Semantic Web, Structured Dynamics, OSF for Drupal, Planet Drupal, OSF Web Services, OSF Widgets, Open Semantic Framework

3.5 Million DBpedia Entities in Drupal 7

In the previous article Loading DBpedia into the Open Semantic Framework, we explained how we could load the 3.5 million DBpedia entities into a Open Semantic Framework instance. In this article, we will show how these million of entities can be used in Drupal for searching, browsing, mapping and templating these DBpedia entities.

Installing and Configuring OSF for Drupal

This article doesn’t cover how OSF for Drupal can be installed and configured. If you want to properly install and configure OSF for Drupal, you should install it using the OSF Installer by running this command:

[cc lang=’bash’ line_numbers=’false’]
./osf-installer --install-osf-drupal

Then you should configure it using the first section of the OSF for Drupal user manual.

Once this is done, the only thing you will have to do is to register the OSF instance that hosts the DBpedia dataset. Then to register the DBpedia data into the Drupal instance. The only thing you will have to do is to make sure that the Drupal’s administator role has access to the DBpedia dataset. It can be done by using the PMT (Permissions Management Tool) by running the following command:

[cc lang=’bash’ line_numbers=’false’]
pmt --create-access --access-dataset="" --access-group="http://YOU-DRUPAL-DOMAIN/role/3/administrator" --access-perm-create="true" --access-perm-read="true" --access-perm-delete="true" --access-perm-update="true" --access-all-ws

Searching Entities using the Search API

All the DBpedia entities are searchable via the SearchAPI. This is possible because of the OSF SearchAPI connector module that interface the SearchAPI with OSF.

Here is an example of such a SearchAPI search query. Each of these result come from the OSF Search endpoint. Each of the result is templated using the generic search result template, or other entity type search templates.

What is interesting is that depending on the type of the entity to display in the results, its display can be different. So instead of having a endless list of results with titles and descriptions, we can have different displays depending on the type of the record, and the information we have about that record.


In this example, only the generic search template got used to display these results. Here is the generic search results template code:

Manipulating Entities using the Entity API

The Entity API is a powerful Drupal API that let developers and designers loading and manipulating entities that are indexed in the data store (in this case, OSF). The full Entity API is operational on the DBpedia entities because of the OSF Entities connector module.

As you can see in the template above (and in the other templates to follow), we can easily use the Entity API to load DBpedia entities. In these templates examples, what we are doing is to use this API to load the entities referenced by an entity. In this case, we do this to get their labels. Once we loaded the entity, we end-up with an Entity object that we can use like any other Drupal entities:

Mapping Entities using the sWebMap OSF Widget

Because a big number of DBpedia entities does have geolocation data, we wanted to test the sWebMap OSF Widget to be able to search, browse and locate all the geolocalized entities. What we did is to create a new Content Type. Then we created a new template for that content type that implements the sWebMap widget. The simple template we created for this purpose is available here:

Then, once we load a page of that Content Type, we can see the sWebMap widget populated with the geolocalized DBpedia entities. In the example below, we see the top 20 records in that region (USA):


Then what we do is to filter these entities by type and attribute/values. In the following example, we filtered by RadioStation, and then we are selecting a filter to define the type of radio station we are looking for:


Finally we add even more filtering options to drill-down the geolocalized information we are looking for.


We end-up with all the classical radio station that broadcast in the region of Pittsburgh.


Templating Entities using Drupal’s Templating Engine

Another thing we get out of the box with Drupal and OSF for Drupal, is the possibility to template the entities view pages and the search resultsets. In any case, the selection of the template is done depending on the type of the entity to display.

With OSF for Drupal, we created a template selection mechanism that uses the ontologies’ structure to select the proper templates. For example, if we have a Broadcaster template, then it could be used to template information about a RadioStation or a TelevisionStation, even if these templates are not existing.

Here is an example of a search resultset that displays information about different type of entities:


The first entity is an organization that has an image. It uses the generic template. The second one is a person which also use the generic template, but it has no image. Both are using the generic template because none of the Organization nor the Person templates have been created. However, the third result uses a different template. The third result is a RadioStation. However, it uses the Broadcaster template since the RadioStation class is a sub-class-of Broadcaster and because the Broadcaster template exists in the Drupal instance.

Here is the code of the Broadcaster search result template:

Now let’s take a look at the template that displays information about a specific Entity type:


This minimal records displays some information about this radio station. The code of this template is:

Building Complex Search Queries using the OSF Query Builder

A system administrator can also use the OSF Query Builder to create more complex search queries. In the following query, we are doing a search for the keyword “radio“, we are filtering by type RadioStation, and we are boosting the scoring value of all the results that have the word “life” in their slogan.


The top result is a radio station of Moscow that has “Life in Motion!” as its slogan. We can also see the impact of the scoring booster on the score of that result.


As we can see with these two articles, it is relatively easy and fast to import the DBpedia dataset into a OSF instance. By doing so, we end-up with a series of tools to access, manage and publish this information. Then we can leverage the OSF platform to create all kind of web portals or other web services. All the tools are there, out-of-the-box.

This being said, this is not where lies the challenge. The thing is that there is more than 500 classes and 2000 properties that describes all the content present in the DBpedia Ontology. This means that more than 2000 filters may exists for the Search API, the sWebMap widget, etc. This also means that more than 500 Drupal bundles can be created with hundred of fields, etc.

All this need to be properly configured and managed by the Drupal site developer. However, there are mechanisms that have been developed to help them managing this amount of information such as the entity template selection mechanism that uses the ontologies’ structure to select the display templates to use. For example, you could focus on the entity Broadcaster, and create a single template for it. Automatically, this template could be used by sub-classes such as BroadcastNetwork, RadioStation, TelevisionStation and many others.

The Open Semantic Framework is really flexible and powerful as you may have noticed with this series of two articles. However, the challenge and most of the work lies into creating and configuring the portal that will use this information. The work lies into creating the search and entities templates. To properly define and manage the bundles and fields, etc.

Semantic Web, Structured Dynamics, OSF for Drupal, OSF Web Services, OSF Widgets, Open Semantic Framework

Loading DBpedia into the Open Semantic Framework


This first article or a series of two will show you how to load DBpedia into a Open Semantic Framework instance. A second article will be published that will show you how the 3.5 million entities present in DBpedia can be accessible from a Drupal 7 installation. All the entities will be searchable, templatable, viewable, mappable, editabled and revisionable directly within Drupal.
Loading DBPedia into a OSF instance is not overly complex. Someone can easily manage to do it using this tutorial, and ending up with a OSF instance loaded with the full DBpedia dataset.

Creating a Open Semantic Framework Instance

The first step is to create a OSF instance. This tutorial uses the AWS EC2 OSF image. However, you can easily perform the same steps except that you should use the OSF Installer to install OSF on your own Ubuntu 12.10 server.
To create the OSF instance we will use to load DBpedia, we use one of the following OSF 3.0 AMI:
Region arch root store AMI
us-east-1 64-bit EBS ami-afe4d1c6
us-west-1 64-bit EBS ami-d01b2895
us-west-2 64-bit EBS ami-c6f691f6
eu-west-1 64-bit EBS ami-883fd4ff
sa-east-1 64-bit EBS ami-6515b478
ap-southeast-2 64-bit EBS ami-4734ab7d
ap-southeast-1 64-bit EBS ami-364d1a64
ap-northeast-1 64-bit EBS ami-476a0646

Then to make things faster, we used a EC2 c3.4xlarge server with 75G of disk space.

In this tutorial, we are not re-configuring any passwords or settings for this vanilla instance. However, if you are to create an instance of your own, you should read the Creating and Configuring an Amazon EC2 AMI OSF Instance manual to configure it for you own purpose and to make it secure.

Note that most of the steps to load DBpedia into Virtuoso come from Jorn Hees’ article about this subject.

Also note that you should make sure to path the files in the following 3 commits. These issues have been found while writing this blog post, and haven’t (yet) made it into the AMI we use here: 88d6f1a782744a62bf83d52eceff695e0fee773b, 1389744b7dbf8f755a1bb9be468b3c51df75d6d8 and 719b4a776d43345e73847e6c785a4e9964b83a1c

Downloading DBpedia

The second step is to download all the DBpedia files that you want to use in your OSF instance. For this tutorial, we focus on the files where we can get the titles, abstracts, descriptions, all the mapped properties, the geolocalization of the entities, etc. You can download all these files by running the following commands:

[cc lang=’bash’ line_numbers=’false’]
mkdir -p /usr/local/data/dbpedia/3.9/en cd /usr/local/data/dbpedia/3.9/en wget wget wget wget wget wget wget bzip2 -d *

Loading DBpedia into Virtuoso

The next step is to use the Virtuoso’s RDF Bulk Loader to load all the DBpedia triples into Virtuoso. To do so, the first step we have to do is to create a new OSF dataset where the DBpedia entities will be indexed. To create the new dataset, we use the DMT (Datasets Management Tool) to create it. Note that the DMT is already installed on that OSF AMI 3.0.

[cc lang=’bash’ line_numbers=’false’]
dmt -n --osf-web-services="http://localhost/ws/" --uri="" --creator="http://localhost/wsf/users/admin" --title="DBpedia 3.9" --group="http://localhost/wsf/groups/administrators"

Then we have to create and configure the RDF Bulk Loader. The first step is to create the procedure file that will be used to import the tables and procedures into Virtuoso:

[cc lang=’bash’ line_numbers=’false’]
cd /tmp/

Then create a file called VirtBulkRDFLoaderScript.vsql and add the following code in that new file:

[cc lang=’sql’ line_numbers=’false’]
CREATE TABLE load_list ( ll_file VARCHAR, ll_graph VARCHAR, ll_state INT DEFAULT 0, -- 0 not started, 1 going, 2 done ll_started DATETIME, ll_done DATETIME, ll_host INT, ll_work_time INTEGER, ll_error VARCHAR, PRIMARY KEY (ll_file)) ALTER INDEX load_list ON load_list PARTITION (ll_file VARCHAR) ; CREATE INDEX ll_state ON load_list (ll_state, ll_file, ll_graph) PARTITION (ll_state INT) ; CREATE TABLE ldlock (id INT PRIMARY KEY) ALTER INDEX ldlock ON ldlock PARTITION (id INT) ; INSERT INTO ldlock VALUES (0); CREATE PROCEDURE ld_dir (IN path VARCHAR, IN mask VARCHAR, IN graph VARCHAR) { DECLARE ls ANY; DECLARE inx INT; ls := sys_dirlist (path, 1); FOR (inx := 0; inx < LENGTH (ls); inx := inx + 1) { IF (ls[inx] LIKE mask) { SET ISOLATION = 'serializable'; IF (NOT (EXISTS (SELECT 1 FROM DB.DBA.LOAD_LIST WHERE LL_FILE = path || '/' || ls[inx] FOR UPDATE))) { DECLARE gfile, cgfile, ngraph VARCHAR; gfile := path || '/' || REPLACE (ls[inx], '.gz', '') || '.graph'; cgfile := path || '/' || regexp_replace (REPLACE (ls[inx], '.gz', ''), '\\-[0-9]+\\.n', '.n') || '.graph'; IF (file_stat (gfile) <> 0) ngraph := TRIM (file_to_string (gfile), ' \r\n'); ELSE IF (file_stat (cgfile) <> 0) ngraph := TRIM (file_to_string (cgfile), ' \r\n'); ELSE IF (file_stat (path || '/' || 'global.graph') <> 0) ngraph := TRIM (file_to_string (path || '/' || 'global.graph'), ' \r\n'); ELSE ngraph := graph; IF (ngraph IS NOT NULL) { INSERT INTO DB.DBA.LOAD_LIST (ll_file, ll_graph) VALUES (path || '/' || ls[inx], ngraph); } } COMMIT WORK; } } } ; CREATE PROCEDURE rdf_read_dir (IN path VARCHAR, IN mask VARCHAR, IN graph VARCHAR) { ld_dirr (path, mask, graph); } ; CREATE PROCEDURE ld_dir_all (IN path VARCHAR, IN mask VARCHAR, IN graph VARCHAR) { DECLARE ls ANY; DECLARE inx INT; ls := sys_dirlist (path, 0); ld_dir (path, mask, graph); FOR (inx := 0; inx < LENGTH (ls); inx := inx + 1) { IF (ls[inx] <> '.' AND ls[inx] <> '..') { ld_dir_all (path||'/'||ls[inx], mask, graph); } } } ; CREATE PROCEDURE ld_add (IN _fname VARCHAR, IN _graph VARCHAR) { --log_message (sprintf ('ld_add: %s, %s', _fname, _graph)); SET ISOLATION = 'serializable'; IF (NOT (EXISTS (SELECT 1 FROM DB.DBA.LOAD_LIST WHERE LL_FILE = _fname FOR UPDATE))) { INSERT INTO DB.DBA.LOAD_LIST (LL_FILE, LL_GRAPH) VALUES (_fname, _graph); } COMMIT WORK; } ; CREATE PROCEDURE ld_ttlp_flags (IN fname VARCHAR) { IF (fname LIKE '%/btc-2009%' OR fname LIKE '%.nq%' OR fname LIKE '%.n4') RETURN 255 + 512; RETURN 255; } ; CREATE PROCEDURE ld_file (IN f VARCHAR, IN graph VARCHAR) { DECLARE gzip_name VARCHAR; DECLARE exit handler FOR sqlstate '*' { ROLLBACK WORK; UPDATE DB.DBA.LOAD_LIST SET LL_STATE = 2, LL_DONE = CURDATETIME (), LL_ERROR = __sql_state || ' ' || __sql_message WHERE LL_FILE = f; COMMIT WORK; log_message (sprintf (' File %s error %s %s', f, __sql_state, __sql_message)); RETURN; }; IF (f LIKE '%.grdf' OR f LIKE '%.grdf.gz') { load_grdf (f); } ELSE IF (f LIKE '%.gz') { gzip_name := regexp_replace (f, '\.gz\x24', ''); IF (gzip_name LIKE '%.xml' OR gzip_name LIKE '%.owl' OR gzip_name LIKE '%.rdf') DB.DBA.RDF_LOAD_RDFXML (gz_file_open (f), graph, graph); ELSE TTLP (gz_file_open (f), graph, graph, ld_ttlp_flags (gzip_name)); } ELSE { IF (f LIKE '%.xml' OR f LIKE '%.owl' OR f LIKE '%.rdf') DB.DBA.RDF_LOAD_RDFXML (file_open (f), graph, graph); ELSE TTLP (file_open (f), graph, graph, ld_ttlp_flags (f)); } --log_message (sprintf ('loaded %s', f)); } ; CREATE PROCEDURE rdf_load_dir (IN path VARCHAR, IN mask VARCHAR := '%.nt', IN graph VARCHAR := '') { DELETE FROM DB.DBA.LOAD_LIST WHERE LL_FILE = '##stop'; COMMIT WORK; ld_dir (path, mask, graph); rdf_loader_run (); } ; CREATE PROCEDURE ld_array () { DECLARE first, last, arr, len, local ANY; DECLARE cr CURSOR FOR SELECT TOP 100 LL_FILE, LL_GRAPH FROM DB.DBA.LOAD_LIST TABLE OPTION (INDEX ll_state) WHERE LL_STATE = 0 FOR UPDATE; DECLARE fill INT; DECLARE f, g VARCHAR; DECLARE r ANY; WHENEVER NOT FOUND GOTO done; first := 0; last := 0; arr := make_array (100, 'any'); fill := 0; OPEN cr; len := 0; FOR (;;) { FETCH cr INTO f, g; IF (0 = first) first := f; last := f; arr[fill] := VECTOR (f, g); len := len + CAST (file_stat (f, 1) AS INT); fill := fill + 1; IF (len > 2000000) GOTO done; } done: IF (0 = first) RETURN 0; IF (1 <> sys_stat ('cl_run_local_only')) local := sys_stat ('cl_this_host'); UPDATE load_list SET ll_state = 1, ll_started = CURDATETIME (), LL_HOST = local WHERE ll_file >= first AND ll_file <= last; RETURN arr; } ; CREATE PROCEDURE rdf_loader_run (IN max_files INTEGER := NULL, IN log_enable INT := 2) { DECLARE sec_delay float; DECLARE _f, _graph VARCHAR; DECLARE arr ANY; DECLARE xx, inx, tx_mode, ld_mode INT; ld_mode := log_enable; IF (0 = sys_stat ('cl_run_local_only')) { IF (log_enable = 2 AND cl_this_host () = 1) { cl_exec ('checkpoint_interval (0)'); cl_exec ('__dbf_set (''cl_non_logged_write_mode'', 1)'); } IF (cl_this_host () = 1) cl_exec('__dbf_set(''cl_max_keep_alives_missed'',3000)'); } tx_mode := bit_and (1, log_enable); log_message ('Loader started'); DELETE FROM DB.DBA.LOAD_LIST WHERE LL_FILE = '##stop'; COMMIT WORK; WHILE (1) { SET ISOLATION = 'repeatable'; DECLARE exit handler FOR sqlstate '40001' { ROLLBACK WORK; sec_delay := RND(1000)*0.001; log_message(sprintf('deadlock in loader, waiting %d milliseconds', CAST (sec_delay * 1000 AS INTEGER))); delay(sec_delay); GOTO again; }; again:; IF (EXISTS (SELECT 1 FROM DB.DBA.LOAD_LIST WHERE LL_FILE = '##stop')) { log_message ('File load stopped by rdf_load_stop.'); RETURN; } log_enable (tx_mode, 1); IF (max_files IS NOT NULL AND max_files <= 0) { COMMIT WORK; log_message ('Max_files reached. Finishing.'); RETURN; } WHENEVER NOT FOUND GOTO looks_empty; -- log_message ('Getting next file.'); SET ISOLATION = 'serializable'; SELECT id INTO xx FROM ldlock WHERE id = 0 FOR UPDATE; arr := ld_array (); COMMIT WORK; IF (0 = arr) GOTO looks_empty; log_enable (ld_mode, 1); FOR (inx := 0; inx < 100; inx := inx + 1) { IF (0 = arr[inx]) GOTO arr_done; ld_file (arr[inx][0], arr[inx][1]); UPDATE DB.DBA.LOAD_LIST SET LL_STATE = 2, LL_DONE = CURDATETIME () WHERE LL_FILE = arr[inx][0]; } arr_done: log_enable (tx_mode, 1); IF (max_files IS NOT NULL) max_files := max_files - 100; COMMIT WORK; } looks_empty: COMMIT WORK; log_message ('No more files to load. Loader has finished,'); RETURN; } ; CREATE PROCEDURE rdf_load_stop (IN force INT := 0) { INSERT INTO DB.DBA.LOAD_LIST (LL_FILE) VALUES ('##stop'); COMMIT WORK; IF (force) cl_exec ('txn_killall (1)'); } ; CREATE PROCEDURE RDF_LOADER_RUN_1 (IN x INT, IN y INT) { rdf_loader_run (x, y); } ; CREATE PROCEDURE rdf_ld_srv (IN log_enable INT) { DECLARE aq ANY; aq := async_queue (1); aq_request (aq, 'DB.DBA.RDF_LOADER_RUN_1', VECTOR (NULL, log_enable)); aq_wait_all (aq); } ; CREATE PROCEDURE load_grdf (IN f VARCHAR) { DECLARE line ANY; DECLARE inx INT; DECLARE ses ANY; DECLARE gr VARCHAR; IF (f LIKE '%.gz') ses := gz_file_open (f); ELSE ses := file_open (f); inx := 0; line := ''; WHILE (line <> 0) { gr := ses_read_line (ses, 0, 0, 1); IF (gr = 0) RETURN; line := ses_read_line (ses, 0, 0, 1); IF (line = 0) RETURN; DB.DBA.RDF_LOAD_RDFXML (line, gr, gr); inx := inx + 1; } } ; -- cl_exec ('set lock_escalation_pct = 110'); -- cl_exec ('DB.DBA.RDF_LD_SRV (1)') & -- cl_exec ('DB.DBA.RDF_LD_SRV (2)') &

Then we have to load it into Virtuoso using the following command:

[cc lang=’bash’ line_numbers=’false’]
/usr/bin/isql-vt localhost dba dba VirtBulkRDFLoaderScript.vsql

Then we have to configure the RDF Bulk Loader. First enter in the isql interface:

[cc lang=’bash’ line_numbers=’false’]

Then copy/paste the following SQL code into the isql interface:

[cc lang=’sql’ line_numbers=’false’]
-- load the files to bulk-load ld_dir_all('/usr/local/data/dbpedia/3.9', '*.*', ''); -- list all the files that will be loaded SELECT * FROM DB.DBA.LOAD_LIST; -- if unsatisfied use: -- delete from DB.DBA.LOAD_LIST and redo; EXIT;

Then enter the isql interface again:

[cc lang=’bash’ line_numbers=’false’]

And copy/paste the following SQL lines:

[cc lang=’sql’ line_numbers=’false’]
rdf_loader_run(); -- will take approx. 2 hours with that EC2 server checkpoint; commit WORK; checkpoint; EXIT;

Configure the Datasets Management Tool

The next step is to properly configure the DMT to bulk load all the DBpedia entities into OSF.

Let’s step back, and explain what we are doing here. What we did with the steps above, is to use a fast method to import all the 3.5 million DBpedia records into Virtuoso. What we are doing now is to take these records, and to index them in the other underlying OSF systems (namely, the Solr full text search & faceting server). What the following steps will be doing is to load all these entities into the Solr index using the CRUD: Create web service endpoint. Once this step is finished, it means that all the DBpedia entities will be searchable and facetable using the OSF Search endpoint.

The first step is to edit the dmt.ini file to add information about the dataset to update:

[cc lang=’bash’ line_numbers=’false’]
vim /usr/share/datasets-management-tool/dmt.ini

Then add the following section at the end of the file:

[cc lang=’ini’ line_numbers=’false’]
[DBpedia] datasetURI = "" baseURI = "" datasetLocalPath = "/usr/local/data/dbpedia/3.9/en/" converterPath = "/usr/share/datasets-management-tool/converters/default/" converterScript = "defaultConverter.php" converterFunctionName = "defaultConverter" baseOntologyURI = "" sliceSize = "500" targetOSFWebServices = "http://localhost/ws/" filteredFiles = "instance_types_en.nt" forceReloadSolrIndex = "true"

Other Configurations to Speed-Up the Process

Now we will cover a few more configurations that can be performed in order to improve the speed of the indexation into OSF. You can skip these additional configuration steps, but if you do so, do not index more than 200 records per slice.

First search and edit the virtuoso.ini file. Then find the ResultSetMaxRows setting and configure it for 1000000 rows.

Then we have to increase the maximum memory allocated for the CRUD: Create web service endpoint. You have to edit the index.php file:

[cc lang=’bash’ line_numbers=’false’]
vim /usr/share/osf/StructuredDynamics/osf/ws/crud/create/index.php

Then check around line #17 and increase the memory (memory_limit) to 1000M.

Then we have to change the maximum number of URIs that the CRUD: Read web service endpoint can get as input. By default it is 64, we will ramp it up to 500.

[cc lang=’bash’ line_numbers=’false’]
vim /usr/share/osf/StructuredDynamics/osf/ws/crud/read/interfaces /DefaultSourceInterface.php

Then change 64 to 500 at line #25

Importing the DBpedia Ontology

before we start the process of importing the DBpedia dataset into OSF, we have to import the DBpedia Ontology into OSF such that it uses what is defined in the ontology to optimally index the content into the Solr index. To import the ontology, we use the OMT (Ontologies Management Tool).

[cc lang=’bash’ line_numbers=’false’]
cd /data/ontologies/files/ wget bzip2 -d dbpedia_3.9.owl.bz2 # Load the DBpedia Ontology omt --load="file://localhost/data/ontologies/files/dbpedia_3.9.owl" --osf-web-services="http://localhost/ws/" # Create the permissions access record for the administrator group to access this ontology pmt --create-access --access-dataset="file://localhost/data/ontologies/files/dbpedia_3.9.owl" --access-group="http://localhost/wsf/groups/administrators" --access-perm-create="true" --access-perm-read="true" --access-perm-delete="true" --access-perm-update="true" --access-all-ws # Regenerate the underlying ontological structures omt --generate-structures="/data/ontologies/structure/" --osf-web-services="http://localhost/ws/"

Import DBpedia Into OSF

This is the final step: importing the DBpedia dataset into the OSF full text search index (Solr). To do so, we will use the DMT (Datasets Management Tool) that we previously configured to fully index the DBpedia entities into OSF:

[cc lang=’bash’ line_numbers=’false’]
dmt -s -c dmt.ini --config-id="DBpedia"

This process should take up to 24h with that kind of server.


At that point, the DBpedia dataset, composed of 3.5 million entities, is fully indexed into OSF. What that means is that all the 27 OSF web service endpoints can be used to query, manipulate and use these millions of entities.

However, there is even much more that come out-of-the-box by having DBpedia loaded into OSF. In fact, as we will see in the next article, this means that DBpedia becomes readily available to Drupal 7 if the OSF for Drupal module is installed on that Drupal 7 instance.

What that means is that the 3.5 million DBpedia entities can be searched via the Search API, can be manipulated via the Entity API, can be templated using the Drupal templating engine, etc. Then they can be searched and faceted directly on a map using the sWebMap OSF Widget. Then will be queriable via the OSF QueryBuilder that can be used to create all kind of complex search queries. Etc.

All this out-of-the-box.

Semantic Web, UMBEL

Exploding DBpedia’s Domain using UMBEL

A couple of challenges I have found with DBpedia is that it is hard for a system to interact with the dataset and it is hard to figure out how to interpret information instantiated in it. It is hard to know what properties are used to describe individuals; and hard to know what the classes refer to. It is also hard for standalone and agent software to understand the nature of the individuals that are instantiated by DBpedia because the classes they belong to are generally unknown or poorly defined.

In the following blog post I suggest to use a method known as “exploding the domain” to try to overcome these difficulties of using and understanding DBpedia. This adds still further usefulness to DBpedia’s considerable value. This demonstration is based on the UMBEL subject concept structure.

As I will demonstrate below, this method consists of contextualizing classes in a coherent framework to explode their domains. By exploding the domain of a class, we link it to other classes that are defined by external ontologies. By exploding the domain of a class by linking it to externally defined classes, we also help standalone and agent software to understand the meaning for that class (at least if they understand the meaning of the classes that have been linked to it). Note that we are able to explode the domains by linking classes using only three properties: rdfs:subClassOf, owl:equivalentClass and umbel:isAligned.

First of all, let me give some background information about how DBpedia individuals and UMBEL named entities have been created, and how both datasets have been linked together.

How DBpedia individuals are instantiated

DBpedia is a dataset that is based on the well known Wikipedia encyclopedia. Basically DBpedia creates one individual for each Wikipedia page. Most of the individuals that are instantiated in this way are what we call a “named entity” in UMBEL’s parlance.

But to be instantiated, an individual has to belong to a class. DBpedia chooses to use Yago‘s classification system (that is based on WordNet) to instantiate those DBpedia individuals. This means that all DBpedia individuals belong to at least (theoretically) one Yago class. This means that all DBpedia individuals are instances of Yago classes (and in some rarer cases, they are also instances of classes defined in external ontologies).

How UMBEL named entities have been created

For its part, UMBEL’s named entities dictionaries come from different data sources. Currently, most all public UMBEL named entities also come from Yago (example: Aristotle), but many also come from the DBTune dataset (example: Pete Baron) or others. (UMBEL’s design allows more named entities to be plugged into the system as additional dictionaries at will.)

However, unlike DBpedia, we do not use Yago’s classification system to instantiate these named entities. And unlike Yago, we do not use the WordNet classes to instantiate the named entities either.

The current UMBEL subject concept structure is based on OpenCyc. This means that the relations between the classes that instantiate the UMBEL named entities come from the Cyc knowledge base.

So while we use Yago’s named entities (from Wikipedia) as a starting basis, we instantiate them using the UMBEL subject concept classes instead of the WordNet classes. So, basically, we have switched the WordNet conceptual framework for the UMBEL (or OpenCyc) one.

But, how did we create these UMBEL named entities, instantiated using UMBEL subject concept classes and based on Yago? Here is the linkage path:

Yago classes –> WordNet synsets <– Cyc collections <– OpenCyc classes <– UMBEL subject concept classes

Et voilĂ  !

How UMBEL named entities are linked to DBpedia individuals

OK, so now how do we link UMBEL named entities to DBpedia individuals? It is simple. Remember that DBpedia individuals have been created from Wikipedia pages. Also remember that Yago individuals come from the same Wikipedia pages. We can then make the link between the individuals from DBpedia and the individuals from Yago based on Wikipedia URLs.

Exactly the same logic applies for linking DBpedia individuals to UMBEL named entities.

The end result of this linkage is that we have UMBEL named entities that are the same as DBpedia individuals. The difference is that the UMBEL named entities are now instances of UMBEL subject concepts: a totally different conceptual structure.

Remember that these named entities are contextualized in a coherent conceptual framework. And this characteristic means a lot for what is yet to come.

Web services to search and visualize these named entities

We created two new web services on the UMBEL web services home page (the user interface to these web services; the endpoints will be released later) to help people interact with these named entities:

  1. The “Search Named Entities Dictionaries” web service
  2. The “Named Entity Detailed Report” web service

The first web service lets you search amongst all publicly available UMBEL named entities dictionaries.

The second web service lets you visualize detailed information about any named entity.

This information page shows you the full scope of information about a named entity: which class it belongs to (subject concept classes as well as external classes); which other individuals, from other datasets, are identical to them; examples of web services that get queried with information about this named entity; etc.

Exploding the domain of Plato

Now that this background information has been established, let’s take a look at what is happening when we link DBpedia individuals to UMBEL named entities: how that actually works to explode the domain.

Let’s take the example of dbpedia:Plato. This individual is currently defined in DBpedia as:

  • yago:AncientGreekPhysicists
  • yago:PhilosophersOfLanguage
  • yago:PhilosophersOfLaw
  • yago:PoliticalPhilosophers
  • yago:AncientGreekVegetarians
  • yago:AcademicPhilosophers
  • yago:Philosopher110423589

Fine, but what does this mean? What if my system doesn’t know any of these classes? We, as humans, know that Plato is a person, a human being. But it is totally another story for a software agent.

What we want to do here is to explode Plato’s domain to try to find a meaning that my software system can understand.

In UMBEL, the “Plato” named entity is defined as an umbel:Person and an umbel:Intellectual. If you take a look at the detailed report for these two subject concepts, you will be able to see in the section “Broader Subject Concepts” the super-classes that Plato belongs to. So we know that Plato is a social being, a homo sapiens, etc. This is basically what happens with Yago too, except that the conceptual structure (the way to describe the entity) differs.

However one thing that is happening is that we exploded Plato’s domain with classes defined in external ontologies. As you can notice in the sections “Broader External Classes” and “Equivalent External Classes”, Plato is also a: foaf:Person, a foaf:Agent and a cyc:Person.

This means that if my software agent doesn’t know what a “yago:Person100007846” means; it alternatively may know what a foaf:Person or a foaf:Agent means. And if it knows what it means, then it will be able to properly manipulate it: to display it in a special way; to refer to it as a person; so to do whatever it can with information about a “person”.

This exploding the domain works because these external ontologies classes have been referentially linked to a coherent conceptual structure.

The inference path

Let’s take a look at the fundamental reasons why the scenario above works.

First, you, and your system, have to trust the UMBEL named entities dictionaries and the UMBEL subject concept structure to perform the inference that I will explain below. If you and your system trust these linkage assertions, then you will be able to act according to the knowledge that has been inferred.

DBpedia individuals are linked to UMBEL named entities using the owl:sameAs property. This means that DBpedia individual A is identical (same semantic meaning) as the UMBEL named entity B. They both refer to the same individual.

This means that if B is defined as being of rdf:type sc:Person (“sc” stands for Subject Concept), then we can infer that A is defined as being of rdf:type sc:Person too.

If sc:Person is owl:equivalentClass with foaf:Person, we can infer that umbel:B is a foaf:Person, so that dbpedia:A is a foaf:Person too!

We can see similar examples for exploding the domains:

Exploring ConceptualWorks, PeriodicalSeries and NewspaperSeries

In my “UMBEL as a Coherent Framework to Support Ontology Development” blog post from last week, I showed how UMBEL subject concepts acted to create context for linked classes defined in external ontologies. Since DBpedia individuals are instances of classes, and that some of these classes are linked to UMBEL, these subject concept classes also give context to those individuals!

As some examples, go ahead and take a look at the “Named Entities for …” section of these detailed report pages:

The partial list of named entities that are returned by the detailed report viewer shows named entities that mainly come form Wikipedia (so that have links to DBpedia). These subject concepts gives a coherent context to those DBpedia individuals.

You should quickly notice, for example, that dbpedia:Kansas_City_Times is not only a sc:NewspaperSeries, a sc:PeriodicalSeries and a sc:ConceptualWork. You also notice that it is a frbr:Work, a bibo:Periodical and a bibo:Newspaper.

The context created by these UMBEL subject concepts gives not only new power to linked external classes, but also to their instances, such as these DBpedia individuals!


Contexts created by UMBEL subject concepts emerge by the power of linkage that exists between all the subject concepts, and the linkage between those subject concepts classes with classes defined in external ontologies. These contexts are consistent because of the coherence of the structure that is powered by OpenCyc (Cyc).

So far, most Linked Data has been about the “things” or named entities of the world, organized according to either Wikipedia categories or WordNet. These structures may have some internal structural consistency, but were never designed to play the role as a coherent reference framework. The coherence of UMBEL (based on the coherence of Cyc) is a powerful contextual lever for bringing order to this chaos.

Once information gets linked to a coherent framework such as UMBEL, things start to happen; powerful things. And, with each new linkage and relation to additional external ontologies, that power increases exponentially.

I wrote this blog post to show again the power of exploding the domain using DBpedia as an example, and how UMBEL can help to use and to leverage such big datasets.