Interoperability for searching Learning Object Repositories

Interoperability for searching Learning Object
Repositories: The ProLearn Query Language
Stefaan Ternier(2), David Massart(3), Alessandro Campi (1),
Sam Guinea(1), Stefano Ceri (1), Erik Duval(2)
(1) Politecnico di Milano (POLIMI), Italy
(2) Katholieke Universiteit Leuven (K.U.Leuven) / ARIADNE Foundation, Belgium
(3) European Schoolnet (EUN)
With the worldwide adoption of the Internet as a ubiquitous platform for exchanging
information, new opportunities are available for sharing educational experiences and
material. Thousands of institutes worldwide offer access to courses, lessons, seminars,
and exercises, typically stored in repositories and offered to users under a variety of
formats. Due to their educational nature, these contents are often referred to as Learning
This article describes the "ProLearn Query Language", a query language that we have
developed for repositories of learning objects. PLQL is primarily a query interchange
format, used by source applications (or PLQL clients) for querying repositories (or PLQL
In defining PLQL, we have combined exact search, used for selecting learning objects by
means of queries on their metadata, and approximate search, used for locating learning
objects by means of keyword-based search. A query in PLQL contains both "exact
clauses" and "approximate clauses", where each clause is syntactically well-defined;
exact clauses match keywords against metadata with a known structure, while
approximate clauses match keywords freely, either against metadata or against indexes
based upon the documents’ contents.
In this article, we give a precise description of the semantics of PLQL, concerning both
kinds of clauses and their mutual relationship and describe two experimentation efforts
around PLQL: one involving the ARIADNE repository and the other the EUN Learning
Resource Exchange initiative.
1 Introduction
With the worldwide adoption of the Internet as a ubiquitous platform for exchanging
information, new opportunities are available for sharing educational experiences and
material. Thousands of institutes worldwide offer access to courses, lessons, seminars,
and exercises, typically stored in repositories and offered to users under a variety of
formats. Due to their educational nature, these contents are often referred to as Learning
The IEEE Learning Technology Standards Committee [1] defines a learning object as
“any entity, digital or non-digital, that may be used for learning, education or training”.
Over the last decade, a substantial industry and research community has developed
around the notion of such reusable learning objects, addressing a wide variety of contexts
including training, schools and academia.
A crucial problem faced by the learning community is how to produce and deliver quality
content for online learning experiences. Thousands of educational institutes worldwide
offer access to their learning objects. By improving share and reuse of learning objects,
the cost of creating them declines. A first problem to address is that of finding the most
useful learning objects for performing a given educational task. For this purpose, learning
objects are collected within dedicated repositories that can be accessed by query and
search engines.
Every learning object can be tagged with metadata, which is descriptive information that
allows the learning object to be found easily. Figure 1 depicts an example of a learning
object together with associated metadata.
The most relevant standards for describing learning objects are LOM, Dublin Core, and
Figure 1: An example of a Learning Object and its associated LOM metadata.
The IEEE Learning Object Metadata (LOM) [1] is a hierarchical metadata standard
usually encoded in XML, published by the IEEE in 2002. Its purpose is to enable the
description of learning objects through attributes that include the type of object, author,
owner, terms of distribution, and format, as well as pedagogical attributes, such as typical
learning time or interaction style. LOM is based on early work in ARIADNE [3] and IMS
Dublin Core (DC) [5] is a standard for generic resource descriptions. The simple DC
metadata element set consists of 15 elements, including title, creator, subject, description,
publisher, contributor, date, type, format, identifier, source, language, relation, coverage,
and rights.
MPEG-7 is an ISO/IEC standard for describing multimedia content. MPEG-7 Multimedia
Description Schemes (DSs) are metadata structures in XML that facilitate searching,
indexing, filtering, and access [24].
Learning objects in repositories are usually described by metadata. Therefore, access to
learning objects can take advantage of queries upon metadata for selecting the objects
that are most suited to the needs of learners or teachers. In addition, many learning
objects include textual material that can be indexed, and such indexes can also be used to
filter the objects by matching them against user-provided keywords.
This article describes PLQL, a proposal for a simple query language standard for
retrieving learning objects from heterogeneous repositories. PLQL is primarily a query
interchange format, used by source applications (or PLQL clients) for querying
repositories (or PLQL servers). PLQL has been designed with the goal of effectively
supporting search over LOM, DC and MPEG-7 metadata. However, PLQL does not
assume or require these metadata standards.
Figure 2 shows an example of an architecture, set up within the GLOBE initiative,
interconnecting several networks of learning object repositories. Clients direct queries to
a federated search engine, which in turn forwards the query to a number of repositories
and returns the answer to the client.
Search Client
Search Engine
SQI Gateway
Figure 2: The GLOBE network uses PLQL in combination with SQI
The work presented in this article builds on previous standardization work in the area of
technology-enhanced learning. The CEN/ISSS Simple Query Interface (SQI) [7][15]
specifies an abstract search protocol that can be bound to various technologies and has
been designed to support external query languages and metadata standards. The SQI
protocol builds the software interconnections represented in Figure 2 and enables the
transferring of a query from a client to a server, but it does not include a query language.
Therefore, the interaction between a client and a server takes place by using a query
language agreed between each client-server pair. PLQL aims to be the query language of
choice in this context.
2 Definition of PLQL
PLQL is based on existing language paradigms, and aims to minimize the need for
introducing new concepts. Specifically, we have borrowed approximate search concepts
from Contextual Query Language (CQL) [13], a well-established language used for
library search. Given that an XML binding is available for all relevant metadata
standards for learning objects, we have also decided to express exact search by using
query paths on hierarchies, borrowing concepts from XPath [18]. Thus, PLQL combines
two of the most popular query paradigms, allowing its implementations to re-use existing
technology from both fields: approximate search (using information retrieval engines
such as Lucene) and exact search (using XML-based query engines).
The result of a PLQL query is normally a set of URI’s pointing to documents, possibly
augmented with meta-information describing them; the actual retrieval of each document
is a task that should be performed by the PLQL client. If the query includes approximate
clauses, then the result is an ordered set; ordering of result URI’s is determined by the
PLQL server and takes into account approximate clauses. The implementation of PLQL
can therefore use existing technology for both exact search (using, e.g., XML-based or
relational database technology) and approximate search (using, e.g., information retrieval
engines such as Lucene). Language implementations consist primarily of translators of
the two kinds of clauses to the corresponding engines, because the adopters of PLQL are
not concerned with "query optimization".
In designing PLQL, we have also aimed at allowing very simple repositories to support at
least a subset of the functionalities. Thus, one of the main concerns of the language
design was to provide progressive levels with increasing expressive power. This permits
different repositories to support PLQL levels according to their specific capabilities. The
structure of the result returned by a PLQL query is defined similarly.
We next present the current specification of PLQL, which introduces three query levels
and four query result levels. For each level, we present the syntax and the semantics, and
we provide the motivations that have led us to introduce the level.
2.1 Query Levels
Initially, the SQI initiative was bootstrapped using an extremely lightweight query
language. This query language was the minimum common denominator in terms of the
search features all the partners had in common: an approximate keyword-based search.
Integrated as level 0 of PLQL, this query language enables a low cost of adoption.
Unfortunately, however, its expressive power is not always sufficient. Although we
envision that more levels will be required (and some query language features have
already been classified according to a level hierarchy which goes up to 5), only the levels
0-2 have been defined in detail, as they serve the needs of the repositories that we have
considered so far. The remaining three levels may be detailed in future versions of the
2.1.1 Level 0
As previously mentioned, level 0 mainly covers simple conjunctive approximate search.
Search terms must match the metadata or the textual content of the learning objects
themselves. All the search terms must be matched for the object to be selected. The
implementation of ranking is delegated to the repository being searched. If no ranking
method is explicitly specified by the PLQL client, ranking can be based on the relevance
of keywords. Notice that level 0 is absolutely metadata schema independent. This is
obviously a limitation in expressive power, but it introduces clear advantages in federated
searches, since no mediation is necessary.
The syntax is defined by the following Backus Naur Form productions:
0-1: PLQLQuery ::= approximateClause
0-2: approximateClause ::= operand | '(' approximateClause ')' | approximateClause 'and'
0-3: operand ::= term1 | term2 | integer | real
0-4: term1 ::= charString1
0-5: term2 ::= charstring2
0-6: charString1 ::= Any sequence of characters that does not include any of the
following: whitespace, tab, ‘(‘, ‘)’, ‘=’, ‘<’, ‘>’,’“’, ‘/’, ‘\’, ‘.’
If the final sequence is the reserved word 'OR' (case insensitive), its token is returned
0-7 charString2 ::= Double quotes enclosing a sequence containing any character except
double quote (unless preceded by backslash ‘\’). All backslashes that do not precede a
double quote are a part of the character string.
0-8: integer ::= [0-9]+
0-9: real ::= [0-9]*\.[0-9]+
Level 0 is identified by the following URI:
The Simple Query Interface offers a setQueryLanguage method through which a source
can set the language and level of the queries it will submit. With this URI, a PLQL client
can indicate that a PLQL level 0 will be submitted.
The following are valid examples of level 0 queries.
“ship” and “boot”
(ship and boot) and schooner
2.1.2 Level 1
As previously mentioned, the expressive power of level 0 is not always enough.
Communities such as EUN, delivering quality content to schools all over Europe,
expressed a strong need for extending queries with the ability to specify selection criteria
based on the language of the learning object and the age range for which it is targeted.
Authoring applications, such as the ALOCOM [9] plug-in for MS-office, required the
ability to select content based on the document type (e.g., slide, slide set, image, diagram,
etc). These and other simple extensions are tackled by PLQL level one.
Since many metadata schemas are hierarchical structures, level 1 introduces the notion of
paths (separated by dot ‘.’ symbols) for navigating them. Level 1 also assumes that
metadata will be either of LOM, DC, and MPEG-7; therefore, the path’s root can only be
one of the following: ‘dc’, ‘lom’, and ‘mpeg-7’.
Level 1 mixes exact and approximate search criteria. Therefore, we need to clarify the
semantics of having both in the same query. Exact clauses are executed first to produce a
result set, which is then pruned by executing the approximate clauses. The latter are also
responsible for the ranking of results. When the exact clauses do not match any metadata,
an error code is returned to the query designer, who can accordingly modify the query
being issued.
However, we also allow designers, when they are defining the
alternative behavior for those times when exact clauses do not
Designers can require that, in such a case, the exact clauses
approximate query keywords. If this happens, a special code is
designer can know that these alternative semantics have been used.
query, to specify an
match any metadata.
be transformed into
returned, so that the
Level 1 only supports conjunction, meaning that all exact clauses need to be considered.
Moreover, we only allow the equality predicate for exact clauses.
Both level 0 and 1 allow for clauses to be surrounded by parentheses. Note that in these
two levels, their use cannot change the semantics of a query.
Notice that the definition inherits already existing productions from PLQL level 0. If we
present a new production rule with the same number as an earlier production, the old one
is replaced by the new one.
1-1: PLQLquery ::= clause
1-10: clause ::= exactclause | approximateclause | '('clause')' | clause 'and' clause
1-11: exactclause ::= standard'.'path operator operand
1-12: path ::= term1 | path '.' path
1-13: operator ::= '='
1-14: standard ::= 'dc' | 'lom' | 'mpeg-7'
Level 1 is identified with the following URI:
The following queries are valid level 1 PLQL queries.
lom.general.title = "ship" and lom.general.language = "en"
lom.classification.keyword.langstring.string = “schooner” and (ship and boot)
2.1.3 Level 2
Level two of PLQL takes into account the hierarchical nature of Learning Object
Metadata. Query Languages such as CQL, work well for flat metadata schemas, such as
DC, but they fail at exploiting the hierarchic features of LOM or MPEG-7. These changes
introduce new expressiveness both in the exact clauses and in the approximate ones.
For exact clauses, level 2 makes it possible to use disjunction and all the conventional
comparison predicates, and not just conjunction and equality as in level 1. In addition,
path expressions can now use parentheses. This allows traversing the hierarchical
structure to stop at given nodes and express criteria on several of the descendants. Level 2
is also open to arbitrary namespaces beyond ‘lom’, ‘dc’, and ‘mpeg-7’.
For what concerns approximate clauses, we introduce the use of the ‘exact’ symbol to
indicate exact string matching.
Once again, already defined productions are replaced by new ones that have the same
production number.
2-1: PLQLquery ::= clause
2-2: approximateClause ::= operand | '(' approximateClause ')' | approximateClause
boolean approximateClause
2.10: clause ::= exactclause | approximateClause | clause 'and' clause | '('clause')'
2-11: exactclause ::=pathexpr | '(' exactclause ')' | exactclause boolean exactclause
2-13: operator ::= '=' | '>' | '>=' | '<' | '<=' | 'exact'
2-14: standard ::= 'dc' | 'lom' | 'mpeg-7' | term1
2-15: pathexpr ::= path operator operand | pathExp
2-16: pathExp ::= path '.' pathExp | '(' selector boolean selector ')'
2-17: selector ::= path operator operand | selector boolean selector | '(' selector ')' | '('
selector boolean selector ')'
2-18: boolean ::= 'and' | 'or'
Level 2 is identified with the following URI:
The following queries are correct level 2 PLQL queries.
lom.general.(title.langstring.string = "ship" and (language = "de" or language = "en"))
lom.lifecycle.contribute.(role exact “publisher” and entity = “ARIADNE”)
lom.relation.(kind exact “isreferencedby” and resource.identifier.entry = “1082-9873”
and resource.identifier.catalog exact “ISSN”)
2.2 Query Result Levels
Besides the query format to be exchanged, we also need to agree on the results format in
order to complement the SQI protocol and achieve full search interoperability between
learning object repositories. The current PLQL specification defines four levels of query
result formats with increasing levels of detail, ranging from a format that only includes
the number of results, to a format with detailed specific metadata regarding these results.
When issuing a PLQL query through SQI, the designer is required to specify the desired
result level. In addition, the designer can also specify two optional parameters:
1. The ranking method to be used server-side for result extraction. Examples of
ranking methods are: ranking results based on the amount times a learning object
has already been downloaded or ranking based according to the cumulative
relevance of the keywords.
2. The name of the standard to be used (LOM, DC, MPEG-7, …) when returning
metadata with the results.
The four levels are defined incrementally as follows:
Level 0: the number of results must be returned.
Level 1: the results must also contain the URIs for the learning objects, which can then be
retrieved by the client. If ranking is available server-side, the results are returned in an
ordered way, so that the “best” results are presented first.
Level 2: metadata information must be added to the result set. We do not express limits
on what is to be returned. However, we expect at least the learning object title, author and
language to be included.
Level 3: a numeric ranking value is added for each result, as well as an identifier for the
ranking method that has been used if it is available.
The Backus Naur Form definition follows:
1: PLQLRES ::= '' level [‘/’ standard ‘/’ method
[‘/’ standard ‘/’ method] is optional in level 0 and compulsory in the other levels
2: level ::= '0'|'1'|'2'|'3'
3: standard ::= 'dc'| 'lom' | 'mpeg-7' | term1
4: method ::= string
Result example
In the following example, the query has returned a level 3 result using the ‘lom’ metadata
standard. This is indicated by the URI:,
where 3 indicates the level and 'lom' indicates the selected standard. The result is bound
in XML.
<?xml version="1.0" encoding="UTF-8"?>
<Results xmlns:xsi=""
<Record position="1" rankingValue="80">
<lom xmlns="">
<string language="en">ship</string>
<string language="nl">boot</string>
<string language="en">schooner</string>
<string language="nl">schoener</string>
3 Implementation of PLQL
PLQL was designed in a way that allows for easy mapping to various paradigms for
metadata management. So far, we have mapped PLQL to Lucene, XQuery, and SQL.
Apache Lucene is an important deployment context for PLQL as this open source toolkit
for text indexing and searching is widespread and easy to use.
XQuery is the W3C recommendation for querying XML data; with this mapping, all
PLQL levels can be mapped and executed on XML database systems.
All mappings were done following the same principles: For each level of PLQL, flex
technology was used to generate a lexer that maps a PLQL input stream to tokens. These
tokens are to be interpreted by a yacc parser that closely follows the structure of the
PLQL statements. Every BNF statement corresponds to a yacc rule that maps the
statement to a concrete query.
Mapping to Lucene
Lucene queries apply to documents that contain one or more fields, where every field is a
name-value pair. For a given LOM instance, a list of name-value pairs can be easily
generated by using XML path expressions as names. Table 1 illustrates how the simple
LOM instance, depicted in Figure 1, is mapped to a Lucene document:
Table 1.
Lucene document
<lom.classification.keyword.langstring.language> en
<lom.classification.keyword.langstring.language> nl
This serialization into Lucene documents is straightforward; hence, setting up a Lucene
index of metadata for use in PLQL is very easy.
3.2 Mapping to XQuery
Mapping PLQL to XQuery requires generating suitable flwr expressions, as suggested by
the following examples in Table 2:
Table 2.
“Ship” and Schooner
Corresponding XQuery
for $lom in /lom
contains($lom, “Ship”) and
contains($lom, “Schooner”)
return $lom
lom.general.title.langstring.string =“Ship” for $lom in /lom
and lom.general.language = “en”
and @language="en"]
return $lom
and language="en") and “Schooner”
for $lom in /lom
and @language="en"] and
contains($lom, “Schooner”)
return $lom
4 PLQL Experimentation
The ARIADNE repository toolset features a metadata store component that exposes its
functionality through web services [19]. We have implemented an SQI interface with
support for PLQL on this metadata store, integrating the previously discussed mappings.
A Lucene index offers support for PLQL level 0 and 1, while PLQL level 2 support is
offered through XQuery. Currently, both IBM DB2 and eXist, an open source project, are
supported for executing XQuery statements.
The ARIADNE query and indexing tools enable the sharing and reuse of learning objects.
Through PLQL, this tool now supports faceted search. A search box enables a PLQL
level 0 search to be expressed. Once results have been retrieved, the user can, for
instance, select the “higher education” facet, which will result in the PLQL level 0 query
being rewritten as a level 1 query. For example, a PLQL query like “Ship” can be refined
in this way to:
“Ship” and lom.educational.context = “higher education”
As a result of integrating PLQL and SQI in this query tool, the tool can now be reused in
a loosely coupled fashion with other learning object infrastructures that implement the
same specifications. Through this experience, we learned that some features are not
supported by PLQL. For instance, faceted search gives a better user experience when a
tool displays the amount of results per facet. Future work will focus on profiling the
PLQL results so that this facet information can be returned as part of a response.
Figure 3: The ARIADNE query and indexing tool
OAI-PMH [23] is a protocol for metadata harvesting (i.e., selecting metadata records
from repositories based upon their identity, the date of their last modification, and their
membership in predefined sets). As suggested by Sanderson et al. [10], we implemented
an OAI-PMH component that builds on SQI and PLQL. The implementation requires a
target to support a minimal LOM metadata application profile with mandatory support for
lom.general.metaMetadata.identifier and lom.metaMetadata.contribute. Support for the
identifier is necessary for implementing the OAI-PMH getRecord method that retrieves a
record, using the record identifier. Selective harvesting in OAI-PMH requires datestamps
to be available in the metadata. When harvesting all records that are altered between two
dates, all records are selected that have the contribute element with role “creator” and a
date that falls within the specified range. Optionally, support for OAI-PMH sets can be
mapped to a classification structure or to other LOM metadata fields. Furthermore, the
minimal LOM application profile requires the number of contribute metadata elements in
the lifecycle category of LOM with role “creator” to be exactly one. Doing so, a datestamp based OAI-PMH request can be mapped to queries with the following structure:
lom.metaMetadata.contribute.(role = “creator”
and date > 2000-01-01 and date < 2006-05-05)
4.2 PLQL in the EUN Learning Resource Exchange
The Learning Resource Exchange (LRE) is a pan-European federation of learning
resource repositories [12]. It provides the means to unlock the educational content hidden
in digital repositories across Europe and share it among all partners of the LRE and their
users. The service is offered to actors providing digital content: ministries of education,
regional educational authorities, commercial publishers, broadcasters, cultural institutions
and other non-profit organizations offering extensive but heterogeneous catalogues and
repositories of online content to schools.
From a technical standpoint, the LRE consists of a brokerage system [14] to which
independent systems (e.g., learning resource repositories, educational portals, learning
(content) management systems, learning resource repositories) connect to share learning
resources in a federated way. The LRE repositories are heterogeneous. They were
designed independently and rely on very different technologies to manage descriptions of
learning resources (e.g., relational databases, XML databases, object databases, file
indexes, and other ad hoc solutions). The LRE aims at federating these repositories by
providing a uniform access to their content through federated searching.
Figure 4: LRE Federated Searching.
A federated search consists of an educational portal end-user (e.g., a teacher or a pupil)
querying all the metadata repositories available on the LRE for references of learning
resources matching the search criteria. The portal sends users’ queries to the brokerage
system that propagates the query to all the repositories of the federation (Figure 2A). The
repositories process the query and return the results to the brokerage system that then
forwards the results to the system that originally sent the query (Figure 2B).
During LRE federated searches, interoperability is achieved by using the simple query
interface (SQI) [15] in combination with PLQL and the LRE Metadata Application
Profile (LRE-AP) [16], an application profile of the IEEE Learning Object Metadata [1]
adapted to the context of primary and secondary schools in Europe.
The LRE-AP consists of a conceptual schema (named information model) from which
different bindings can be derived (e.g., an XML schema, a relational schema). The LREAP conceptual schema is used as an LRE federated schema that can be referred to when
querying the federation. Its XML binding is used as an exchange format for learning
resource descriptions (i.e., query results).
PLQL was chosen as the LRE query language for its ability to express queries as
arbitrary constraints on the LRE-AP conceptual model, including the 12 basic query
types (and their Boolean combinations: and, or, not) identified by LRE teachers and
pupils as necessary to support their learning discovery activities.
These 12 queries consist of searching for resources:
1. in a given language (LOM 1.3)
2. described by metadata in a given language
3. of a given structure
4. with a given status (e.g., draft)
5. targeting an audience within a given age range
6. licensed under a given creative commons license
7. authored by a given author
8. of a given "learning resource type"
9. created at a given creation date
10. of a given mime-type
11. covering a given curriculum subject (competencies)
12. described with a given LRE thesaurus descriptor
Among them, queries 1, 2, and 10 can be expressed in PLQL layer 1 whereas the other
ones require level 2.
Until now, the LRE has been primarily developed within the CALIBRATE and MELT
projects with support from the European Commission’s IST and eContentplus programs.
During the summer of 2007, PLQL and the LRE Context Set were implemented by most
of the federation repositories. At the time of writing (November 2007), the language is
supported by:
Repositories of resources from different EUN projects (myEurope, Insafe, Insight,
Xplora, eTwinning, CELEBRATE),
A harvester that centralizes metadata from three Austrian repositories,
The repository of the Polish portal Scholaris,
Trubar, a Slovenian repository hosted by the University of Ljubljana,
A Hungarian repository hosted by EDUCATIO Public Utility Company,
An Estonian repository hosted by Tallinn University on behalf of the Tiger Leap
foundation, and
A repository managed by the Lithuanian Ministry of Education.
This represents a total of approximately 65,000 learning assets and resources that can be
searched (using PLQL) by teachers and pupils across Europe.
Related Work
Query Languages
CQL (the Contextual Query Language) was an important starting point for our work on
PLQL. Although we have borrowed many concepts, the approaches differ in some key
aspects. CQL is a congress initiative often used in combination with the SRU/W search
protocol for library search. Its objective was to offer a very expressive query language
that would still be human readable and writable. With respect to PLQL, CQL does not
treat hierarchical metadata structures, since they are not so important in the field of
library search, where flat metadata schemas are mostly used. Commonly used metadata
structures are Dublin Core [5], MARC [20], and Zthes [2]. Another difference is in result
presentation. While CQL adopts result sorting based on metadata fields, PLQL offers
more complex ranking features that are not bound to specific fields. However, ranking
can be implemented in CQL if a metadata field contains a ranking value.
QEL (the Query Exchange Language) [21] is an RDF query language that is expressed
using Datalog syntax [22]. It has been implemented in the Edutella network, a P2P
network that provides search interoperability between learning object repositories. Like
PLQL, QEL is a metadata schema independent language. Since it is based on Datalog the
language simply returns variable bindings that match an RDF subject, predicate, or
object. This differs from PLQL, which is designed to retrieve metadata instances rather
than partial results.
Worldwide LOM Repositories
PLQL is the result of a long stream of research. A fundamental step in this research was
the ARIADNE initiative [3][19] that resulted in a network of learning repositories, based
on a three-tier architecture:
 a repository for searching, publishing and retrieving learning objects;
 an API, that is bound to web services, for enabling loosely coupled manipulation
of the repository;
 applications, such as the moodle plugin or the ALOCoM office plugin [9], that
make the knowledge pool transparently accessible from within third party
Considerable work has been devoted to increase the interoperability between ARIADNE
and other Learning Object Repositories that rely on IEEE LOM. To achieve this goal, the
ARIADNE metadata profile has been mapped into the LOM standard, itself based on an
early version of the ARIADNE metadata schema.
Since 2004, the GLOBE initiative has been established to interlink networks of learning
object repositories worldwide, so as to make all learning resources available to everyone,
at a global scale. The GLOBE network relies on SQI and LOM for the interconnection of
the networks of repositories, using a hybrid approach based on harvesting and federated
6 Conclusions
This article presents PLQL as an emerging standard for querying worldwide,
heterogeneous learning object repositories. This work has followed on the heels of
previous achievements in defining SQI (the Simple Query Interface), a unified access
point to distributed and heterogeneous repositories. PLQL is a "query interchange
format” accommodating the great diversity of the different repositories and their
capabilities. PLQL combines exact and approximate search, and supports queries on
XML-based hierarchical metadata. Our objective of supporting even very simple
implementations has led us to define incremental PLQL levels, with increased expressive
power from one level to the next. Moreover, we have also provided a detailed query
result format.
In order to achieve lasting impact with PLQL, we will combine the following approaches:
We will continue to collaborate with many of the leading repositories
( and hope to convince them to adopt PLQL, or at least
to provide support for receiving and sending queries in PLQL as an exchange
We will introduce PLQL as a candidate in either the CEN/ISSS Workshop on
Learning Technologies, the newly formed CEN Technical Committee on
Learning Technologies, or the IEEE Learning Technologies Standards
We believe that this presents an important step forward for the learning technology
community, as it completes the set of basic building blocks (that also includes SQI and
LOM) and thus creates the opportunity for a global open learning infrastructure – an
opportunity that can unleash the learning potential of a massive un-served (or underserved) community worldwide!
The authors would like to acknowledge: funding assistance of the ProLearn Network of
Excellence; participants to PLQL meetings (Peter Dolog, Geert-Jan Houben, Mikael
Nillson, Bernd Simon, Fridolin Wild, and Zhou Xuan); the members of the CEN/ISSS
Workshop on Learning Technologies for their valuable suggestions and discussions;
contributions from Xavier Ochoa and his team at ESPOL for implementing the PLQL
language and giving valuable feedback on the specification; funding assistance of the project; and funding assistance of the MELT project.
1. IEEE Standard for Learning Object Metadata:
2. Zthes,
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