Archive

Posts Tagged ‘lewis’

Java Semantic & Linked Open Data webapps – Part 5.1

January 18, 2011 1 comment

How to Architect ?

Well – what before how  – this is firstly about requirements, and then about treatment

Linked Open Data app

Create a semantic repository for a read only dataset with a sparql endpoint for the linked open data web. Create a web application with Ajax and html (no server side code) that makes use of this data and demonstrates linkage to other datasets. Integrate free text search and query capability. Generate a data driven UI from ontology if possible.

So – a fairly tall order : in summary

  • define ontology
  • extract entites from digital text and transform to rdf defined by ontology
  • create an RDF dataset and host in a repository.
  • provide a sparql endpoint
  • create a URI namespace and resolution capability. ensure persistence and decoupling of possible
  • provide content negotiation for human and machine addressing
  • create a UI with client side code only
  • create a text index for keyword search and possibly faceted search, and integrate into the UI alongside query driven interfaces
  • link to other datasets – geonames, dbpedia, any others meaningful – demonstrate promise and capability of linkage
  • build an ontology driven UI so that a human can navigate data, with appropriate display based on type, and appropriate form to drive exploration

Here’s what we end up

Lewis Topographical Dictionary linked data app - system diagram

  1. UserAgent – a browser navigates to Lewis TDI homepage – http://uoccou.endofinternet.net:8080/resources/sparql – and
  2. the webserver (tomcat in fact) returns html and javascript. This is the “application”.
  3. interactions on the webpage invoke javascript that either makes direct calls to Joseki (6) or makes use or permanent URIs (at purl.org) for subject instances from the ontology
  4. purl.org redirects to dynamic dns which resolves to hosted application – on EC2, or during development to some other server. This means we have permanent URIs with flexible hosting locations, at the expense of some network round trips – YMMV.
  5. dyndns calls EC2 where a 303 filter intersects to resolve to either a sparql (6) call for html, json or rdf. pluggable logic for different URIs and/or accept headers means this can be a select, describe, or construct.
  6. Joseki as a sparql endpoint provides RDF query processing with extensions for freetext search, aggregates, federation, inferencing
  7. TDB provides single semantic repository instance (java, persistent, memory mapped) addressable by joseki. For failover or horizontal scaling with multiple sparql endpoints SDB should probably be used. For vertical scaling at TDB – get a bigger machine ! Consider other repository options where physical partitioning, failover/resilience or concurrent webapp instance access required (ie if youre building a webapp connected to a repository by code rather than a web page that makes use of a sparql endpoint).

Next article will provide similar description or architecture used for the Java web application with code that is directly connected to a repository rather than one that talks to a sparql endpoint.

Java Semantic & Linked Open Data webapps – Part 4

December 17, 2010 Comments off

What needs writing ?

Now that we have an idea about what tools and technologies are available and the kind of application we want to build we need to start considering architecture and what code we will write around those tools and technologies. The architecture I planned was broadly formed – but not completely – as I went about creating these applications. I was also going to tackle the Linked Open Data webapp first and then do the Semantic Backed J2EE app. I thought MVC first for both, but went in the end with a 2 tier approach for the former, and an n-tier component based approach for the latter. (More about this in the next section). I’m used to the Spring framework, so I thought I’d go with it, and for UI I’d use jQuery and HTML and/or JSP, perhaps Velocity. But nothing was set in stone, and I was going to try and explore and be flexible.

The tools and technologies cover

  • creating an ontology
  • entity extraction
  • RDF generation
  • using RDF with Java
  • Semantic repositories
  • querying sparql end points
    • inference
    • linking data
  • UI and render
Category Linked Open Data webapp Semantic Backed J2EE webapp
creating an ontology The ontology was going to be largely new as there is not much about to deal with historical content. Some bibliograpic ontologies are out there, but this isn’t about cataloguing books or chapters, but about the content within and across the sections in a single book. There are editions for Scotland, Wales and UK also, so I might get around to doing them at some stage. Some of the content is archaic – measurements are in Old English miles for instance. Geographic features needed to be described, along with population and natural resourcces. I wasn’t sure if I needed the expressiveness of OWL over RDFS, but thought that if I was going to start something fresh I might as well leave myself open to evolution and expansion – so OWL was the choice. Some editors dont to OWL, and in the end I settled for Protege. Same thoughts here as for the Linked Data app – why limit myself to RDFS ? I can still do RDFS within an OWL ontology. Protege it is
entity extraction Having played with GATE, OpenNLP, MinorThird and a foray into UIMA I settled on writing my own code. I needed close connections between my ontology, extracting the entities and generating RDF from those entities – most of these tools dont have this capability out of the box (perhaps they do now, 1 year on) – and I also wanted to minimise the number of independent steps at this point so that I could avoid writing conversion code, configure multiple parts in different ways and for different environments or OS. There is also a high barrier to entry and a long learing curve for some of these tools. I had read a lot, enough even, and wanted to get my hands dirty. I decided to build my own, based on grep – as most of these tools use regex at the bottom end and build upon it . It wasn’t going to be sophisticated, but it would be agile, best effort, experience based coding I’d be doing, and learning all the way – not a bad approach I think. I’d borrow techniques from the other tools around tokenisation and gazeteering, and if I was lucky, I might be able to use some of the ML libraries (I didnt in the end). So, with the help of Jena, I wrote components for

  • Processing files in directories using “tasks”, outputting to a single file, multiple files, multiple directories, different naming conventions, encoding, different RDF serialisations
  • Splitting single large file into sections based on a heading style used by the author. This was complicated by page indexing and numbering that a very similar style, and variations within sections that meant that end-of-section was hard to find. I got most entries out, but from time to time I find and embedded section wthin another. This can be treated individually, manually, and reimported into the repository to replace the original and create 2 in its place
  • Sentence tokenisation – I could have used some code from the available libraries and frameworks here, but its not too difficult, and when I did compare to the others eventually, I discovered that they also came a cropper in the same areas I did. Some manual corrections are still needed no matter how you do it, so I stuck with my own
  • Running regex patterns, accumulating hits in a cache. A “concept” or entity has a configuration element, and a relationship to other elements (a chain can be created).
    • The configuration marries an “Entity” with a “Tag”(URI). Entities are based on a delimiter, gazeteer.
    • Entities can be combined if they have a grouping characteristic.
    • An Entity can be “required” meaning that unless some “other” token is found in a sentence, the entity wont be matched. This can also be extended to having multiple required or ancialliary matches, so that a proportion need to be found (a likelihood measure) before an entity is extracted.
    • Some Entities can be non-matching – just echo whatever is in the input – good for debug, and for itemising raw content – I use this for echoing the sentences in the section that Im looking at – the output appears alongside the extracted entities.
    • The Required characteristic can also be used with Gazeteer based greps.
    • Entities have names that are used to match to Tags
  • Creating a Jena Model and adding those entities based on a configured mapping to an ontology element (URI, namespace, nested relationship, quantification (single or list, list type)
  • Outputting a file or appending to a file, with a configured serialisations scheme (xml/ttl/n3/…)
This was a different kind of application – here no data exists at the start, and all is created and borne digital. No extraction needed.
RDF generation I naively started the RDF generation code as a series of string manipulations and concatenations. I thought I could get away with it, and that it would be speedy ! The RDF generation code in Jena didnt seem particularly sophisticated – the parameters are string based in the end, and you have to declare namespaces as a string etc so what could possible go wrong ?? Well, things got unwieldy, and when I wanted to validate, integrate and reuse this string manipulation code it became tedious and fractious. Configuration was prone to error. Jena at higher stages of processing then needs proper URIs and other libraries operate on that basis. So, just in time, I switched – luckily I had built the code thinking that I might end up having to alter my URI definition and RDF generation strategy, so it ended up being a discrete replacement – a new interface implementation that I could plug in.
Tags can be

  • reference – always create the same URI – used with properties mostly – eg rdfs:type
  • append – a common and complete base, with just a value appended
  • complex – a base uri, intermediate path, ns prefix, type or subject path, a value URI different from the containing element
  • lookup – based on entity value, return a particular URI – like a reverse gazeteer
Here, RDF generation isnt driven by extraction or preexisting entites, but by the Object model I used. See the next row for details.
Using RDF with Java Fairly early on I settled with Jena as opposed to Sesame. There are some notes I found comparing Jena to Sesame1, but some of the arguments didnt mean anything to me at the early stages. There wasnt much between them I thought, but the Jena mailing list seemed a bit more active, and I noted Andy Seaborne’s name on the Sparql working group2. Both are fully featured with Sparql endpoints, repositories, text search and so on, but take different approaches3 . Since then I’ve learned a lot of course, and Ive compiled my own comparison matrix[110]. . So – I went for Jena, and I probably will in other cases, but Sesame may suit things better in others.

While Jena is Object oriented, working with it is based on RDF rather than objects. So if you have a class with properties – a bean – you have to create a Model, the Subject and add the properties and their values, along with the URIs and namespaces that they should be serialised with. You cannot hand Jena a Bean and say “give me the RDF for that object”.

For this project that wasn’t an issue – I wasnt modelling a class hierarchy, I wanted RDF from text, and then to be able to query it, and perhaps use inference. Being able to talk to Sparql endpoints and manipulate RDF was more important than modelling an Object hierarchy.

1. http://www.openrdf.org/forum/mvnforum/viewthread?thread=2043#7470
2. http://www.w3.org/2009/sparql/wiki/User:Andy_Seaborne
3. Theyre different because they can be  – this isn’t like programming against a standard like JDBC, there isnt a standard way of modelling RDF in Java or as an Object – there are domain differences that may well make that impossible, in entirety. Multiple inheritance, restrictions and Open World Assumption make for mismatches. ProLog and LISP may be different or more suited here, or perhaps some other language.

Here I needed to be able maintain parallel worlds – and Object base with a completely equivalent RDF representation. And I wanted to be able to program this from an enterprise Java developer’s perspective, rather than a logician or information analyst. How do I most easily get from Object to RDF without having to code for each triple combination [109]? Well it turns out there are 2 choices, and I ended up using one and then the other. It was also conceivable that I might not be able to do what I wanted, or that it wouldnt perform – I saw the impact of inference on query performance in the Linked Data application – so I wanted to code the app so that it would be decoupled from the persistence mechanism. I also needed to exert authorization control – could I do this with RDF ?

  • Java-RDF – I stuck with Jena – why give up a good thing ?
  • Object-RDF – Jena has 2 possibilties – JeanBean, and Jastor. I settled for JenaBean as it seemed to have support and wasnt about static class generation. This allows you to annotate your javabeans with URI and property assertions so that a layer of code can create the RDF for you dynamically, and then do the reverse when you want to query.
  • AdHoc Sparql – the libraries work OK when you are asking for Objects by ID, but if you want Objects that have certain property values orconditions then you need to write Sparql and submit that to the library.

So, I could build my app in an MVC style, and treat the domain objects much like I would if I used Hibernate or JDO say. In addition, I could put in a proxy layer so that the services werent concerned about which persistence approach I took – if I wanted, I could revert to traditional RDBMS persistence if I wanted. So I could haveView code, controllers, domain objects (DAO), service classes, a persistence layer consisting of a proxy and an Object to RDF implemenation.

I built this, and soon saw that RDF repositories, in particular Jena SDB, when used with JenaBean are slow. This boils down to the fact that SPARQL ultimatey is translated to SQL, and some SPARQL operations have to be performed client side. When you do this in an Object to RDF fashion, where every RDF statement ends up as a SQL join or independent query, you get a very very chatty storage layer. This isn’t uncommon in ORM land and lazy loading is used so that for instance, a property isnt retrieved until its actually needed – eg if a UI action needs to show a particular object property in addition to showing that an object exists. In the SPARQL case, there are more things that need to be done client side, like filtering, and this means that a query may retrieve (lots) more information than its actually going to need to create a query solution, and the processing of the solution is going to take place in your application JVM and not in the repository.

I wanted then to see if the performance was significantly better with a local repository even if it couldnt be addressed from multiple application instances (TDB), and if Sesame was any better. TDB turned out to be lots faster, but of course you cant have multiple webapps talking to it unless you use address it as a Sparql endpoint- not an Object in Java code. For Sesame tho, I needed to ditch JenaBean, and luckily, in the time I had been building the application a new Java Object-RDF middleware came out, called Empire-JPA[72].

This allows you to program your application in much the same way as JeanBean – annotations and configuration – but uses the JPA api to persist objects to a variety of backends. So I could mark up my beans with Empire Annotations (leaving the JenaBean ones in place) and in theory persist the RDF to TDB, SDB, any of the Sesame backends, FourStore and so on.

The implementation was slowed down because the SDB support wasn’t there, and the TDB support needed some work, but it was easy to work Mike Grove at ClarkParsia on this, and it was a breath of fresh air to get some good helpful support, an open attitude, and timely responses.

I discovered along the way that I couldn’t start with a JenaBean setup, persist my objects to TDB say, and switch seamlessly to Empire-JPA (or vice versa). It seems that JenaBean persists some configuration statements and these interfere with Empire in some fashion – but this is an unlikely thing to do in production, so I havent followed it thru.

Empire is also somewhat slower than JenaBean when it comes to complex object hierarchies, but Mike is working on this, and v 0.7 includes the first tranche of improvements.

Doing things with JPA has the added benefit of giving you the opportunity to revert to RDBMS or to start with RDBMS and try out RDF in parts, or do both. It also means that you have lots of documentation and patterns to follow, and you can work with a J2EE standard which you are familiar with.

But, in the end Semantic Repositories aren’t as quick as SQL-RDBMS, but if you want RDF storage for some of your data or for a subset of your functionality, a graph based dataset, a common schema, vocabulary (or parts of) for you and other departments or companies in your business circle, and the distinct advantage of inference for data mining, relationship expressiveness (“similar” or other soft equivalences rather than just “same”) and discovery.

A note about authorization (ACL) and security: None of the repositories I’ve come across have access control capabilities along the lines of what you might see with an RDBMS – grant authorities and restrictions just aren’t there. (OpenVirtuoso may have something as it has a basis in RDBMS (?)).

You might be able to do some query restriction based on graphs by making use of a username, but if you want to say make sure that a field containing a social securrty number is only visible to the owner or application administrator (or some other Role) but not to other users, then you need to do that ACL at the application level. I did this in Spring with Spring Security (Acegi), at the object level. Annotations and AOP can be used to set this up for Roles, controllers, Spring beans (that is beans under control of a Spring context) or beans dynamically created (eg Domain objects created by controllers) . ACL and authentication in Spring depend on a User definition, so I also had to create an implementation that retrieved User objects from the semantic repository, but once that was done, it was an ACL manipulation problem rather than an RDF one.

The result was a success, if you can ignore the large dataset performance concerns. A semantic respository can easily and successfully be used for persistence storage in a Java J2EE application built around DAO, JPA and Service patterns, with enterprise security and access control, while also providing a semantic query capability for advanced and novel information mining, discovery and exploration.

Semantic repositories This application ultimately needs to be able to support lots of concurrent queries – eg +20 per sec, per instance. Jena uses Multiple Reader Single Writer approach for this, so should be fine. But with inference things slow down a lot, and memory needs to be available to service concurrent queries and datasets. The Amazon instance I have for now uses a modest 600mB for Heap, but with inference could use lots more, and a lot of CPU. Early on I used a 4 year old Dell desktop to run TDB and Joseki, and queries would get lost in it and never return – or so I thought. Moving to a Pentium Duo made things better, but its easy to write queries that tie up the whole dataset when youre not a sparql expert and can in some cases can cause the JVM to OoM and/or bomb. SDB suffers (as mentioned in the previous section) and any general purpose RDBMS hosted semantic repository that has to convert from SPARQL to SQL and back-and-forth will have performance problems. But for this application, TDB currently suffices – I dont have multiple instances of a Java application and if did host the html/js on another instance (a tomcat cluster say) then it would work perfectly well with Joseki in front of TDB or SDB. On the downside, an alternative to Jena is not a real possibility here as the Sparql in the pagecode makes heavy use of Jena ARQ extensions for counts and other aggregate functions. Sparql 1.1 specifies these things, so perhaps in future it will be a possibility. As a real java web application one of the primary requirements here is that the repository is addressable using java code from multiple instances1. TDB doesnt allow this because you define it per JVM. Concurrent access leads to unpredictable results, to put it politely. SDB would do it, as the database takes care of the ACIDity, but its slow.

I also wanted to be able to demonstrate the application and test performance with RDBMS technology or Semantic Repository, or indeed NoSQL technology. The class hierarchy and componentisation allows this, but at this stage I’ve not tried going back to RDBMS or the NoSQL route. Empire-JPA allows a variety of repositories to be used, and those based on Sesame include OWLIM and BigData which seem to offer large scale and clustered repository capability. To use AllegroGraph or Rdf2Go would require another implementation of my Persitence Layer, and may require more bean annotations.

So, nothing is perfect, everything is “slow”, but flexibility is available.

1. It might be possible to treat the repository as remote datasource and use SPARQL Select and Insert/Update queries (and this may be more performant it turns out), but for this excerise I wanted to stick with tradition and build a J2EE application that didnt have hard coded queries (or externalised and mapped ones a la iBatis) but that encapsulated the business logic and entity as bean and service object base.

  • querying sparql end points
  • inference
  • linking data
More important here than in the J2EE webapp, being able to host a dataset on the Linked Data Web with 303 Redirect, permanent urls, slash rather than hash URIs and content negotiation meant that I ended up with Joseki as the Sparql endpoint, and a servlet filter within a base webapp that did the URI rewriting, 303 redirect and content negotiation. Ontology and instance URIs can be serviced by loading the Ontology into the TDB repository. The application is read only, so theres no need for the Joseki insert/update servlet. I also host an ancillariy dataset for townlands so that I can keep it distinct for use with other applications, but federate in with an ARQ Service keyword. Making links between extracted entities and geoNames, dbPedia and any other dataset is done as a decorator object in the extraction pipeline. Jena’s SPARQL objects are used for this, but in the case of the Geonames webservice, their Java client library is used.

One of the issues here of course is cross-domain scripting. Making client side requests to code from another domain (or making Ajax calls to another domain) isnt allowed by modern UserAgents unless they support JSONP or CORS. Both require an extra effort on the part of the remote data provider and  could do with some seamless support (or acknowledgement at least) from the UI javascript libraries. It happens that Jetty7 has a CORS filter (which I retrofitted to Joseki 3.4.2 [112]). JSONP can be fudged with jQuery it turns out, if the remote dataset provides JSON output – some don’t. The alternative is that for anyone wishing to use your dataset on the Linked Open Data web, that they must implement a server side proxy of some kind and (usually) work with RSF/XML. A lot of web developers and mashup artists will baulk at this, but astonishngly, post Web2.,0, they still seem to be out of the reach of many dataset publishers. Jetty7 with its CORS fitler goes a long way to improving this situation, but it would be great to see it in Tomcat too, so that publishers don’t have to implement what is a non-trivial filter (this is a security issue after all), and clients dont have to revert (or find/hire/blackmail) to server side code and another network hop.

Vladimir Dzhuvinov has another CORS filter [111], that adds request-tagging and Access-Control-Expose-Headers in the response.

The only need of Sparql endpoint here is for debug purposes. You need to be able to see the triples as the repository sees them when you use an ORdfM layer so that you can understand the queries that are generated, why some of your properties arent showing up and so on.

For query handling I needed a full featured console that would allow me inference (performance permitting) and allow me to render results efficiently. I also needed to be able to federate queries across datasets or endpoints – especially to UMBEL so that I could offer end users the ability to locate data tagged with an UMBEL URI that were “similar” to one they were intersted in (eg sharing a skos:broader statement) . Jena provides the best support here in terms of SPARQL extensions, but inference was too slow for me, and I could mimic some of the basic inference with targetted query writing for Sesame. Sesame doesnt do well with aggregate functions, and inference is per repository and on-write, so you need to adjust how you view the repository compared to how Jena does it. Sesame is faster with an in-memory database.

UI and render This is an exercise in HTML and Ajax. It’s easy to issue Sparql queries that are generated in Javascript based on the what needs to be done, but theres one for every action on the website, and its embedded in the code. Thats not a huge problem given the open nature of the dataset and the limited functionality thats being offered (the extraction process only deals with a small subset of the available information in the text). jQuery works well with Joseki, local or not [112] so the JSON/JSONP issue didnt arise for me. Getting a UI based on the Ontology was possible using the jOWL javascript library, but its not the prettiest or most intuituve to use. A more sophisticated UI would need lots more work, and someone with an eye for web page design 🙂 Here, the UI is generated with JSP code with embedded JS/Ajax calls back to the API. URLs are mapped to JSP and Role based access control enforced. Most URLs have to be authenticated. Spring has a Jackson JSON view layer so that the UI could just work with Javascript arrays, but this requires more annotations on the beans for some properties that cause circular references. The UI code is fairly unsophisticated and for the sake of genericity, it mostly just spits out what is in the array, assuming that the annotations have taken care of most of the filtering, and that the authorization code has done its work and cloaked location, identity and datetime information. The latter works perfectly well, but some beans have propoerties that a real user wouldnt be interested in.

Velocity is used in some places when a user sends a message or invitation, but this is done at the object layer.

The UI doesnt talk Sparql to any endpoint. Sparql queries are generated based on end user actions (the query and reporting console), but this is done at the Java level.

[109] http://www.mindswap.org/~aditkal/SEKE04.pdf
[110] https://uoccou.wordpress.com/wp-admin/post.php?post=241&action=edit
[111] http://blog.dzhuvinov.com/?p=685
[112] https://uoccou.wordpress.com/2010/11/29/cors-servlet-filter/

Townlands of Ireland in Semantic Web format

April 14, 2010 Comments off

I have added the townlands database from seanruad.com to the system. According to NUI Galway, this is the information fomr the 1851 General Alphabetical Index to the Towns, Townlands, Baronies and Parishes in Ireland.  Electoral division is not present – this was introduced in the index accompanying the 1901 census.

About 63000 townlands each with a link to

  • Parish
  • Province
  • Barony
  • County
  • Poor Law Union
  • Size in acres
  • Alias

I have decorated Parish, Province and County with geonames and dbpedia owl:sameAs. A federated query from the Lewis system is now also done when a URI is hovered over. I started by using the owl:sameAs as a join point but found after some eyeball testing that it misses quite a lot – to be investigated – so now doing a regex on the name with word boundary. Kind of defeats the purpose, but means the data gets more visibility until I can build a quick UI for it.

So now with Lewis we have townland correlations. Government and Parliamentary reports which were often based on the townland rather than the Parish can now be linked. One suggestion has been to do this with the 1906 Untenanted Parliamentary report to get a before-after-famine snapshot of land tenency. Was land being retained by landlords for private use; were potential tenants denied access and rights to land usage, what changed in tenancy patterns after the famine ?

Semantic Progress

February 12, 2010 Comments off

Now have inference working with a basic ontology for LewisT.

select * {

?s rdf:type geonames:Feature

}

returns all Location class individuals, as Location is owl:subClass geonames:Feature

Needs a hefty machine – ram and cpu.

Have created an ontology

Have updated the RDF and ontology to use restrictions and intermediate classes (object properties) to model compound sub properties etc. This has also meant removing the # identification of properties and having classnames in URIs. Some of the Sparql gets more verbose in this case, eg.

Adminregions which are different than the location names in which they exist.

SELECT ?s ?sname ?o ?oname
{
 ?s foaf:name ?sname .
 ?s lew:adminregion ?o .
 ?o lew:placename ?n .
 ?n foaf:name ?oname .
 filter (?sname != ?oname )
}

So now have

  • LoD RDF Dataset
  • Sparql endpoint
  • Ontology
  • Lucene based text search on Literals, via sparql :

    – find places with Kill in the name, their county and province

    SELECT ?place ?pr ?ad
    
    {
     ?lit apf:textMatch '+KILL*' .
     ?place ?p ?lit .
     ?place foaf:name ?n .
     ?place lew:County ?ad .
     ?place lew:Province ?pr
    }
  • Facets (via group by and count – perhaps SOLR can still do a better/easier job here) eg
SELECT ?pr count(?pr)

{
 ?lit apf:textMatch '+KILL*' .
 ?place ?p ?lit .
 ?place foaf:name ?n .
 ?place lew:Province ?pr .

}
group by ?pr



Semantic Progress

January 29, 2010 2 comments

Got lucene search function working in Jena finally, having figured out where my bugs were. Now possible to freetext search literals and get back RDF of subject referring to that literal, or any other construct

At http://uoccou.endofinternet.net:2020/lewist.html do this :

PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX muo: <http://purl.oclc.org/NET/muo/muo#>
PREFIX lew: <http://purl.org/uoccou/lewist/>
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX geonames: <http://www.geonames.org/ontology#>
PREFIX dbpedia: <http://dbpedia.org/resource/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX ucum: <http://purl.oclc.org/NET/muo/ucum/unit/length/>
PREFIX wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX list: <http://jena.hpl.hp.com/ARQ/list#>
PREFIX apf: <http://jena.hpl.hp.com/ARQ/property#>
CONSTRUCT { ?s ?prop ?lit }
WHERE { ?lit apf:textMatch '+"Board of First Fruits"' . 
 ?doc ?p ?lit .
 ?s ?prop ?doc
}

Next steps are to create my own extension function to search across both ontology and text, and group results.

Semantic Progress

January 22, 2010 Comments off

Looking at some of the content over xmas decided to update the URI resolver to return content when a property without a resource name was asked for – returning all resources that have or refer to that property. Looking at other LoD Datasets tho this isn’t the norm and the Ontology definition for the property should be returned. In effect the namespace declaration should be resolved to an Ontology definition.

Theres something spooky bad about all this too, this 303 business. Why should a client of a LoD dataset care to differentiate between the name for something, an idea, and the address of the information that describes it ? Why have a 303 wasting all that bandwidth ?

Why not dynamically resolve this within the resolver itself, like all web apps do – having an url like http://uoccou.something.def/resource/something can be resolved internally to anything I choose, or cause some behaviour in code like a call to a database where the descriptive information can be gathered from. Once this is done, the information can be returned via whatever content negotiation needs to occur.

The important thing is that different aspects of the same thing have different addresses/URIs and can be addressed individually if required.
The address for the thing that identifies something should be different than

  • the address that is the description of that something – a physical newspaper page,
  • which is different from a resource describing that newspaper page – (copyright, owner, location etc),
  • which is different from a resource pointed to by that description (a pointer to something describing the content of that page) – a semantic resource on server X, part of archive set Y,
  • which is different from a description of that description -inside front cover, news section,
  • which is different from the description itself – col1-8, stories 1-15, figures 1-3, photos 1-5).

And then you get into address for stories, paragraphs, bylines, slugs and so on. But to get from the first address identifiying the concept-in-context, to what a human would commonly call the “thing” they’re interested in takes a bunch of steps. Does a machine doing the same thing care in the same way ? Does the person that made the software that drives the process on the machine that makes the call to the top level URI care ? Should the default behaviour be defined for the top level URI and the intermediate ones be left to interested parties that make a specific call ?

Once the logical separation is done the physical thing can happen if needs be but it doesnt need to happen – it may make good architectural sense in some cases to have physically separated services for resolution, identification, and description but in others it may not. Spinning a useragent around chasing 303s and so on diminishes performance (or UI responsiveness) and

Semantic Progress

December 1, 2009 Comments off

Got some feedback from the Pedantic Web Group on the Lewis data set just published. Some interesting things I had noticed or chosen to ignore

  • Need a root response for the base of the namespace http://purl.org/uoccou/lewist. Empty isnt much good
  • Need to handle #URIs in my redirector code – agents don’t, browsers generally dont pass it
  • Should change some of the simpler URI properties to datatyped literals
  • Need lang attributes on text
  • Should have VoID.
  • Bits and pieces

Other than that, will hopefully add the MP and Seat name to the dataset by end of this week. Will prob use foaf:name for now. Begs the question about whether I should get the Hansard data sets into RDF and publish.