Warwick Framework: A Container Architecture for Aggregating Sets of

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METADATA STRUCTURES and PROGRAMMING for DISTRIBUTED
DICTIONARY RESOURCES in a CONTEXT of LEARNING
Volodumur Shirokov, Volodumur Manako
Ukrainian Lingua-Informational Fund
of the National Academy of Sciences of Ukraine
54, Volodimirska str., Kiev, 01601, Ukraine,
tel. (380)(44) 267-4859, e-mail: [email protected]
Katherine Sinitsa, Alla Manako
International Research and Training Center
of Information Technologies and Systems UNESCO/IIP,
40, prosp. Glushkova, 03187, Kiev, Ukraine,
tel. (380)(44) 266-6311, e-mail: [email protected], [email protected]
Abstract. A model to support Autonomous Learning by learning-centric Dictionaries (ALD model)is suggested. Dictionary metadata
structures and programming based on ALD model as well as a unified dictionary player and some case studies are described.
Анотація. У доповіді наведена наша точка зору на роль словникових ресурсів у розподілених електронних/цифрових середовищах
у контексті навчання і структури метаданих та програмування для цих ресурсів.
1.0 Introduction
Present trends in the job market require people to adapt their knowledge and skills to changes in the workplace,
thus, making it crucial to have access to life long autonomous learning (EC’95g). The old metaphor restricted the
role of a dictionary to the auxiliary storage of specific portions of information that can be retrieved upon a direct
user's request. The role of interactive dictionaries may be extended to the one of a tool which supports various
learning activities, facilitating the formation of learner’s mental knowledge structures, suggesting a conceptual
framework for domain knowledge, ensuring the incremental incorporation of new knowledge guided by the
learner's professional goals (Colazzo, 1996), (Sinitsa&Mizoguchi, 1998), (ComNEd'99), (EDT, 1999). A
forefather of Russian academic science, M. Lomonosov recommended to learn mathematics, because “it puts
one’s mind into order”, so could good interactive dictionaries.
Design and maintenance of the dictionary resources require extensive programming skills and enormous manpower to create and manage a variety of software tools for the identification, extraction, ordering, transfer,
storage, maintenance, representation of dictionary resources. Much of the progress of modern software
development is based on the concepts of “interoperability”, “adaptability” and “reusability”. Interoperability of
dictionary resources is defined as an ability to ensure a usage of dictionary resources components developed in
some place by some set of tools or for some platform, in another place, with a different set of tools or at other
platforms. Adaptabity of dictionary resources is defined as an opportunity to tailor dictionary resources content
to individual and situational needs. Reusability of dictionary resources is defined as a feature that allows for
incorporation of dictionary resources components into multiple applications. Interoperability and reusability
requires technical, economic, and legal infrastructures. Of these three conditions, the technical infrastructure is
almost certainly the easiest -- agent-based systems and computing, functional specifications, component
architecture, metadata, object-oriented programming, and true mark-up languages are likely to play an important
role.
However, there are not enough “common solutions” applicable to the process of transformation of the dictionary
resources, dictionary industry and infrastructure from a paper-based to a “digital-based” environment (WFC,
1996), (CSM, 2000) to ensure a discovery and use of interoperable, adaptable and reusable dictionary resources
in distributed digital environments.
In this paper a model to support Autonomous Learning by learning-centric Dictionaries (ALD model) is
described together with dictionary metadata structures and programming based on ALD model. It also contains a
description of a unified dictionary player and some case studies.
2.0 Background
Approaches for discovery/use of such“common solutions” may be roughly represented as a decompositioncomposition approach and “learning-centric” approach.
Decomposition-composition unformal approach. The POINTER EC Project ( POINTER) defines terminological
resources as: “ a structured set of concepts and their designations (graphical symbols, terms, phraseological
units, etc.) in a specific subject field”. For the created terminological resources and their components some
solutions are formulated unformally.
Decomposition-composition formal approach. Dictionary resources are created by decompositions of general
formal model into separate solutions with later compositions of those solutions for a various domains (MOV,
1996), (ModSUM, 1996), (DICsit, 1998). Common formal structural model of lexicographical (including,
dictionary) systems is constructed on the basis of the principles formulated for Language science and
modularization of dictionary metadata (ShirCrimea, 1994), (ShirMON, 1998), (UkrPROG98), (ShirMMS, 1999),
(Crimea 99), (CSM, 2000).
Learning-centric approach. Taking into account a general model of the lexicographic systems, dictionary
resources are created and used in a distributed digital environment in a broader context - a context of learning
(ComNEd'99), (HCI, 1999), (Crimea, 1999), (CSM, 2000). Some key components of the dictionary
environments are described in (UkrINTEI, 1999), (Crimea, 1999), (CSM, 2000).
3.0 Developing a Learning-Centric Approach
3.1 A common model to support autonomous learning by learning-centric dictionaries
We consider a user of the dictionary resources as an "autonomous learner" - an Agent (person, family, etc.) that
self-directed discover and use resources in electronic environments, trying to enlarge his/her knowledge, skills or
capabilities for professional and other activity. Thus, the autonomous learner must (ComNEd'99), (HCI, 1999):
(1) discover/use personal learning objects: learning strategies, goals, action plans taking into account both
personal (learning style, skills cards, resources, etc.) and broader (legal, social, economic, technical) context;
(2) discover/learn from personal or external resources.
Learning Virtual Environment (LVE) organized as a set of interoperable and communicating networked
tools/systems, may add value to each of its components by extension and integration of their Roles in
autonomous learning process, introduction of cognitive support for a autonomous learner.
Taking into account (1)-(2), a model to support autonomous learning by LVE can be defined as : <TEXT>
Annotation:
Annotation Name for <TEXT>: <al> /* (i.e. autonomous learning)
Short Annotation Description of <al>: model to support autonomous learning
by LVE. Description Level: <level: al>
Full Description of <al>:
al.1:
al.2/2:
al.3:
for <Right> <personal/broader context>
<to support>/<do>
the <Right> <autonomous learning>
al.4:
al.5:
al.6:
al.7:
of the <<Right> <learning resources>
at the <Right> <time>,
at the <Right> <place>,
at the <Right> <cost>
Some of rules to construct such <TEXT> are:
 the Roles of autonomous learner are supported by a Role-playing Agents (UkrINTEI, 1999) according to this
<TEXT> (Examples of Roles are shown in the Table 1). Autonomous learner is a Roles-playing Agent;
 Root(s) for LVE/( Autonomous Learner) Role-play of <level: rl> are: rl.*;
 <TEXT> is not determined ab inito the Action order for <al>;
 <TEXT> interpretation in digital environment: <...> is container abstractions for digital objects/actions
metadata with complex distributed active relationships (WFC, 1996), (DARs, 1997), (CSM, 2000);
 each <Right> (or <Best> for an Agent point of view) has various behaviors in autonomous learning lifecycles (see detail about behavior “states” of dictionary resources in (UkrINTEI, 1999)). It could be measured
in personal/broader dimensions and scales. For example, <Right> for <learning resources> is an Agent,
which supports the meeting of the following conditions: (1) <learning resources> must correspond to
standards; (2) <learning resources> must have definite multilingual extensions, etc.
Decomposition of this <TEXT> is performed as follows.
We define <<Right> <autonomous learning> as <<Right> <sequences> of <autonomous learning activity>>,
which an autonomous learner discovers, explicitly defines, orders, performs, etc.
In a “classroom” LVE, keeping balance between new and known components, defining granularity of learning
units, planning, sequencing and monitoring is done by a tutor, thus allowing a learner to concentrate on subject
matter. An autonomous learner has to perform both roles simultaneously, which requires reasoning at both
subject level and meta-level.
Examples of Core activities in autonomous learning are shown in the Table 1 (see also details in (ComNEd'99),
(EDT, 1999), (UkrINTEI, 1999)),
Activity
Roles
Details
Comment
+ creator
+ digital traveller
+ learner of a subject
+ evaluator
+ manager ...
Personalization
of learning
+ strategies/ goals/
plans
+ skills, capabilities
+ learning style
+ learning resources ...
Navigation
+ in knowledge space
+ discovery/use related
learning blocks
Manage-ment + educational
of learning
+ informational
+ administrative ...
of learning strategies, goals, action plans
perform navigation in knowledge space
mastery of learning objects,
of performance content (history, current, new) of selflearning
Action
Example of personalization:
for <personal/broader context> <do> the <<Right>
autonomous learning> <of highest quality of
terminological resources of the Internet>, <tailored to my
individual and situated needs>, <delivered costeffectively>
<to my home/mobile workplace> and
<on monthly basis>
where to get relevant information, how to process it and
adopt to new situations
parameters for personal profiles:
life long learning
autonomous learning
collaborative work
subject learning ...
Table 1 - Examples of Core activities in autonomous learning
Therefore, in accordance with the Table 1, <<Right> <autonomous learning>> is defined as:
<level: al.3>:
<<Right> <sequences> of:
<Right> < Role>
<Right> <Personalization of self-learning >
<Right> < Navigation >
<Right> < Management of self-learning >>, etc.
A note on usage of the term “digital traveller” in the Table 1:
“Ricky Erway's suggested a Digital Tourist metaphor (OCLC). His metaphor casts the resource seeker as a
"digital traveler" who, like a real traveler, can navigate in early stages of the process using a general "phrase
book" type of approach. However, just as a real traveler must actually learn the language of the specific culture
in order to navigate within it, the information seeker must adopt domain-specific syntax and semantics to
succeed in the discovery process, i.e. change a role of "digital traveler" to the one of “a learner of a subject”).
Further details of <Right> <learning resources >.
Interactive dictionary is a collection of “right created” (POINTER) knowledge structures/ objects, which are
relatively stable in many dimensions: time, space, used, etc . (Crimea, 1999). THUS:
 dictionary knowledge structures/objects may serve as life long learning resources for autonomous learner.
For example, a learner discovers or studies some of terminological resource in the Internet. A knowledge
object - the Term ‘Internet’ is presented in the dictionaries as TermDesignation & TermDescription.
TermDesignation (POINTER) for term ’Internet’ is a stable designation - <Internet>. Therefore, if one stores
TermDesignation together with some personal metadata (personal performance content metadata ) about this
term in his/her Personal library, then this TermDesignation will be a long-term “Entry” for life long
autonomous learning of this term;
 dictionary knowledge structures/objects (for example, the term ‘Internet’) may interact via Agents with the
related knowledge structures of glossaries (containing this term), which are embedded in or linked to learning
resources. Those knowledge structures/objects of glossaries may have links to the learning blocks (about
‘Internet’) of those resources. Thus, dictionary knowledge structures/objects may be used by a tool as a life
long road-map to generate a plan for autonomous learning of the related learning resources.
Taking into account those reasons, <<Right> <learning resources>> is defined as:
<level: al.4>:
<Right> <<<stable> <right created>
<knowledge structures/objects collections of domains>>
and <related learning resources >>.
Taking into account the above, a model of autonomous learning support by dictionaries is defined as the
following <TEXT> (In this paper a full <TEXT> is not included):
<level: al.*>
<for <personal/broader context>
<to support>/<do>
<Right> <sequences> of
<Right> < Self-role(s)-play>
<Right> <Personalization of self-learning>
<Right> < Navigation >;
<Right> < Management of self-learning>
of the <Right> <stable> and <right created>
<knowledge structures/objects collections of domains> and
<related learning resources >,
at the <Right> <time>,
at the <Right> <place>,
at the <Right> <cost>.
Interactive dictionaries, which support this Model and are naturally added/embedded in LVE, is called here
learning-centric dictionaries (LDS). The virtual environment, which supports core LDS functions is called a
Dictionary Player (DP).
Such full <TEXT> we call model to support autonomous learning by learning-centric dictionaries (ALD model).
3.2 Towards Learning-Centric Interactive Dictionaries
Currently, all spectrum of dictionaries exists in an electronic form: from short glossaries, to multilingual
dictionaries, explanatory dictionaries and encyclopedia. Dictionaries are created by context-dependent methods
and procedures, they have complex knowledge structure, diverse ordering systems, context-dependent behavior,
etc. Modern computer-based technologies freed these products from space and time restrictions, added new
media and dynamic objects, connected knowledge objects by distributed and dynamic relationships.
Interactive dictionaries vary in size, purpose, explanation style, interface, access to entries, and use of illustrative
media, but have some specific features in common. These features, important for the dictionary support of
autonomous learning, are the following.
Interactive dictionaries are stable collections of formally defined and viewed from multiple points knowledge
structures/objects for autonomous learning. The dictionary knowledge structures/objects are formally defined by
dictionary mark-up language (ShirMON, 1998) and represented by information fragments collections (CSM,
2000). Some fragments have names indicating their content, which are used in navigation; some fragments
collections may have their own catalogs; some fragments may have complex relationships and links to each
other. Dictionary knowledge structure consists of a large number of sub-structures, which are multi-dimensional,
colorful and allows for several views. Thus, a dictionary being a specific sublimation of knowledge, should be
able to demonstrate its content from different viewpoints.
Few tools, which support autonomous learning of dictionary knowledge. Interactive dictionaries do not support
an explicit order for autonomous learning of dictionary knowledge, i.e. they do not identify appropriate
fragments of dictionary knowledge, put pieces together and evaluate the learning solutions. Navigation process
in dictionary knowledge structures/objects is long and complicated, and must be actively present in a “memory
of the autonomous learner”. Moreover, autonomous learning of dictionary knowledge requires
decomposition/composition of various iterations and/or cycles: goals - objectives - plans - performance evaluation (ComNEd'99), (EDT, 1999). For example, autonomous learning of dictionary knowledge requires
(HCI, 1999), in particular a local learning (mastering a specific concept, extending and upgrading one's
knowledge of a separate concept, study of examples) and integration of knowledge (comparison of some
concepts within a class, tracing relations between the concepts, exploration of the related concepts).
Based on ALD model, we suggest the following solution for the reconstruction of a interactive dictionaries into
LDS:
1. Separation of dictionary knowledge structure from its context-dependent behaviors - a tools. It allows for
creation of a unified Dictionary Player for various and distributed dictionary knowledge structures/objects.
2. To support cataloging of dictionary resources on the Net. Currently dictionary resources are cataloged by
common used surrogates (Crimea, 1999) such as bibliographic descriptions in MARC format. Structure and
parameters of those descriptions are intended for description of any resource, thus, a learner cannot determine
specific parameters of dictionary objects, the depth of their description etc.
3. Dictionary Player is based on ALD model and supports learning “Wish-Trees” (see details about “Wish
Tree” in (EDT, 1999)):
 agent role-play and plan-oriented autonomous learning of stable and right created knowledge
structures/objects collections and related learning resources;
 decomposition/composition of autonomous learning content;
 structuring and ordering of that content and its cataloging into personal library/database;
 simulations of personal performance content and/or of missing features;
 multiple linked representations from different viewpoints of ALD model and performance content with
various linked metadata collections for learner and/or agents. For all this, performance content (history,
current, new] is more explicit for autonomous learner.
4.0 Programming for Learning-Centric Interactive Dictionaries
An autonomous learning is based on ALD model and is executed by programs, which are also based on ALD. A
simple example of a navigation program fragment:
/* Example of common fragment;
/*
for [<+personal/broader context>]
do [[ <+Right> <+Learning>] [<+Right> <+KnowledgeStructures/Objects>]]
while [[<+Right> <+Time>] .AND. [<+Right> <+Place>& <+Cost>]]
enddo
/*
/* (Where: “+” = one or more elements required; à [] - designation of container
/* (WFC, 1996), (CSM, 2000); <+ personal/broader context > and <+Right> is Distributed /*Active
Relationships (DARs, 1997), (CSM, 2000);
/*
/*
/*EXAMPLE: some personal fragment
/*
/* [<Annotation>] = [<Goal2>]
/* “Learn phraseological usage of a term in multilingual glossaries”
/*
/* [<Goal2>]= [[<Learning>][<Phraseologisms>]
/*
/* [<Annotation>] = [<Objective>]
/* “Navigation
/* in mono and multilingual glossaries”
/*
/* [<Objective1>] = do [<PerformNavigation>]
/*
while [<Glossary>] = [<Glossary1> .or. <Glossary2> ]
/*
enddo
/*
/* [<Objective2>] = do [<EvaluateNavigation>]
/*
while [<Glossary>] = [<Glossary1> .or. <Glossary2> ]
/*
enddo
/*
/* [<Plan1>] = [[<Objective1>][<Objective2>]]
/*
for [[<+MyWorkPlace>]=<Home>.or.<Mobil>]]
do [<PerformPlan1>]
while [[<InternalLinkOf SemanticStates>]= [<PhraseologicalLink>]]
enddo
/*
/* [<PerformPlan>]=if [[<+CurrentEntry>]=[<+SemanticState>]]
/*
then [<NavigateInSemanticStates>]
/*
else [<GotoPlanEntry>]
/*
endif
/*
/* about SemanticStates see in (ShirSEM, 1999).
/* ENDofEXAMPLE
5.0 Metadata Structures for Learning-Centric Dictionary Resources
Dictionary environment in any of its behavior states has: <+Agent>, <+Role>, <+Action>, <+Information
Resource> (UkrINTEI, 1999) and <+Relationships> (CSM, 2000). Agents transform Information Resources by
the Actions. Agents may play different Roles in transformarions of the performance content. Agents and
Information Resources combined by the Actions support the Roles.
Examples of Agents are: persons, organisations, search instruments, dictionary authoring environments, agents
of third-party such as brokerage systems. Examples of Information Resources are: a dictionary-style document,
standards and/or base, WWW-pages. Examples of Actions are: originate, deposite, reformat, deliver. Examples
of Roles see in Table 1.
Metadata is a descriptive label, that can be used to index resources to make them easier and more convenient to
find and use. Such labels are “ data about data” and are refered to as “metadata” (WFC, 1996). We use a more
elaborated definition of metadata given by Lorcan Dempsew and Rachel Herry “Metadata is data associated
with objects which relieves their potential users of having full knowledge of their existence or characteristics. It
supports a variety of operations” (Dempsey, 1998).
Therefore, <+Agent>, <+Role>, <+Action>, <+Information Resource> and <+Relationships> have data, which
are associated with them and used for support of the autonomous learning. Definition/setting/using of those
metadata is based on ALD Model. Dictionary metadata structures are described in (ShirMon, 1998), (HCI,
1999), (Crimea 99), (CSM, 2000).
6.0 Case Studies
Ukrainian Lingua-Informational Fund of the National Academy of Sciences of Ukraine is working on the
design of an electronic version of new generation of academic Ukrainian general purpose dictionaries (de facto
dictionary standard in Ukraine) with a unified Dictionary Player. This product is oriented at the use in
educational bodies, libraries, state departments and private companies and is supported by the leading language
and learning centers of Ukraine. This project has been initiated by the Decree of the President of Ukraine “ On
the development of the National dictionary bases” and is financed by the State innovation fund of Ukraine.
STACCIS project, in which International Research and Training Center UNESCO/IIP has been involved, was
focused on dissemination of information about telecommunication applications, tools and techniques through the
network of so-called "information and demonstration centers". Besides a number of electronic catalogs
comprising references to electronic publications, software, tools, services, distance courses and various
collections, it will include as an extension a Basic Internet Glossary with a unified Dictionary Player for novices
in telecommunications to ease search of relevant topics and facilitate professionally-oriented learning.
The above mentioned unified Dictionary Player based on ALD Model will support the following functions:
* definition/use of personal roles, goals, plans as learning objects for autonomous learning of our dictionary
collections;
* navigation in collections of the learning objects and additional functionality to support navigation in a broader
space;
* structuring and ordering of personal performance (history, current, new) content and its cataloging with an
annotations in the personal library/database;
* simulation of personal performance content and of missing features;
* evaluation of behaviors;
* management of self-learning;
* cognitive support for the mentioned functions.
7. 0 Conclusion
Existing interactive dictionary model ab inito is a closed system. Autonomous learners as users of this closed
system cannot resolve their problems in an open LVE. A fundamental question of the approach to a interactive
dictionary as a closed system is: “How a dictionary may be linked to other dictionaries or third-party resources
situated outside of the closed environment?” In a real world, interaction of interactive dictionaries with an
“external world” is not supported. An autonomous learner needs more resources than may exist in any “closed
world” of interactive dictionary. Therefore, an ideal solution for autonomous learning: dictionary model is an
open system.
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