Posts Tagged ‘software’

Parsing m3u file for curl checks using AWK

July 28, 2016 Comments off

I recently had reason to make use of an m3u playlist file for an IPTV device. I found one on, a great resource for all kinds of sat and IPTV issues. IIRC I came across this after finding a post about a plugin at

In any case the m3u file contained lots of links to IPTV stations that were no longer available of not responding anymore so I wrote an awk script to parse the m3u and based on an expression execute and action that was a system() call. AWK is a great tool to use search expressions and logic on records in files but is a PITA to debug – and my other attempts at using getline didnt help either. M3U files are a sequence of paired records which makes grep and shell scripting innappropriate I think (which I did try firstly) and awk seems a better way, even if it needs a bunch of calls out to another process/shell.

See my post on for more ( but heres the script if you need it to parse/validate any other kind of m3u playlist from time to time.

#!/usr/bin/awk -f
 print "#EXTM3U"
 if ( $0 ~ /^#EXTINF/ ) {
 #reset URL so it doesnt print the same one twice
 } else if ( $0 !~ /^#EXTINF/ ) {
 #print "Found "URL

#print ITM"\n"URL
 # curl --connect-timeout $SLEEP --output /dev/null --silent $u
 #RC=system("curl --connect-timeout 5 --output /dev/null -silent" URL)

if ( URL != "" ) {
 cmd = "curl --head --location --fail --max-time 10 --connect-timeout 5 --head --output /dev/null -silent " URL
 #print "Calling : "cmd
 RC = system( cmd )
 #print "Return code : "RC
 if ( RC == 0 ) {
 print ITM"\n"URL


Open Semantic Desktop Search – good but….

April 22, 2016 4 comments

….needs more administration documentation I think, or maybe an idiots guide for the likes of me. I installed the Desktop version VirtualBox image and it all went fairly smoothly. After setting up doc shares to an archive of about 800k docs on a NAS things started indexing. Cool ! Facets ! Keywords ! Metadata ! But all was not right – it was slowish – but hey its a VM and my host is not super-top-of-the-range (a Haswell pentium G3258 – so I made sure it was running with 2 CPUs and had 4gb RAM and about 40gb disk to play with at the start. Monitoring it is easy with the search GUI or using the XML REST response at http://localhost:8983/solr/admin/cores?action=status. But things seem to halt at times, or CPU spikes and not much appears to be happening – where do you find any info about what OSDS is doing right now ?

So – the usual places – web server, syslog, etc. Only trouble is I can get a desktop terminal to run in the VM – it seems to start then nothing. So ctrl-f2 into a console. What user id ? Turns out its “user”. What’s the password ? turns out its “live”. I found the for solr in /var/solr and adjusted to INFO level with console and file output, restarted SOLR and…no more info. Messages and syslog need root access – sudo of course – but have to add user “user” to sudoers. Whats the root password then ? I found it somewhere in the documentation but now (ironic) I cant re-find it there. So if you find it let me know – and when you do you can update the sudoers to include user live. Turns out the other place to look for clues is the /tmp dir – it contains the tesseract OCR and tika tmp copies of things so you can monitor the number of files in there and see progress.

But I still cant find out what exactly is going on right now (or maybe this is all there is) and importantly I cannot really guess when the detection, extraction, OCR and indexing will finish. I have a file count from my archive and can see the numbers of current docs indexed but that doesnt give me much help in terms of timing. Tesseract seems pretty sensitive and some quick blog and forum searching seems to confirm that. Still – despite this and the occasional crash or VM abort (and no real way to understand why except removing the most recently active folder share from the VM and wading thru /var/log – making only 1 cpu available to the VM seems to help the crash frequency it turns out) its still going to be better than Recoll I think which wont have facets are the possibilities or RDF enrichment, vocabularies etc. I’d also like to try out ElasticSearch with it – soon.

So –

  • zenity at 50% ? – kill the pid – its just a GUI notification that somethings running, and not really needed
    Nautilus seems to miss behave and if you leave it open on /tmp it also seems to take 50% cpu – kill it
    Give the VM plenty of RAM. I seem to have come across some SMP bug in debian on my system so Ive tuned the VM down to 1 cpu, which seems to help
  • Important dirs on your travels…
    • /opt/solr,
    • /var/solr/logs
    • /var/log/messages
    • /tmp
    • ~
    • /var/opensemanticdesktopsearch
    • /var/lib/opensemanticsearch (*.py files for UI)
    • /usr/share/solr-php-ui/templates/view.index.topbar.php (more UI files – eg header)
    • /var/solr/data/core1/conf
    • /usr/share/solr-php-ui/config.php
    • /usr/share/solr-php-ui/config/config.facets.php (add facet to this list – even tho it says not to because the UI will overwrite it : it doesnt tho – so they appear in the UI)
      ./opensemanticsearch/enhancer-rdf (map facets to purls)
  • tika on localhost:9998
  • default logging for tika and tesseract appears to be system.out
  • sudo apt-get update !

Adding facets

Note editing and uploading facets via text file at http://localhost/search-apps/admin/thesaurus/facet/ attempts to overwrite config.facets.php but fails !

'Facet' object has no attribute 'title'

but the facet is created in Django and appears under “ontologies”, but without any named entities. Some debugging and rooting around shows that the PHP code in /var/lib/opensemanticsearch/ontologies.views is looking for facet.title from the form, when it is in fact called facet.label. Changing line 287 in this file to

""".format(     facet.facet.encode('utf-8'),    facet.label.encode('utf-8')

means that you can now upload text files with concepts for facets. These then show up under the facet name in the right hand column, but dont show up as “concepts” that you can alias for instance.

Collapsing facet menus

if the list of concepts or even the list of facets gets long then putting them in accordian might be good idea. I used Daniel Stocks jQuery plugin. ( Download then include the plugin, eg in


add the following script include


Then : change line 229 (in function print_fact) to in

<div id="<?= $facet_field ?>" class="facet" data-collapse="accordian">

Counting docs

A quick script to show the number of processed docs on the cmdline

echo "Outputting to $FILE"
wget -o /tmp/status_msg.log -O $FILE http://localhost:8983/solr/admin/cores?action=status 
grep --color numDocs $FILE
rm $FILE

Any more tips ?


  • Update (May 4 2016) – about half way thru volume of 800k docs now after 25 days processing. Still crashing out but not so often now it seems. About 20gb of disk used in the VM now.
  • Update (June 1 2016) – finished, but only after I disabled pdf ocr at about 700k – have to come back to this
  • Update June 7 2016 – Ive been trying to get exif data into solr from all the jpegs that I have but without much success until now. After head scratching and debugging and trying to work it out I have had to
    • provide a tika config file :
      <?xml version="1.0" encoding="UTF-8"?>
         <!-- Most things can use the default -->
         <parser class="org.apache.tika.parser.DefaultParser">
           <!-- Don't use DefaultParser for these mimetypes, alternate config below -->
         <!-- JPEG needs special handling - try+combine everything -->
         <parser class="org.apache.tika.parser.jpeg.JpegParser" >
    • update/fix the /etc/init.d/tika script start/respawn cmd to correctly use that file (and reboot the vm as init restart doesnt seem to work and systemctl daemon-restart doesnt either – or maybe its just my dud config) :
      daemon --respawn --user=tika --name=tika --verbose -o /tmp/tika.log -O /tmp/tika.err -- 
      java -jar /usr/share/java/tika-server.jar -c /home/user/osds-config.xml
    • try and work out if the /usr/lib/python2.7/etl/ script was working or not -lots of extra print statements and verbose = True. The long and the short of it is that it is working, but the extracted metadata fields defined in the script dont include much in the way of exif fields, and even if they did we’d also have to update the /var/solr/data/core1/conf/schema.xml to include them as fields. Thats the next job…
    • A handy cmdline test of the tika-server is to post a jpeg to it using curl. If your init script isnt working you wont get much back, likewise of the file you think you are posting doesnt actually exist, and if you are getting a 415 unsupported media type back in verbose curl response, it probably means you tika config file is screwed, like mine was, but I kept ignoring that – fool !. I went back to unit level and defined a single test dir in the /etc/opensemanticsearch/filemonitoring/files and put one test jpeg in there. Using the curl cmd you can then test the tika-server is working (you’ll get back a json blob with exif fields), and then using ‘touch’ and /usr/bin/opensemanticsearch-index-dir you can test the pipeline in full.
      curl -vX POST -H "Accept: application/json" -F file=@exif.jpg http://localhost:9998/rmeta/form -H "Content-type: multipart/form-data"
  • (Update Sept 2016) – new version of OSDS available that seems to work better out of the box. Interface changes, django defaults to english, adding named/entities and facets doesn’t barf. (although this doesnt seem to be possible during the crash as the system is completely unresponsive)

MonetDB and OpenJena

April 6, 2012 1 comment

MonetDB has been updated recently with a Dec 2011-SP2 release. Having previously tried to integrate it with OpenJena and failed because of the use of multiple inner joins, I was happy to find that the update fixed those problems and allows all the integration/unit-tests to pass.

This means of course that Im going to now have to create a patch to Jena (see Jira issue[1]), and when thats done, you can follow the instructions below to test it out – literally run the unit tests. I have been using Ubuntu 11.10 amd64 for this so the notes below reflect this:

1) Download latest MonetDB and JDBC driver

2) Install as per instructions (default username:monetdb with password:monetdb)

3) In your home dir create a my-farm directory

4) Create an "" file to house your local settings for PATH etc

export JAVA_HOME=/usr/lib/jvm/java-6-sun
#point this to whereever you have SDB installed
export PATH=$SDBROOT/bin:$PATH
#point this to whereever you have downloaded the MonetDB JDBC driver
export SDB_JDBC=~/Downloads/monetdb/jdbcclient.jar

5) Create a "monet_h.ttl" assembly file to define a layout2/hash repository

@prefix sdb:     <> .
@prefix rdfs:     <> .
@prefix rdf:     <> .
@prefix ja:      <> .

# MonetDB

<#store> rdf:type sdb:Store ;
sdb:layout     "layout2/hash" ;
sdb:connection <#conn> ;

<#conn> rdf:type sdb:SDBConnection ;
sdb:sdbType       "MonetDB" ;    # Needed for JDBC URL
sdb:sdbHost       "localhost" ;
sdb:sdbName       "TEST2H" ;
sdb:driver        "nl.cwi.monetdb.jdbc.MonetDriver" ;
sdb:sdbUser        "monetdb" ;
sdb:sdbPassword        "monetdb" ;
sdb:jdbcURL    "jdbc:monetdb://localhost:50000/TEST2H";

6) create a script – ""– to drop,create and initialise the repo – this needs to be used each time you run the sdbtest suite. It will make use of the and the monet_h.ttl

monetdb stop TEST2H
monetdb destroy TEST2H
monetdb create TEST2H
monetdb release TEST2H
. ./
bin/sdbconfig --sdb monet_h.ttl --create

7) Run the script

8) Check things went ok with

i) mclient -u monetdb -d TEST2H.

ii) \D

You should see a dump of the schema. There should be among other things a prefixes table.

9) Now for the unit tests :

Create a monetdb-hash.ttl file that Jena can use to connect with

@prefix sdb:     <> .
@prefix rdfs:     <> .
@prefix rdf:     <> .
@prefix ja:      <> .

[] rdf:type sdb:Store ;
sdb:layout     "layout2" ;
sdb:connection _:c ;

_:c rdf:type sdb:SDBConnection ;
sdb:sdbType       "MonetDB" ;    # Needed for JDBC URL
sdb:sdbHost       "localhost" ;
sdb:sdbName       "TEST2H" ;
sdb:driver        "nl.cwi.monetdb.jdbc.MonetDriver" ;
sdb:sdbUser        "monetdb" ;
sdb:sdbPassword        "monetdb" ;
sdb:jdbcURL    "jdbc:monetdb://localhost:50000/TEST2H?debug=true&logfile=monet.debug.log";

10) If in Eclipse, with the SDB source, create a run configuration for sdbtest.

#Main class : sdb.sdbtest
#Arguments: --sdb monetdb-hash.ttl ./testing/manifest-sdb.ttl

11) Run the test suite – all tests should pass.

12) Next : Load some RDF and test performance !……


Whats the point : Semantic RESTful Web Services ?

March 9, 2011 4 comments

Well, I think its dawning on me, that what Roy Fielding talks about (rather abstractly) [1] is what Henry Story neatly summarises and provides examples of [2] – REST SOA, with connected semantics. I’m one of those who can be accused of implementing REST not in the Roy Fielding manner of the word, but in the anyting thats not WS* “meaning”. I’ve done request mapping, content negotiating, resource rendering in XML, Json (and a bunch of others), GET,PUT,POST,HEAD etc etc etc, but never all together, and never in the true Spirit of Roy. But when you add the semantic web, you can really see that theres something good going on here – “easy” and ubiquitous webservices.

Roy talks about representations, resources and connectedness, about agents or service consumers that deal with well-known media types and links, and nothing else – REST implies that a user agent is “thin”, understands basic-and-well-known types and protocols, and renders a look and feel and a behaviour that reacts to what it is fed. As he says it should work with the “follow your nose” principal (no need for WADL[3,4]).

For a browser this would mean that you point it an URL, it displays content suitably, that it receives and displays links with appropriate CRUD capabilities for it and and relations it is given.  For example, given a book resource, render it using the .book CSS class, and create links to add to shopping cart, get a contents list, add to a favourites list. For a chapter in the book, there may be link to print it, to relate it to a chapter in another similar book, to annotate it and send to a colleague. For a daemon or agent it might mean that it alters the time at which it performs an action against a resource, or what action it takes. The navigation and action controls aren’t determined by business or display logic, but by the resource and its relations – the agent consuming the resource knows it has to display or follow a link, the CSS may have display capabilities based on the resource type or context, the workflow steps will appear at the right time for the right user, under the right circumstances. Client logic is solely to deal with converting representations to appropriate media-types, and driving application state – using relations and verbs to make transitions with links.

But the thing that got me spinning, as I tried to understand the abstractedness, and as I looked into JAX-RS [5], and its various implementations (well, Spring* in particular TBH,which doesnt do JAX-RS in fact [6]) was that the connectedness and follow-your-nose principal seemed absent. Its all very well and cosy (and arguably easy) to create some platform code that maps URIs to classes and methods and HTTP verbs, and then to output XML or JSON or not (think JSP), or perhaps even Atom, OData, RDF, N3 or TTL but wheres the linked connectedness – the things we talk about and take for granted in Linked Open Semantic Data world ? And how does it know what links to create, how to generate them, and how they should be presented (if there’s a human involved) ?

Well, Henry blows that lightbulb for me when he illustrates from his foaf profile all the foaf:knows relations [2]. In a RESTful world where a service returns a foaf file and reads the foaf:knows elements it can decide what to do based on that predicate – it can deduce that the resource represented is a Person and can create the links it chooses using what it knows about foaf:knows and REST verbs – create/read/update/delete. It might allow addition of another foaf:knows with a PUT to the URI identifying the owner, an update to a mailing list so that all those foaf:knows objects are added, or automatically update a trust counter against a system resource because if Henry foaf:knows TBL in this context, then TBL must be “good for it” :-). In addition, it only knows that a URI represents that Person, and the URI could be a hypermedia link in the form of an URL, a ftp or webDav link, or some other protocol. Finally, this “knows” concept is really an upfront agreement about what representations are being used for the state of the application (it knows and XML schema, or an Ontology, or perhaps even looks them up on the fly), but navigating thru state is controlled by the interactions with the service (Http verbs) and the responses (status and agreed represenation in the body content) received.

At first sight those RESTful libraries don’t really need to know that much about the connectedness – they only need to map verbs and serve resources with those links embedded (RDF anyone? ) and using those well-known vocabularies, classes,relations and constraints – ie ontologies. But what about workflow : I post an object or resource, I get a response with the ID of that resource, and I need a link that tells me where to go for the next state transition ?

So, lo-and-behold, we have semantic linked data and REST superadditively combined, in a loosely coupled web (or “cloud”, if you like that keyword) of semantic links, intelligent user agents that understand those links or their context, web resolvable URIs, and value-added interlinked services – in effect a “Web Service Bus”. [7] !!


  1. Point your People tool at the RESTful people+location web service and it “just works” to give you a social-network-mashup of connected people and interests (provenance, trust), and then
  2. switch over to your Energy consumption application and it also just works (based on what it has chosen to do and the well-known ontologies and resources it understands) – see how big your carbon footprint is when you meet TBL next week at Geneva if you fly,drive or take the train – and maybe you’ll be able to see who you can meet on the way and who else will be sitting beside you.

But your not out of the woods yet, doing semantic RESTfu web apps isnt a clear open space : your application still has to deal with authentication, input validation, long lived database transaction control, multithreading, performance, perhaps object relational mapping, but jax-rs/REST takes care of the object-message-mapping (the interface-to-implementation layer), your client or agent is thin but intelligent, and your middle tier contains your business logic.

Your application will need to honour the request-response state machine, perhaps checking availability using OPTIONS, or Etags.

You’ll need to decide how to transform from your programming model of choice – OO perhaps – to Resource. Some of the object to RDF mapping within libraries like Empire[15], JenaBean[16], Sommer [22]{defunct?), object-triple [17] may help. Perhaps this wont be an issue for you if you can foist the RESTful resource and linkage proposition onto an object model and remain in the object world – why waste processor and resource when you store data in RDF, convert to an Object on retrieval, process, convert to Xml-RDF or JSON on the way out, then parse and walk in a JSP before rendering as HTML ? As an OO programmer on the web you’re familiar with marshalling objects in and out of different serialization –  RDF/XML/JSON/HTML, but you do want and need to minimise those transitions. Perhapsfor “Big Data” we should stay in the Resource world : persist to a fast native RDF triplestore or HPC based system on a cluster of MapReduce or somesuch (CouchDB[20], Heart/HBase [21] ? perhaps BigData[18] or SHARD[19], AllegroGraph[23] ?), and talk to it with ProLog or some such – forget the Object paradigm and embrace the Linked, Open World Resources, and also do it with REST.

You also have to be clear that REST suits what you want to do (other architectures haven’t just been demoted to history) what your services are  -what you are interfacing with, what are your domain objects, what service operations are exposed when, what workflow do you need to encompass[13], and how granular you need to be – a shopping cart application will need to save items to a shopping list, rather than save the items themselves (or the cart resource probably), but it will also, behind the service, need to update a stock control or inventory – which isnt exposed to your end user.  So be clear about which service level CRUD operations you need to expose to your user or “agent”, and which if any domain objects you need to directly manipulate.

But in the end, hopefully, you’ve still followed your enterprise principles and patterns, but you’ve adopted a long lasting web-scale architecture, and if youve added the semantic vocabulary, you’ve got the basis for successful evolution, a network effect, adaptable clients and agents and a successful resolution to an important business case – thats why your doing this, isn’t it, not because its cool ?

Update : April 24 – read Otavios paper on RESTfulGrounding [25] but also read Alowisheq, Millard and Tiropanis EXPRESS RESTful services paper[26]. RESTfulGrounding does for REST and WADL what OWL-S does for WSDL – it gets Semantic descriptions into the syntactic descriptions that automated services might use to interact with a web service, and facilitates discovery, composition, monitoring and execution. EXPRESS takes a different approach and based on an existing RESTful web service allows you to create an OWL description that can also be RESTfully accessed to describe the services resources, relations and “parameters” (OWL DataTypeProprty and ObjectProperty). They describe an adaptation of Amazon S3 buckets and docs with EXPRESS and compare with SA-REST and OWL-S approaches.

I like EXPRESS more than RESTfulGrounding as the simplicity appeals : the way it in turn relies on REST to underpin the service description access and interaction, adheres to RESTful principles for message exchange – using TTL rather than XML – , follow-you-nose, and the fact that this in turn means I don’t have to learn much if I want to make use of it. It does need the use of a code generator for stubs and URIs and a manual step to define which methods apply to which URIs, and doesn’t do much for discovery and composition – but they acknowledge this and intend to work on it – and a real implementation with these tools needs to be made available so that people like me can try it out. Is there one ?

I need to understand more about WADL[27,28] (why is it needed in the first place ?) and how I might go about actually building a set of services that need to be described and then discovered and composed to provide some useful value, but EXPRESS fits nicely into web scale, lo-fi approaches that quickly gain traction and that might make use of a CPoA kind of approach for discovery and composition.

* You’ve got other choices :

  • Apache CXF – perhaps best if you come from the WS* camp or have a mixture [8]
  • GlassFish Jersey – seems to have good traction, with hooks into Spring et al [9]
  • RESTeasy – JBoss jax-rs implementation [10]
  • RESTlet – not sure about this, seems to have good support, taking a different approach apparently – eg RESTlet vs SERVlet, but I need more info to do it justice [11]
  • PLAY Framework – has good REST support I understand from others. [12]
  • Clerezza – Apache incubator project with RDF, jax-rs, scala and “renderlet” support. Looks interesting from a RDF PoV, but maybe not so interesting from an OOD PoV [14]



















Java Semantic & Linked Open Data webapps – Part 5.2

February 8, 2011 Comments off

Continuation from previous article in series

The overall architecture for the Semantic backed J2EE app is different from the Linked data app already discussed because we need a business logic layer and a decoupling from the persistence layer. We also want to create a Java app rather than a semantic application so that the programming paradigms and patterns are familiar to the Enterprise java developer.


Semantically backed J2EE webApp - System diagram

Here we see a fairly standard 3 tier MVC application. Browser requests URIs from the appserver, or makes an Ajax call and gets html from server side JSPs or JSON formatted data in response, respectively. The application server contains java code that maps URIs and API calls to controllers, which make calls to service classes and DAO code. The DAO code makes call via a persistence proxy to get data from the server that is unmarshalled from RDF to java objects (or makes writes in the other direction). The persistence layer is configured to use an implementation that takes care of the Object to RDF mapping – two implementations are available (JenaBean and EmpireJPA). These in turn use their own protocols to talk to native or location repositories, or typically JDBC talk with standard DBMS. Spring and Spring security provide infrastructure level services for dependency injection, component wiring, MVC abstractions, and role, method and data level security for beans and dynamically created object instances. These technologies are shown below in the AppServer layer cake.


Technology libraries and tools used in Semantically backed J2EE WebApp

Technology libraries and tools used in Semantically backed J2EE WebApp

Obviously, there are many things going on here, and they’ll need some discussion

  • Basic building blocks, tool selection
  • Security considerations and restrictions
    • authentication – OpenID, admin login, facebook connect
    • authorisation role,uri,method,data levels
    • registration process
    • ownership, group (friend) and application membership, resolution (date & location cloaking)
    • ACL – data and dynamic object level authorisation
    • Syndication –
      • json,jsonp (get/post),,ajax,
      • cors
      • oauth
      • API and result formats
  • Scale, concurrency, transactions,  failures, and performance
  • URIs, ontology, linkage
  • input, output interfaces
  • ontology to object/interface mapping

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.

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.


Final section in Java Semantic Webapps Part 3.1 completed

December 9, 2010 Comments off

I’ve filled out the tools matrix with the 60 or so tools, libraries and frameworks I looked at for the two projects I created. Not all are used of course, and only a few are used in both. Includes comments and opinion, which I used and why, and all referenced. Phew.