustrates a possible, generalized approach to dynamic semantic publishing.
Dynamic Semantic Publishing

Process explanation:
  • Step 1: A blog-specific crawling agent indexes articles linked from central archives pages. The index is stored as RDF, which enables the easy expansion of post URLs to richly annotated content objects.
  • Step 2: Not-yet-imported posts from the generated blog index are parsed into core structural elements such as title, author, date of publication, main content, comments, Tweet counters, Facebook Likes, and so on. The semi-structured post information is added to the triple store for later processing by other agents and scripts. Again, we need site (or blog engine)-specific code to extract the various possible structures. This step could be accelerated by using an interactive extractor builder, though.
  • Step 3: Post contents are passed to APIs like OpenCalais or Zemanta in order to extract stable and re-usable entity identifiers. The resulting data is added to the RDF Store.
  • After the initial semantification in step 3, a generic RDF data browser can be used to explore the extracted information. This simplifies general consistency checks and the identification of the site-specific ontology (concepts and how they are related). Alternatively, this could be done (in a less comfortable way) via the RDF store's SPARQL API.
  • Step 4: Once we have a general idea of the target schema (entity types and their relations), custom SPARQL agents process the data and populate the ontology. They can optionally access and utilize public data.
  • After step 4, the rich resulting graph data allows the creation of context-aware widgets. These widgets ("Related articles", "Authors for this topic", "Product experts", "Top commenters", "Related technologies", etc.) can now be used to build user-facing applications and tools.
  • Use case 1: Entity hubs for things like authors, products, people, organizations, commenters, or other domain-specific concepts.
  • Use case 2: Improving the source blog. The typical "Related articles" sections in standard blog engines, for example, don't take social data such as Facebook Likes or re-tweets into account. Often, they are just based on explicitly defined tags. With the enhanced blog data, we can generate aggregations driven by rich semantic criteria.
  • Use case 3: Authoring extensions: After all, the automated entity extraction APIs are not perfect. With the site-wide ontology in place, we could provide content creators with convenient annotation tools to manually highlight some text and then associate the selection with a typed entity from the RDF store. Or they could add their own concepts to the ontology and share it with other authors. The manual annotations help increase the quality of the entity hubs and blog widgets.

Does it work?

I explored this approach to dynamic semantic publishing with nearly nine thousand articles from ReadWriteWeb. In the next post, I'll describe a "Linked RWW" demo which combines Trice bots, ARC, Prospect, and the handy semantic APIs provided by OpenCalais and Zemanta.

ESWC 2009 Linked Data Dashboards

A first Paggr application went live during ESWC2009.
In case you missed the tweets or a local announcement: The first Paggr application went online a few days ago. This year's ESWC Technologies Team pushed things a little further, with RFID tracking during the event and extended conference data that includes detailed session and date/time information (kudos to Michael Hausenblas for RDFizing even PDFs).

Based on this dataset, we provided a conference explorer and stress-tested the "Dog Food" server while at it. The system survived, but I also learned a lot. We used about 50 RDF stores for the different public and user-specific dashboards, which basically worked nicely. However, rendering non-ugly resource summaries requires a bit of endpoint hammering, and some of the more complex path queries resulted in timeouts. Yesterday, I had to create a mirror from the data dump to route a couple of widgets through a replicated (ARC :-) endpoint. But then this is also one of the powerful possibilities that come with semantic web technologies. You can often switch or double the back-end repository in no time, and without any code changes. (And as all the Sparqlets are created in a web-based tool, I didn't even have to upload a changed configuration file. I simply tweaked a SPARQLScript parameter.)

Anyway, there are a couple of public dashboards, in case you'd like to give it a try (it's still an early version), I also embedded a short screencast below. The system is going to be moved to a DERI server when the conference is over, but the URIs and data will probably stay stable. (And no, it won't really work with IE yet.) More to come!



HQ version (quicktime, 110MB)

Simple RDFication of SPARQL SELECT results with RDFa

How to use RDFa to make SELECT results locally available as RDF
A couple of weeks ago, I've written about the self-enforcing value spiral that RDF data enables. Here is an example about how RDFa can be used to support this "Repurpose-Republish" loop.

While data exchange between different semantic web sources is usually RDF-based (i.e. the data always maintain their semantics), there is one major exception: SPARQL SELECT queries. This developer-oriented operation returns tabular data (similar to record sets in SQL). Once the query result is separated from the query, the associated structural data is lost. You can't directly feed SELECT results back into a triple store, even though querying based on linked resources means that you have just created knowledge. It's a pity to show this generated information to human consumers only.

One of the demos at my NYC talk was a dynamic wiki item that pulled in competitor information from Semantic CrunchBase and injected that into a page template as HTML. The existing RDF infrastructure does not let me cache the SELECT results locally as usable RDF. And a semantic web client or crawler that indexes the wiki page will not learn how the described resource (e.g. Twitter) is related to the remote, linked entities.

wiki with linked data

However, by simply adding a single RDFa hook to the wiki item template, the RDF relation (e.g. competitor) can be made available again to apps that process my site content. This is basically how Linked Data works. But here is the really nifty thing: My site can be a consumer of its own pages, too, recursively enriching its own data.

markup-to-SELECT-to-RDFa-to-RDF

I tweaked the wiki script which now works like this: When the page is saved, a first operation updates the wiki markup in the page's graph (i.e. the not-yet-populated template). In a second step, the page URL is retrieved via HTTP. This will return HTML with RDFa-encoded remote data, which is then parsed by ARC, and finally added to the same graph. We end up with a graph that does not only contain the wiki markup, but also the RDFized information that was integrated from remote sites. After adding this graph to the RDF store, we can use a local query to generate the page and occasionally reset the graph to enable copy-by-reference. And all this without any custom API code.

rdfa-to-sparql

RDF/SPARQL-based web development for PHP coders: Meetup presentation and workshop in NYC

I'll give a talk and run a workshop in New York City in May.
The Linked Data meme is spreading and we have strong indications that web developers who understand and know how to apply practical semantic web technologies will soon be in high demand. Not only in enterprise settings but increasingly for mainstream and agency-level projects where scripting languages like PHP are traditionally very popular.

I can't really afford travelling to promote the interesting possibilities around RDF and SPARQL for PHP coders, so I'm more than happy that Meetup master Marco Neumann offered me to come over to New York and give a talk at the Meetup on May 21st. Expect a fun mixture of "Getting started" hints, demos, and lessons learned. In order to make this trip possible, Marco is organizing a half-day workshop on May 22nd, where PHP developers will get a hands-on introduction to essential SemWeb technologies. I'm really looking forward to it (and big thanks to Marco).

So, if you are a PHP developer wondering about the possibilities of RDF, Linked Data & Co, come to the Meetup, and if you also want to get your hands dirty (or just help me pay the flight ticket ;) the workshop could be something for you, too. I'll arrive a few days earlier, by the way, in case you want to add another quaff:drankBeerWith triple to your FOAF file ;)

Paggr screencast: Linked Data Widget Builder

A screencast about Paggr's sparqlet builder.
Running an R&D-heavy agency in the current economical climate is pretty tough, but there are also a couple of new opportunities for these semantic solutions that help reduce costs and do things more efficiently. I'm finally starting to get project requests that include some form of compensation. Not much yet (all budgets seem to be very tight these days), but it's a start, and together with support from Susanne, I could now continue working on Paggr, semsol's Netvibes-like dashboard system for the growing web of Linked Data.

An article about Paggr will be in the next Nodalities Magazine, and the ESWC2009 technologies team is considering a custom system for attendees which is a great chance to maybe get other conference organizers interested. (I see much potential in a white-label offering, but a more mainstream-ish version for Web 2.0 data is still on my mind. Just have to focus on getting self-sustained first.)

Below is a short screencast that demonstrates a first version of the sparqlet (= semantic widget) builder. I've de-coupled sparqlet-serving from the dashboard system, so that I'll be able to open-source the infrastructure parts of Paggr more easily. Another change from the October prototype is the theme-ability of both dashboards and widget servers. Lots of sun, sky, and sea for ESWC ;-)



HQ version (quicktime, 120MB)

The Linked Data Value Spiral

The value of Linked Data grows when it's utilized an re-distributed
I'm currently writing an article about paggr for the Nodalities Magazine. As there is not too much to write about yet, I'm focusing on the basic idea (customizable Linked Data dashboards), its inspiration (TimBL's RDF Clipboard concept), enabling technologies and trends (Live Clipboard, widgets, AJAX homepages, sub-page-level interaction), and the user interface challenges related to generic interaction with Linked Data.

One thing that I thought might be worth sharing separately is the "Linked Data Value Spiral" below. It tries to illustrate that semantic data don't have a single-loop life cycle, but that re-distributing utilized ( = newly meshed/combined) information will create a self-enforcing "Linked Data ecosystem". I tried to associate the individual value creation processes with SemWeb market sectors. (RDF stores, for example, are typical information organization products, paggr tries to remove the bottleneck between utilization and re-distribution, etc.)

Linked Data Value Spiral

It's just an abstraction, the boundaries are of course blurry (a SPARQL endpoint can help with both utilization and discovery), but I still find the simple spiral and its segments handy to classify current products and companies. It even helped me a little to identify market opportunities and gaps:
  • The recent VoiD effort could have a significant impact on the whole Semantic Web progress.
  • Entity extraction providers like Zemanta and OpenCalais could play a huge role to boost creation processes.
  • What about "accelerator" products that offer a shortcut between utilization and creation (i.e. apps that create Linked Data while you are using them, with instant re-distribution)?
  • Is it a problem when a service like Freebase exports RDF but doesn't provide links to external datasets?
  • ...

Feel free to use and share.

Semantic Web by Example: Semantic CrunchBase

CrunchBase is now available as Linked Data including a SPARQL endpoint and a custom API builder based on SPARQLScript.
Update: Wow, these guys are quick, there is now a full RSS feed for CrunchBoard jobs. I've tweaked the related examples.

This post is a bit late (I've even been TechCrunch'd already), but I wanted to add some features before I fully announce "Semantic CrunchBase", a Linked Data version of CrunchBase, the free directory of technology companies, people, and investors. CrunchBase recently activated an awesome API, with the invitation to build apps on top of it. This seemed like the ideal opportunity to test ARC and Trice, but also to demonstrate some of the things that become possible (or much easier) with SemWeb technology.

Turning CrunchBase into a Linked Dataset

The CB API is based on nicely structured JSON documents which can be retrieved through simple HTTP calls. The data is already interlinked, and each core resource (company, person, product, etc.) has a stable identifier, greatly simplifying the creation of RDF. Ideally, machine-readable representations would be served from crunchbase.com directly (maybe using the nicely evolving Rena toolkit), but the SemWeb community has a reputation of scaring away maintainers of potential target apps with complicated terminology and machinery before actually showing convincing benefits, so, at this stage (and given the nice API), it might make more sense to start with a separate site, and to present a selection of added values first.

For Semantic CrunchBase, I wrote a largely automated JSON2RDF converter, i.e. the initial RDF dataset is not using any known vocabs such as FOAF (or FOAFCorp). (We can INSERT mapping triples later, though.) Keeping most of the attribute names from the source docs (and mainly using just a single namespace) has another advantage besides simplified conversion: CrunchBase API users can more easily experiment with the SPARQL API (see twitter.json and twitter.rdf for a direct comparison).

An important principle in RDF land is the distinction between a resource and a page about a resource (it's very unlikely to hear an RDFer say "URLs are People" ;). This means that we need separate identifiers for e.g. Twitter and the Twitter description. There are different approaches, I decided to use (fake-)hash URIs which make embedding machine-readable data directly into the HTML views a bit more intuitive (IMHO):
  • /company/twitter#self denotes the company,
  • GETing the identifier resolves to /company/twitter which describes the company.
  • Direct RDF/XML or RDF/JSON can be retrieved by appending ".rdf" to the document URIs and/or via Content Negotiation.
This may sound a bit complicated (and for some reason RDFers love to endlessly discuss this stuff), but luckily, many RDF toolkits handle much of the needed functionality transparently.

The instant benefit of having linked data views is the possibility to freely explore the complete CrunchBase graph (e.g. from a company to its investors to their organizations to their relations etc.). However, the CrunchBase team has already done a great job, their UI already supports this functionality quite nicely, the RDF infrastructure doesn't really add anything here, functionality-wise. There is one advantage, but it's not obvious: An RDF-powered app can be extended at any time. On the data-level. Without the need for model changes (because there is none specified). And without the need for table tweaks (the DB schema is generic). We could, for example, enhance the data with CrunchBoard Jobs, DBPedia information, or profiles retrieved from Google's Social Graph API, without having to change a single script or table. (I switched to RDF as productivity booster some time ago and never looked back. The whole Semantic CrunchBase site took only a few person days to build, and most of the time was spent on writing the importer.) But let's skip the backstage benefits for now.

SPARQL - SQL for the Web

Tim Berners-Lee recently said that the success of the Semantic Web should be measured by the "level of unexpected reuse". While the HTML-based viewers support a certain level of serendipitous discovery, they only enable resource-by-resource exploration. It is not possible to spot non-predefined patterns such as "serial co-founders", or "founders of companies recently acquired". As an API provider, it is rather tricky to anticipate all potential use cases. On the CB API mailing list, people are expressing their interest in API methods to retrieve recent investments and acquisitions, or social graph fragments. Those can now only be coded and added by the API maintainers. Enter SPARQL. SPARQL, the protocol and query language for RDF graphs provides just this: flexibility for developers, less work for API providers. Semantic CrunchBase has an open SPARQL endpoint, but it's also possible to restrict/control the API while still using an RDF interface internally to easily define and activate new API methods. (During the last months I've been working for Intellidimension; they were using an on-request approach for AJAX front-ends. Setting up new API methods was often just a matter of minutes.)

With SPARQL, it gets easy to retrieve (almost) any piece of information, here is an example query that finds companies that were recently acquired:
SELECT DISTINCT ?permalink ?name ?year ?month ?code WHERE {
    ?comp cb:exit ?exit ;
          cb:name ?name ;
          cb:crunchbase_url ?permalink .

    ?exit cb:term_code ?code ;
          cb:acquired_year ?year ;
          cb:acquired_month ?month .
}
ORDER BY DESC (?year) DESC (?month)
LIMIT 20
(Query result as HTML)

Or what about a comparison between acquisitions in California and New York:
SELECT DISTINCT COUNT(?link_ca) as ?CA COUNT(?link_ny) as ?NY WHERE {
    ?comp_ca cb:exit ?exit_ca ;
             cb:crunchbase_url ?link_ca ;
             cb:office ?office_ca .
    ?office_ca cb:state_code "CA" .

    ?comp_ny cb:exit ?exit_ny ;
             cb:crunchbase_url ?link_ny ;
             cb:office ?office_ny .
    ?office_ny cb:state_code "NY" .
}
(Results)

These are just some simple examples, but they (hopefully) illustrate how RDF and SPARQL can significantly improve Web app development and community support. But hey, there is more.

Semantic Mashups with SPARQLScript

SPARQL has only just become a W3C recommendation, and the team behind it was smart enough to not add too many features (even the COUNT I used above is not part of the core spec). The community is currently experimenting with SPARQL extensions, and one particular thing that I'm personally very interested in is the creation of SPARQL-driven mashups through something called SPARQLScript (full disclosure: I'm the only one playing with it so far, it's not a standard at all). SPARQLScript enables the federation of script block execution across multiple SPARQL endpoints. In other words, you can integrate data from different sources on the fly.

Imagine you are looking for a job in California at a company that is at a specific funding stage. CrunchBase knows everything about companies, investments, and has structured location data. CrunchBoard on the other hand has job descriptions, but only a single field for City and State, and not the filter options to match our needs. This is where Linked Data shines. If we find a way to link from CrunchBoard to CrunchBase, we can use Semantic Web technology to run queries that include both sources. And with SPARQLScript, we can construct and leverage these links. Below is a script that first loads the CrunchBoard feed of current job offers (only the last 15 entries, due to common RSS' limitations/practices, the use of e.g. hAtom could allow more data to be pulled in). In a second step, it uses the company name to establish a pattern join between CrunchBoard and CrunchBase, which then allows us to retrieve the list of matching jobs at (at least) stage-A companies with offices in California.
PREFIX cboard: <http://www.crunchboard.com>
ENDPOINT <http://cb.semsol.org/sparql>
# refresh feed
if (${GET.refresh}) {
 # replaced <http://feeds.feedburner.com/CrunchboardJobs> with full feed
 LOAD <http://www.crunchboard.com/rss/affiliate/crunchboardrss_all.xml>
}
# let's query
$jobs = SELECT DISTINCT ?job_link ?comp_link ?job_title ?comp_name WHERE {
  # source: crunchboard, using full feed now
  GRAPH <http://www.crunchboard.com/rss/affiliate/crunchboardrss_all.xml> {
    ?job rss:link ?job_link ;
         rss:title ?job_title ;
         cboard:company ?comp_name .
  }
  # source: full graph
  ?comp a cb:Company ;
        cb:name ?comp_name ;
        cb:crunchbase_url ?comp_link ;
        cb:office ?office ;
        cb:funding_round ?round .
  ?office cb:state_code "CA" .
  ?round cb:round_code "a" .
}
(You can test it, this really works.)

Now that we are knee-deep in SemWeb geekery anyway, we can also add another layer to all of this and
  • allow parameterized queries so that the preferred state and investment stage can be freely defined,
  • add a browser-based tool for the collaborative creation of custom API calls
  • add a template mechanism for human-friendly results

I'll write about this "Pimp My API" app at Semantic CrunchBase in the next post. Here are some example API calls that were already created with it:
A lot of fun, more to come.

DriftR Linked Data Browser and Editor (Screencast)

A screencast of DriftR, an RDF browser/editor for Trice
While I'm unfortunately struggling to find paid projects these days, I had at least some time to work on core technology for my Trice framework and a new knowee release. The latest module is an in-browser RDF viewer and editor for Linked Data, heavily inspired by the freebase UI (hopefully with less screen flickering, though).

I'm clearly not there yet, but today I uploaded a screencast (quicktime 4MB), and I think I can start incorporating it into the knowee tools soon. Have fun watching it if you like, and Merry X-Mas!

DriftR Screencast

Archives/Search

YYYY or YYYY/MM
No Posts found

Feeds