Reengineering Thesauri for New Applications: the AGROVOC Example
Dagobert Soergel*, Boris Lauser, Anita Liang, Frehiwot
Fisseha, Johannes Keizer and Stephen Katz
*College of Library and Information Services, University of Maryland,
College Park
Email: dsoergel@umd.edu
Food and Agriculture Organization (FAO) of the United Nations,
Library & Documentation Systems Division, 00100 Rome, Italy
Email: {frehiwot.fisseha;
boris.lauser;
anita.liang;
johannes.keizer;
stephen.katz}@fao.org
Key features: References; Table 1,
2, 3, 4,
5, 6; Figure 1
Abstract
Existing classification schemes and thesauri are lacking in well-defined
semantics and structural consistency. Empowering end users in searching
collections of ever increasing magnitudes with performance far exceeding
plain free-text searching (as used in many Web search engines), and developing
systems that not only find but also process information for action, requires
far more powerful and complex knowledge organization systems (KOSs). The
paper presents a conceptual structure and transition procedure to support
the shift from a traditional KOS towards a full-fledged and semantically
rich KOS. The proposed structure also complies with other interoperability
approaches like RDFS and XML in the Web environment. AGROVOC, a traditional
thesaurus developed and maintained by the Food and Agriculture Organization
(FAO) of the United Nations, serves as a case study for exploring the reengineering
of a traditional thesaurus into a fully-fledged ontology. We start the
process of developing an inventory of specific relationship types with
well-defined semantics for the agricultural domain and explore the rules-as-you-go
approach to streamlining the reengineering process.
1 From thesauri to rich ontologies
1.1 The problem
Empowering end users in searching collections of ever increasing magnitudes
with performance far exceeding plain free-text searching (as used in many
Web search engines), and developing systems that not only find but also
process information for action, require considerably more powerful - and
complex - knowledge organization systems (KOS) than the classification
schemes and thesauri that currently exist. Such systems must serve the
following functions, among others:
-
Improved user interaction with the KOS on both the conceptual and the term
level for improved query formulation and subject browsing, and for more
user learning about the domain.
-
Intelligent behind-the-scenes support for query expansion, both concept
expansion and synonym expansion, within one language and across languages.
-
Intelligent support for human indexers and automated indexing/categorization
systems.
-
Support for artificial intelligence and semantic
Web applications.
All of these functions require semantic relations that are more expressive
and nuanced than the few rudimentary categories and relationships found
in traditional thesauri and classifications.
A typical scenario in information retrieval illustrates some of the
shortcomings of current free-text search engines such as Google. A farmer
is interested in finding out about rice and starts a search by entering
the string 'rice'. The results returned in response to the
query immediately indicate several problems. First, because the system
performs the search based on the actual text string entered rather than
on an interpretation of the meaning of the string, many irrelevant results
are retrieved. This occurs because the query term itself is ambiguous (i.e.
rice
can refer to the grain, to the university in Houston, or to the name of
an author, among others). Further, there are millions of results with no
apparently meaningful arrangement. To find something of possible relevance,
the user may need to click and scan page after page of the retrieved results.
Finally, the user is stuck with the results that have been retrieved; to
find other related resources, such as rice cultivation, the user
must start from the beginning again and formulate a different query, despite
the fact that the new query corresponds to concepts related to the original
query. The problem becomes evident: The biggest challenge in information
retrieval is concept identification in a specific domain
of interest!
In contrast, in a semantics-driven information retrieval system, the
system would recognize, i.e. "understand", that the string 'rice'
was ambiguous; it would then request clarification from the user as to
which of the possible meanings was intended. Only then, after the user
disambiguated the term, would the system execute the search. The system
would then retrieve only those resources that had been semantically marked
up (through manual or automatic indexing) with the concept of rice,
no matter what words or even languages are used in the resources to refer
to rice. Moreover, because the system is semantically rich, it not
only presents results that are based on understanding the user's request,
it also offers related concepts the user might not have thought of initially.
Based on a <hasPest> relation, the system could display such
concepts as rice weevil and rice moth. Searching on these
latter concepts could in turn lead to concepts on pesticides used on rice,
and so on. The system could retrieve not only information directly pertinent
to the user's query but also help the user explore and clarify the information
need and find useful related information. In this scenario, a KOS has two
functions: assisting the user with exploring the topic of the query, and
supporting intelligent automatic indexing (metadata assignment) through
statistical and syntactic-semantic analysis and "understanding" of text;
both functions require a KOS with a rich and precisely defined semantic
structure.
To accomplish these and other more sophisticated tasks, the new KOS
must marry the conceptual structure of full-fledged ontologies - well-structured
hierarchies of concepts connected through a rich network of detailed relations
that support concept retrieval and reasoning -
with the terminological richness of good thesauri. While existing
KOSs do not provide the full set of precise concept relations needed for
reasoning, existing KOSs, both large and small, represent much intellectual
capital. This paper explores the question of how this intellectual capital
can be put to use in constructing full-fledged KOSs.
1.2 The relationship of traditional KOS to ontologies
Reengineering thesauri, classification schemes, etc., into ontologies means
building on the information contained in them and refining that information
as needed. Consider the relationships given in the
ERIC
Thesaurus (ERIC = Educational Resources Information Center) with those
given in a hypothetical ontology as shown in Table 1.
Table 1: Statements and rules of a hypothetical ontology versus
the information given in the ERIC thesaurus (broader term (BT), related
term (RT))
|
Eric Thesaurus
|
Hypothetical ontology
|
|
reading instruction
BT instruction
RT reading
RT learning standards
reading ability
BT ability
RT reading
RT perception |
Statements:
reading instruction
<isa>
instruction
<hasDomain>
reading
< governedBy>
learning standards
reading
ability
<isa>
ability
<hasDomain>
reading
<supportedBy>
perception |
|
|
Rule 1
Instruction
in a domain should consider ability in that domain:
X
shouldConsider
Y
IF X
<isa
(type of)>
instruction AND X <hasDomain>
W
AND Y <isa>
ability AND Y <hasDomain>
W
yields:
: The designer of reading instruction should also consider
reading
ability.
Rule
2
X shouldConsider
Z
IF X
<shouldConsider>
Y
AND Y <supportedBy>
Z
yields:
The designer of reading instruction should also consider
perception. |
The inferences given rely on the detailed semantic relationships given
in the ontology. But the ERIC thesaurus gives only some poorly defined
broader term (BT) and related term (RT) relationships. These relationships
are not differentiated enough to support inference.
For another example, consider the hypothetical ontological relationships
and rules we could formulate with these relationships in an example taken
from the AGROVOC thesaurus (described in detail in section
2) in Table 2.
Table 2: AGROVOC relationships compared with more differentiated
relationships of a hypothetical ontology (narrower term (NT), broader term
(BT))
|
AGROVOC
|
Hypothetical Ontology
|
|
Undifferentiated hierarchical relationships in AGROVOC
milk
NT cow milk
NT milk fat
cow
NT cow milk
Cheddar cheese
BT cow milk |
Differentiated relationships in an ontology
milk
<includesSpecific>
cow milk
<containsSubstance>
milk fat
cow
<hasComponent>
cow milk*
Cheddar cheese
<<madeFrom>
cow milk |
|
|
Rule 1
Part X <mayContainSubstance>
Substance Y
IF
Animal W <hasComponent>
Part X
AND Animal
W <ingests>
Substance Y
Rule 2
Food Z <containsSubstance>
Substance
Y
IF Food
Z <madeFrom>
Part X
AND
Part X <containsSubstance>
Substance Y |
In the context of food and nutrition it makes eminent sense
to consider milk and egg as parts of an animal since their nutritional
value and safety depend on the nature of the animal and the feed it ingests
just as do skeletal meat and organ meat. This is an example of careful
definition of relationships.
From the statements and rules given in the ontology, a system could
infer that Cheddar cheese <containsSubstance>milk fat
and, if cows on a given farm are fed mercury-contaminated feed, that Cheddar
cheese made from milk from these cows <mayContainSubstance>mercury.
But the present AGROVOC Thesaurus (described in detail in section
2) gives only narrower term/broadr term (NT/BT) relationships without
differentiation.
The limitations of existing KOS can be summarized as follows:
-
Lack of conceptual abstraction: thesauri and other traditional KOSs
are collections of terms (generic or domain-specific), ordered in a polyhierarchic
lattice structure or a monohierarchic tree structure and interlinked with
some very broad and basic relationships. The distinction between a concept
(meaning) and its lexicalizations (words) is not made consistently, if
at all, in such a system, and as such it does not reflect the ways humans
understand the world in terms of meaning and language.
-
Limited semantic coverage: most thesauri do not differentiate concepts
into types (such as living organism, substance, or process)
and have a very limited set of relationships between concepts, distinguishing
only between hierarchical relationships, i.e. NT/BT, and associative relationships,
i.e. RT. These very rudimentary relationships are not powerful enough to
guide a user in meaningful information discovery on the Web or to support
inference. They do not reflect the conceptual relationships that people
know and that can be used by a system to suggest concepts for expanding
the query or making it more specific. Examples:
-
The relation between cow and its part cow milk is expressed
as NT rather than the more semantically expressive relation <hasComponent>,
so a user who wants to expand the query hierarchically (search for all
concepts narrower than cow as well) could not distinguish between
searching for all cow parts or searching for all varieties of cow;
-
the relation between mad cow disease and the animal it afflicts,
cow,
is expressed using RT instead of the more semantically precise relation
<afflicts>, so the user could not easily assemble a list of all
cow diseases and search for recent occurrences;
-
mad cow disease and one of its symptoms anorexia would also
be related using RT rather than the more semantically expressive relation
<hasSymptom>.
The concept relations provided by most thesauri force all relations into
the two broad categories, hierarchical and associative. Too often the semantic
relationships captured in this way are ambiguous and poorly defined. The
generalization/specialization relations defined in most thesauri are not
adequately developed to be of use for semantic description and discovery
of Web resources. Thus there is a need for a richer and more powerful set
of relationships.
-
Lack of consistency: since the relationships in thesauri lack precise
semantics, they are applied inconsistently, both creating ambiguity in
the interpretation of the relationships and resulting in an overall internal
semantic structure that is irregular and unpredictable. Many of the NT/BT
hierarchical relationships could, for example, be resolved to the non-hierarchical
RT relationship, and vice versa.
-
Limited automated processing: traditionally thesauri were designed
for indexing and query formulation by people and not for automated processing.
The ambiguous semantics that characterizes many thesauri makes them unsuitable
for automated processing.
To overcome these limitations and enable more powerful searching and intelligent
information processing, especially as such capabilities can be made more
widely available through the Web, traditional KOSs must be reengineered
into KOSs that contain domain concepts linked through a rich network of
well-defined relationships and a rich set of terms identifying these concepts.
A concept can be represented by many different terms (words or phrases)
in multiple languages. This paper refers to terms as
lexicalizations
of a concept. One term can identify several concepts (homonymy) and one
concept can have multiple synonymous terms. A concept is conveyed by all
its lexicalizations, the domain it occurs in, and by its
relationships
to other concepts. In addition, valid rules and constraints need to be
specified to provide additional generalizations over sets of related concepts
and to support inference. These systems must also be converted to machine-processable
formats based on Web technologies like XML which tag the vocabularies in
a standardized way.
In contrast to traditional KOSs, ontologies provide conceptual abstraction
and differentiated relationships. Ontologies specifically separate concepts
from lexicalizations and thereby better reflect the structure of human
understanding of a domain. In ontologies, the semantics are developed through
ensuring that each concept within the domain is uniquely and precisely
defined and by specifying elaborated relationships among the concepts.
The relationships in an ontology are explicitly
named and developed with specification of rules and constraints so that
they reflect the context of the domain for which the knowledge is modeled.
Given their more precise and unambiguous semantics, ontologies allow
further knowledge to be inferred from the knowledge explicitly represented
in the ontology. The new (implicit) knowledge could be derived by applying
generalization or transitivity rules, the level of applicability of which
is limited in a poorly defined KOS like a traditional thesaurus. This added
knowledge in the ontology makes it powerful when employed for intelligent
information processing. Although there is a huge cost involved in moving
from thesauri to ontologies, there is an expectation that the added power
of consistency, precision, and completeness will be worth the investment
even though reliable numbers on the return on investment (ROI) of ontology
development are hard to come by.
1.3 Potential benefits of future generation KOSs
For emerging KOSs to satisfy user needs, they must improve both information
organization and retrieval in a way that was not possible with traditional
KOSs. The following potential benefits are expected from such systems:
-
Unique identifiers and formal semantics: the explicit definition
of concepts and relations in an ontology
allows a unique identifier to be assigned to each concept. As each concept
and relation is explicitly defined as a unique entity, the ontology lends
itself to semantic formalization.
-
Internal consistency: another benefit of explicit semantics is the
achievement of internal structural consistency in the expression of knowledge
due to the possibility of applying integrity constraints.
-
Interoperability: clear semantics enables interoperability among
different KOSs since corresponding concepts within different KOSs would
have the same unique identifier, irrespective of the actual lexicalizations
used to express those concepts. Semantic interoperability promotes sharing
and reuse of knowledge.
-
Greater information integration: interoperability among different
KOSs makes it possible for machines to recognize and analyze intended meaning
of terms from disparate vocabularies. This is possible by using structured
meta-information and formal knowledge description such as agreed-upon metadata
schemas, controlled domain vocabularies, and taxonomies. The ability to
integrate terminologies from different sources maximizes the value of investment
made in the ontology.
-
Inferencing capability: new KOSs have the potential for expressing
knowledge beyond what is present in the structure of the system. Unlike
traditional KOSs where both concepts and relations are underspecified and
very few, if any, axiomatic rules exist, the facts (concepts and relations)
and rules that can be derived from an ontology
have the expressive capabilities that allow for reasoning.
-
Automated information processing: new KOSs create improved potential
to discover relevant information from different sources by exploring patterns
and filtering information using conceptual connections represented in the
ontology. This enables question-answering from one or more databases or,
using natural language processing (see next bullet), from text.
-
Natural language processing (NLP) support: offers the possibility
of providing a direct reply to a search question that is expressed in natural
language, using the enhanced relationships and semantics in an ontology,
instead of only returning a list of relevant documents.
-
Search query understanding: using NLP and semantic processing, a
system can understand a query posed in natural language, determine the
concepts involved and, where useful, create a Boolean query.
-
Concept-based search: an ontology
can provide context-aware search capabilities specific to the area of interest.
-
Integrated information search/browse support: text mining on the
Web (Web mining) through meaning-oriented access, dynamic organization
of information with the possibility for cross-domain links are feasible
with emerging KOSs.
-
Search query expansion: the enhancement, extension, and disambiguation
of user query terms become possible with the addition of enriched domain-
and context-specific information.
To be an effective tool to facilitate information categorization, integration
and retrieval, ontologies should be multilingual, domain-specific, and
cross disciplinary at the same time. For maximum application potential
they should be developed in a non-proprietary, application-independent,
and machine-processable format to ensure interoperability among different
systems.
1.4 The process of reengineering: the rules-as-you-go
approach
Reengineering a thesaurus into
an ontology
entails refining thesaurus relationships, a laborious process. The steps
in the process are:
-
Define the ontology structure
-
Fill in values from one or more legacy KOS to the extent possible
-
Edit manually using an ontology editor:
-
make existing information more precise
-
add new information
Step 1 is addressed in
section 3, which gives an overall
conceptual model at a high level of abstraction, and
section
4, which begins the process of defining a set of relationship types
for the food and agriculture domain by examining relationships in AGROVOC
as to their relationship types.
Step 3 is the most laborious. We have plans to streamline this process
by implementing intelligent conversion using a "rules as you go"
approach. The idea is as follows: The KOS editor watches out for patterns;
based on these patterns the editor formulates rules that can be applied
immediately to all subsequent similar cases as illustrated in the following:
-
An editor has determined that
cow NT cow milk should become cow <hasComponent>
cow
milk
-
She recognizes that this is an example of the general pattern
animal <hasComponent> milk (or, even more general
animal
<hasComponent>
body part)
-
Given this pattern, the system can derive automatically
goat NT goat milk should become goat <hasComponent>
goat
milk
since goat is an animal and goat's milk ends with the word milk and
thus can be seen to be a type of milk.
To automate this approach even more, we plan to build an inventory of patterns
such as
animal <
hasComponent>
body part, augmented
by an ontology that specifies the concepts of type
animal (
cow,
goat, sheep, horse, chicken, etc.) and the concepts of type
body
part (
skeletal meat part, liver, bone, milk, egg, etc.). This
information would be drawn from AGROVOC itself and other sources, such
as
Langual,
UMLS,
and even
WordNet. The
system can then detect the applicability of these
patterns,
at least once it saw one example transformed by an ontology editor. The
ontology editors will add to the pattern inventory incrementally.
These patterns are a special type of constraint. Other constraints can
be formulated and used to limit the options presented to the human editor
as thesaurus relationships are refined. The bases for such constraints
are the thesaurus relationships, on the one hand, and the entity types
of the concepts involved, on the other. Table 3 shows some examples of
constraints based on thesaurus relationships.
Table 3: Some relationship constraints
|
Thesaurus Relationships
|
Possible ontology relationships
|
|
NT/BT
|
<hasMember> / <memberOf>
<includesSpecific> / <isa>
<hasComponent> / <componentOf>
<spatiallyIncludes> / <spatiallyIncludedIn>
etc. |
|
RT
|
<similarTo>
<growsIn> / <EnvironmentForGrowing>
<treatmentFor> / <treatedWith>?
<hasMember> / <memberOf>
etc.
Note
that the RT relationship often transforms into relationships that are not
symmetric.
Note further that in a well constructed thesaurus, an RT should not
resolve into an <isa>
relationship. However, reality shows that the RT relationship has been
applied to express this relationship. This can be taken as another proof for
the weak definition of relationships in many thesauri. |
This inventory will constrain the available choices when manually refining
a thesaurus relationship to a more specific ontology relationship. Of course,
an authorized ontology editor can override such constraints and thereby
update the relationship table. As a relationship has been added or refined
the inverse relationship is automatically added or refined.
2 AGROVOC: a multilingual agricultural thesaurus
This section describes the AGROVOC Thesaurus and further illustrates the
problem of semantic under-specification.
2.1 Background
AGROVOC is a multilingual, structured, controlled vocabulary/thesaurus
designed to cover concepts and terminology in agriculture, forestry, fisheries,
food and related domains (e.g. environment). It was developed by the Food
and Agriculture Organization (
FAO) of
the United Nations and the Commission of the European Communities in the
early 1980s to describe documents and other information resources in a
controlled language for indexing and searching. It contains approximately
16,500 descriptors and 10,000 non-descriptors.
AGROVOC is available online
and for download in the five FAO official languages (English, French, Spanish,
Chinese and Arabic). It is translated into other national languages such
as Czech, Danish, German, Italian, Polish, Portuguese, Slovak and Thai.
2.2 Applications and related terminologies
AGROVOC is used for controlled-vocabulary indexing and searching globally
and in various systems throughout the FAO. Systems where AGROVOC is used
include:
-
the AGRIS/CARIS network, an international
information system for indexing and retrieval since 1986, coordinated by
FAO;
-
domain-specific documentation centers around the world;
-
indexers, librarians and translators working in
the global food and agriculture sector.
Within FAO:
AGROVOC coexists as one knowledge organization system next to numerous
others in the agricultural domain. Among the most important ones at the
FAO are:
There are a number of other thesauri in the food and agricultural sector,
developed by other institutions, such as
These thesauri basically all follow the same conceptual structure, which
will be discussed in the following section. Nevertheless, we will see that,
although all these thesauri use basically the same conceptual model, the
information contained in them can differ substantially.
2.3 Conceptual structure of AGROVOC
AGROVOC follows a traditional thesaurus approach. It is a collection of
terms, definitions, and term relationships. As is the case with most thesauri,
a small, standard, non-adaptable set of relationship types is applied to
interlink terms.
2.3.1 Equivalence relationships
USE: Since thesauri have been primarily developed for the purpose
of indexing and retrieval, this relationship indicates that any term preceding
the USE relation should be replaced, for the purposes of indexing documents
and formulating queries, by the term following the USE relation. The relationship
usually (but not always) expresses synonymy between two terms.
USED FOR (UF): This is the inverse of USE and indicates that
term A is USED FOR term B for indexing purposes.
2.3.2 Hierarchical relationships
Narrower Term (NT): if X is a NT of Y, then X is narrower in some
sense than Y. For example,
milk NT
cow milk, grain NT
rice.
Broader Term (BT): if Y is a BT of X, then X is broader than
Y; for example cow milk BT milk, rice BT grain.
BT is the inverse of NT.
Given these rather unspecific definitions, BT and NT relationships can
be applied to express generic relations, meronymic relations, instantiations,
and many others (see section 4).
2.3.3 Associative relationships
Related Term (RT): the thesaurus conceptual model contains the RT
relationship to express any kind of associative relationship between two
terms that is not a hierarchical relationship. This relationship is hence
very ambiguous in that it is the default for all other relationships.
Hierarchical (NT, BT) and associative (RT) relationships are relationships
between concepts. In the thesaurus, these exist only between descriptors.
Following a traditional thesaurus approach, AGROVOC distinguishes between
descriptors
and non-descriptors (often referred to imprecisely as preferred
terms and non-preferred terms). The rationale behind this is that only
a descriptor should be used when referring to the concept (for example,
for indexing and retrieval); each descriptor uniquely and unambiguously
designates a concept. A non-descriptor must not be used for indexing or
retrieval; it is linked through a USE cross-reference to the corresponding
descriptor that must be used instead. There are no relationships from one
non-descriptor to another.
2.3.4 Scope notes
Many descriptors in AGROVOC have a scope note, which can be a definition
of a term, a history note, instructions to the indexer or searcher, or
simply a comment. The purpose is to provide the user with more detail about
the term and its usage.
2.3.5 Top level structure
Currently AGROVOC has more than 1500 top-level terms, i.e. descriptors
which do not have a broader term, making it cumbersome to access the thesaurus
from a top-level approach and browse through the hierarchy. Superimposed
on AGROVOC is the
AGRIS
categorization scheme;
it has more than
100 top-level categories, ordered in a shallow two-level hierarchy. AGROVOC
descriptors are mapped to the second level of AGRIS categories. For example,
the AGROVOC descriptor
fish farms is
mapped to the AGRIS category
aquaculture production which is a subcategory
of
fishery and aquaculture. Thus the AGRIS categorization scheme
provides high-level organization for information that has been tagged with
AGROVOC descriptors.
2.4 Semantic problems of AGROVOC
Given its minimalist conceptual structure, AGROVOC (as other thesauri)
has a number of
semantic flaws. In the following we will use examples
to point out the major drawbacks of the current system and develop the
rationale for the shift towards a more powerful, expressive, and unambiguous
conceptual model.
2.4.1 Ambiguous descriptor to non-descriptor relationship
In AGROVOC, as indicated, USE/UF covers synonyms and formal variants. In
addition, the relation also links quasi-synonyms and very specific narrower
terms, which the AGROVOC defines as any of the following:
-
"two concepts considered sufficiently alike to
be identified by one descriptor"
-
"a concept and its opposite"
-
"more specific concepts encompassed by one descriptor"
Definition 1 deals with semantically very
closely related, yet separate
concepts (so that the terms designating such concepts would not be true
synonyms), such as
famine
UF hunger
Definition 2 involves concepts on opposite ends of a scale or
otherwise in opposition to each other. (With a few exceptions, the terms
designating such concepts are antonyms). Example:
hydrophilicity
UF hydrophobicity
Definition 3 indicates that USE/UF can also express a hierarchical
relationship, for example:
biological competition
UF interspecific competition
UF intraspecific competition
where the fine distinction between interspecific
competition and intraspecific competition is deemed unnecessary
for retrieval and therefore abandoned in favor of the more general category.
2.4.2 Ambiguous hierarchical definitions
The BT/NT relationship used to build up the hierarchy is very ambiguous;
it lumps together several different types of relationships as the following
examples show:
2.4.2.1
<includesSpecific> relationship (erythrocytes are a specific
kind of blood cell):
blood cells
NT erythrocytes
NT leukocytes
2.4.2.2
<hasComponent> relationship (blood contains as a component
blood
cells):
blood
NT blood cells
2.4.2.3 The following example shows clearly the discrepancies
between different thesauri that apply the ambiguously defined modeling
principles:
AGROVOC and CABI:
water
NT ice
NT water vapor
...
NT fresh water
NT drinking water
But ASFA:
water
RT ice
RT water vapor
...
NT fresh water
Water vapor and ice are phases of water while fresh
water and drinking water are kinds of water, so in AGROVOC
and CABI hierarchical relationships lump together several different semantic
relationships. For retrieval this is generally useful (a search for water
should generally find documents on ice as well), but for more differentiated
retrieval a user may want to ask for water in all phases or for
all
kinds of water. There are many other purposes of semantic processing
that need more differentiated relationships. In ASFA the phase relationship
is treated as a RT, an example of how grouping relationships may lead to
inconsistency. Note, by the way, that neither thesaurus includes the concept
liquid
water, which is logically necessary if water means
water
in any phase.
There are many more examples in AGROVOC where the currently used BT/NT
relationship is used to describe different relationships. The most obvious
ones have been identified and are used in our proposal below in section
4.
2.4.3 Ambiguous associative relationships
Like the BT/NT relationships, the associative RT relationships can be refined
into more specific relationships. Some examples are given below.
2.4.3.1
<hasMember>
relationship
(Anglophone Africa <hasMember> Botswana)
Anglophone Africa
RT Botswana
RT Gambia
RT Ghana
RT Kenya
RT Lesotho
...
2.4.3.2
<causes>
relationship (bleaching <causes>
discoloration):
bleaching
RT discoloration
2.5 The need for reengineering AGROVOC into an ontology
The examples above indicate clearly the ambiguous nature of the relationships
in AGROVOC. With respect to future information retrieval and intelligent
processing needs, where it will be necessary to combine different KOS in
order to give access to different information systems, it becomes evident
that a more rigid structure is required. A reassessment of AGROVOC (as
well as other thesauri) to transform its UF, NT, BT, and RT relationships
into unambiguously defined relationships and hierarchical order will provide
the first step towards solving the problem of ambiguity and inconsistency
in information description and retrieval.
3 Conceptual model: combining thesauri and ontologies
This section introduces a conceptual model that provides the necessary
structure to create precise semantics to facilitate the transition from
traditional thesauri to ontologies. Figure 1 shows the high level conceptual
model we propose. Its chief characteristic is a clear separation of the
concept level, the term or lexicalization level, and the string level.
Present thesauri give a more or less muddled representation of information
about concepts and information about terms. The proposed structure allows
for a clear separation of concept information and term information. This
model owes much to the structure of the
UMLS.
Figure 1. Conceptual model for combining thesauri and ontologies
3.1 The basic model
The following is just the broad outline of the model. Many more types of
information could be added. In any event, we consider the model extensible.
On the other hand, not all applications will use all features of the model.
For example, our model provides for relationships between notes (for example,
as hypertext links). This is not possible in all environments but very
useful in some. Our intent is to present a framework that can be used for
the simplest thesaurus or the most complex and rich ontology in a format
that communicates equally to thesaurus and ontology editors with a background
in information
science, artificial intelligence,
or linguistics.
-
A
concept encapsulates meaning.
-
A concept can be represented or designated by one or more linguistic expressions,
namely terms or lexicalizations which can be single words or multi-word
phrases (or composite words in agglutinative languages).
-
A term, in turn, can take variant forms (singular/plural, variations in
case, spelling variants, abbreviations, acronyms);
so just as a concept can have many lexical representations, a term can
have many string manifestations.
Each concept, term, and string can be assigned an identifier, preferably
a Unique Resource Identifier (URI)
; for concepts,
UMLS uses Concept Unique Identifiers (CUI), while the Topic Map Standard
uses unique subject identifiers. Using unique concept identifiers allows
for unambiguous reference to concepts, as opposed to often ambiguous terms.
Concepts can furthermore be assigned notations (such as class numbers in
the Dewey
Decimal Classification; notations
are also called term numbers); notations can be used to maintain a logical,
meaningful sequence in hierarchical displays.
Concepts take center stage in our proposed
thesaurus/ontology information model; accordingly, relationships between
concepts are central. Concepts are arranged in hierarchies and have
additional
relationships to other concepts in the network; a hierarchy can be
defined on any weak ordering relationship including isa, part-whole, spatial
containment, etc. (the relationship must be transitive and not
symmetric, but must have an existing inverse relationship, for example
<componenttOf>
is the inverse relationship of <hasComponent>).
There are many other relationship types, such as <causes>
; a scheme of relationship types needs to be defined
for the domain of the respective thesaurus. One source for finding relationship
types is the detailed analysis of concept relationships present in the
thesaurus that is to be reengineered into a richer ontology (see section
4). Each concept should be assigned to an entity type or facet, such
as process, function, substance, living
organism (see, for example, the semantic types in the UMLS
Semantic Network); the type of a concept constrains its participation
in relationships.
A concept is designated or represented by one
or more lexicalizations or terms in one or more languages;
this is the linkage between the concept level and the term level. For examples
see Table 4.
Table 4. Concepts, terms, strings
(concept and term numbers are fictitious and used only for illustration)
|
Concept ID
|
Term ID
|
Strings manifesting the term
|
| AGROVOC:C316301 |
AGROVOC: T657210 |
bovine spongiform encephalopathy,
BSE |
|
"
|
AGROVOC: T657211 |
mad cow disease, Mad Cow Disease,
MCD |
|
"
|
AGROVOC: T734567 |
encephalopathy spongiforme bovine,
ESB |
|
"
|
AGROVOC: T734566 |
maladie de la vache
folle, MVF |
|
"
|
AGROVOC: T700345 |
encefalopatia espongiforma bovina,
EEB |
|
"
|
AGROVOC: T700346 |
enfermedad de la vaca loca, EVL |
| AGROVOC:C014593 |
AGROVOC: T187953 |
plow, plows, plough, ploughs,
plogh [a frequent misspelling] |
|
"
|
AGROVOC: T498001 |
charrue |
|
"
|
AGROVOC: T498002 |
materiel de labour |
If a term is a homonym (designates more than one concept), several disambiguated
terms are introduced. The homonym is linked to each of the disambiguated
terms, and each disambiguated term is linked to the corresponding concept.
Two terms designating the same concept are called synonyms. Conversely,
if one does not agree that concepts per se exist, one can simply view "concept"
as a convenient shorthand for an equivalence class of terms that are linked
by the <hasSynonym>
relationship, such as the synsets in Wordnet.
A KOS may select a preferred term as the term used to represent the concept
or it may make that choice dependent on the audience (for example, veterinarians
versus farmers).
Terms can be connected through many relationships such as <hasSynonym>
(with <hasScientificName>
as a special case), <hasAntonym>,
<hasCognate>
(term in a different language from the same root), and <hasTranslation>.
One might think that the synonym
and translation
relationships are not needed since all terms linked to the same concept
would be synonyms or translations. However, two terms may be linked to
the same concept yet be used in different contexts, i.e. they are not strict
synonyms. If a concept has linked to it several English terms and several
French terms, it is not true that just any of the French terms is a good
translation for a given English term (see the examples in Table 4). Another
example of term-specific relationships is <hasAntonym>.
For example, big and small designate opposite concepts but
are not antonyms. (The antonym pairs are big versus little and
large
versus small; see Wordnet.)
Finally, a term is manifested in one or more
strings,as
shown in Table 4. Strings can be connected through relationships such as
<hasCaseVariant>,
<hasSpellingVariant>,
<hasAbbreviationOrAcronym>,
<pluralOf>
/ <singularOf>,
which are all subordinate of a broader relationship <hasStringVariant>.
A term can be seen as a convenient shorthand
for an equivalence class of strings that are linked by the <hasStringVariant>
relationship. A KOS may select a preferred variant as the string used to
represent the term or it may make that choice dependent on the audience
(as in British versus US
spellings). A string, especially an acronym, may belong to several terms,
in which case it needs to be disambiguated.
In addition, a concept, a lexicalization/term,
a string, or a relationship type can have several types of notes
(definitions, usage notes, comments, image, etc.) in different languages
(in the case of multilingual thesauri). Just like concepts and terms, notes
can be related to each other through relationships such as <hasTranslation>,
<hasSimplifiedVersion>,
<hasOtherDefinition>,
or any other type of hyperlink. Many other pieces of information about
terms can be added, for example, case frames for verbs (in case the verb
has a case frame different from the case frame for the corresponding action
concept) or register (see below) or whether the term is the preferred term
for the concept. Administrative data will be accommodated as well.
Relationship types themselves can form relationship
hierarchy (i.e. a relationship of relationships), in which more generic
relationships are further up in the hierarchy than more specific relationships,
for example, <componentOf'>
is a specific kind of <partOf>
relationship.
Why define concepts, terms, and strings as
separate entity types?
First, each of these
entity types takes different types of information. Conceptual relationships
and other information are associated with concepts. Linguistic information,
such as part of speech and how a term combines with other terms into sentences,
usage, or information on etymology, are associated with terms. Information
such as that a string is an acronym is associated with terms. Usage information
may sometimes be associated with strings; for example, lay people may commonly
use a slang abbreviation while professionals use the full string. Definitions
are primarily associated with concepts but may also be associated with
terms.
Second, this distinction
avoids confusion. In a standard thesaurus like AGROVOC, for each concept
that is to be used in indexing and searching, a preferred term, and for
that term a preferred string, is selected; this string is the descriptor.
Non-descriptors are linked only to descriptors, not among themselves. As
a result, BSE, mad cow disease, and
MCD [which we
made for illustration] are all linked to bovine spongiform encephalitis
as synonyms (or, in some thesauri, as synonym and as abbreviations). But
the information that BSE belongs with bovine spongiform encephalitis
and MCD with mad cow disease is lost. Furthermore, if decisions
on terms are made (for example, omitting mad
cow disease as a non-scientific name), these decisions should apply
to all term variants, in the example MCD as well.
3.2 Model extensions
As was mentioned above, many more types of information could be added to
concepts, terms, strings, notes, and relationships. For example, we might
specify an audience (general lay public, K-12 students by grade level,
university students, experts), a subject domain, a scope (as in Topic Maps),
or a specially selected subset of concepts and terms to be used for a given
application, or all concepts and terms taken from a given source.
Scopes could be defined in many ways. For example, one might
define a scope as the conceptual system embedded and expressed in a language
(whereas the link from terms and notes to language simply refers to the
surface form). Consider the conceptual system underlying Walpiri (an Australian
indigenous language); one of its noun classifiers includes women,
fire,
and dangerous things (Lakoff 1987). A native speaker of English would
find this classifier and the corresponding <isa>
relationships very curious. Thus one would introduce the category and the
<isa>
relationships with a scope of the Walpiri conceptual system. (By the way,
the relationship between these relationships makes sense in the context:
fire is dangerous; fire is sometimes started by or anyway related to the
sun; the gender of the noun for sun is female). Many such problems, if
more subtle, occur in thesauri for international use.
A subvocabulary can be extracted using any type information about
concepts, terms, strings, and relationships that is available in the thesaurus.
Thus one could extract as subvocabulary
-
a subset that was selected for a given application;
-
all strings that are acronyms (for an acronym reference);
-
all scientific names;
-
all entries for taxonomic entities for the entire range of living things
or for a given large taxon such as insects;
-
terms suitable for a given audience.
Each of these subvocabularies provides a specific view on the entire KOS
for a given purpose. In online implementations such subvocabularies can
be created on the fly or defined as views for certain user groups. But
subvocabularies can also be printed or exported (for example, a subvocabulary
extracted as the personal KOS of a researcher who maintains an information
retrieval system on his or her own computer).
3.3 Limitations
The separation into the concept layer and the term layer is appealing for
its simplicity and elegance but it is somewhat of an oversimplification.
Terms, particularly terms in different languages, rarely mean exactly the
same thing. So the question arises as to when to map two terms to the same
concept - and possibly explain shades of meaning and associations in the
definition of each term that complements the definition of the concept
- and when to create two closely related concepts, possibly under one broader
concept. Our model permits any type of relationship between terms. Thus
it is possible to introduce conceptually motivated relationships between
terms that more accurately reflect the reality of language than the mapping
of terms to "concepts". These two representations of conceptual information
can coexist within the same system.
3.4 Implementation
All relationships from all layers (concept, term, string) can be stored
in the same format within a database. The type of each element should be
explicitly given to enable integrity constraints (so that the relationship
<
hasSpellingVariant>
is not allowed between two concepts, for example). A concept can be identified
by a URI or other number (cleanest solution) or by its preferred term in
the base language of the thesaurus (the term being typed as preferred).
Likewise, a term can be identified by a URI or other number (cleanest solution)
or by the preferred string (the term being typed as preferred). The same
holds for strings. The main difference with implementations in most existing
thesaurus management software is that relationships between non-descriptors
are allowed. Thoughts for an XML/RDF schema for KOS data are presented
in the
Appendix.
3.5 Related approaches
The proposed conceptual model integrates well with standardization approaches
regarding Web technologies like RDFS. The proposed structure shows all
aspects of a proposed RDFS-compatible Thesaurus Interchange Format by
Matthews
et
al. (2002), which will appear as a W3C note. The proposal is being
done in the context of the
SWAD-Europe
project. The
Appendix presents another approach
for representing ontology and thesaurus data in XML/RDFS.
4 The AGROVOC case: exploring conceptual relationships
in the agricultural domain
The model we introduced has no restrictions on potential relationships
to be applied. The model is extensible, and any possible specific relationships
can be included. We carried out a preliminary linguistic and conceptual
analysis of AGROVOC and found a set of relationships; most of them are
well known (but it is important to know that they are needed in the food
and agriculture domain), some of them add new nuances. Table 5 lists relationship
types found in AGROVOC or otherwise proposed here, and subsections 4.1
- 4.3 give an explanation and examples for some of these relationship types;
others appear in examples throughout the paper. This section is not in
any way intended as a complete list of relationship types; it merely gives
examples to illustrate the additional information and clarity of conceptual
structure that can be conveyed through more specific relationships. Much
more work, including comparison, is needed to converge on a set of relationships
to replace the currently used thesaurus relationships BT, NT, RT, USE and
UF in a reengineering of AGROVOC.
Table 5: Concept relationships:
Examples
| X, Y are concepts
Isa
X <includesSpecific>
Y / Y <isa> X
X <inheritsTo>
Y / Y <inheritsFrom>
X
Holonymy / meronymy (the generic whole-part relationship)
X <containsSubstance>
Y / Y <substanceContainedIn>
X
X <hasIngredient>
Y / Y <ingredientOf>
X
X <madeFrom>
Y / Y <usedToMake> X
X <yieldsPortion>
Y / Y <portionOf>
X
X <spatiallyIncludes>
Y / Y <spatiallyIncludedIn>
X
X <hasComponent>
Y / Y <componentOf>
X
X <includesSubprocess>
Y / Y <subprocessOf>
X
X <hasMember>
Y / Y <memberOf>
X
Further relationship examples (some from (Schmitz-Esser
1999)
X <causes>
Y / Y <causedBy>
X
X <instrumentFor>
Y / Y <performedByInstrument>
X
X <processFor>
Y / Y <usesProcess>
X
X <beneficialFor>
Y / Y <benefitsFrom>
X
X <treatmentFor>
Y / Y <treatedWith>
X
X <harmfulFor>
Y / Y <harmedBy>
X
X <hasPest>
Y / Y <afflicts>
X
X <growsIn>
Y / Y <growthEnvironmentFor>
X
X <hasProperty>
Y / Y <propertyOf>
X
X <hasSymptom>
Y / Y <indicates>
X
X <similarTo>
Y / Y <similarTo>
X
X <oppositeTo>
Y / Y <oppositeTo>
X
X <hasPhase>
Y / Y <phaseOf>
X
X <growsIn>
Y / Y <EnvironmentForGrowing>
X
X <ingests>
Y / Y <ingestedBy>
Y
|
4.1 The logical generic relationship
4.1.1
X <includesSpecific>
Y / Y <isa>
X (implies X <
inheritsTo>
Y / Y <
inheritsFrom>
X)
This is the standard generic relationship between
concepts. It can be used for hierarchical inheritance (but is not the only
relationship for this purpose). Hierarchical inheritance is useful within
the thesaurus/ontology to streamline the writing and presentation of definitions
and scope notes and for inheriting relationships. Examples (all NT in AGROVOC
unless stated otherwise):
chemical soil types <includesSpecific>
saline soils
bovine spongiform encephalopathy <includesSpecific>
spongiform
encephalopathy
cells <includesSpecific>
blood
cells
blood cells <includesSpecific>
leukocytes
leukocytes <includesSpecific>
lymphocytes
lymphocytes <includesSpecific>
T-lymphocytes
UF in AGROVOC
4.2 The part-whole family of relationships
There are several relationships that fall under the part-whole umbrella.
Some of these can be displayed in a hierarchical format. However, the direction
of the hierarchy, and the direction of hierarchical inheritance, is sometimes
from part to whole and sometimes from whole to part. All relationships
are shown starting from the whole first.
4.2.1 X <containsSubstance>
Y / Y <substanceContainedIn>
X and X <hasIngredient>
Y / Y <ingredientOf>
X
Y is the material or substance of which X is made by nature (<containsSubstance>)
or by man (<hasIngredient>).
Y loses its identity once it is incorporated into X. There is no implication
of an "all and only" relation with respect to the composing substance,
where Y is the sole substance making up X; but a subsequent refinement
could make this distinction. In AGROVOC, this relationship appears as BT/NT
or RT; quite often, it does not appear at all. Some examples are given
below:
blood <containsSubstance>
blood
gases
blood <containsSubstance>
blood
lipids
blood <containsSubstance>
blood
proteins
blood <containsSubstance>
blood
cells (borderline case, see comment in 4.2.4)
All NT in AGROVOC
cocoa beverages <hasIngredient>
cocoa
powder
RT in AGROVOC
4.2.2 X <yieldsPortion>
Y / Y <portionOf>
X
X <yieldsPortion>
Y describes a relation between a mass X and a piece Y taken from the mass,
for example,
roots <yieldsPortion>
cuttings RT in AGROVOC
(Note: the example assumes root cuttings, but AGROVOC refers
to any kind of cutting.
4.2.3 X <spatiallyIncludes>
Y / Y <spatiallyIncludedIn>
X
This relation is used for objects with spatial extent. It holds when X
is an inalienable part of Y, identifiable but not inherently separable.
These include body parts and geographical locations.
Asia <spatiallyIncludes>
East
Asia NT in AGROVOC
Transitivity is also a feature of spatial relations. If X
<spatiallyIncludes>
Y
and Y <spatiallyIncludes>
Z,
then X <spatiallyIncludes>
Z.
Asia <spatiallyIncludes>
East
Asia
East Asia <spatiallyIncludes> China
→
Asia <spatiallyIncludes> China
4.2.4 Y <hasComponent> Y / Y <componentOf> X
This relationship holds when X is a part of Y that retains its identity
as an object even when built into the whole. In addition, each X must be
enumerable or nameable, i.e. not part of a mass. Examples:
plough <hasComponent>
ploughshare
RT in AGROVOC
woody plant <hasComponent>
plant
anatomy (i.e. parts of plants) RT in AGROVOC
nucleus <hasComponent>
chromosome
NT in AGROVOC
There are cases when it is hard to decide when to use <containsSubstance>
and when to use <hasComponent>; blood
<?> blood cell is such a borderline case. We decided on blood
<hasComponent>
blood
cell because each blood cell is a recognizable and separate entity.
But one could equally strongly argue for blood
<containsSubstance>
blood
cell because a blood cell is part of a mass and cannot be distinguished
from others within the mass; it has at most a very weak identity as an
object.
4.2.5 X <includesSubprocess>
Y / Y <subprocessOf>
X
There are many processes in AGROVOC which could be linked using this relation,
for example,
ATP cycle <includesSubprocess>
phosporylation RT in AGROVOC
4.2.6 X <hasMember>
Y / Y <memberOf>
X
X <hasMember>
Y indicates a relation of membership within a collective or group
or organization.
Francophone Africa <hasMember>
Benin RT in AGROVOC
biotope <hasMember>
plant not in AGROVOC
pesticide crops <hasMember>
Artemisia absynthium RT in AGROVOC
Note that transitivity is not a characteristic of membership relations.
4.3
Further relationship examples (some from
Schmitz-Esser 1999)
4.3.1 X <causes>
Y / Y <causedBy>
X
Examples
overgrazing <causes>
desertification RT in AGROVOC
Serpulina hyodysenteriae <causes>
swine dysentery
preharvest sprouting <causes>
crop losses
4.3.2 X <instrumentFor>
Y / Y <performedByInstrument>
X
This relation expresses the fact that concept X is instrumental to achieve,
as a result, concept Y. Example:
plough <instrumentFor>
ploughing RT in AGROVOC
The instrument considered may be one applied by a living being (man,
animal) or a machine or a system. The sense of the relation points to the
result achieved by the use of the instrument.
4.3.3 X <processFor>
Y / Y <usesProcess>
X
This is a case where X indicates a process involved in Y. Examples:
soil injection <processFor>
fertilization RT in AGROVOC
gonadectomy <processFor>
sterilization [of organisms] BT in AGROVOC
4.3.4 X <beneficialFor>
Y / Y <benefitsFrom>
X
fertilization <beneficialFor>
crop
yield Not found in AGROVOC
4.3.5 X <treatmentFor>
Y / Y <treatedWith>
X
Pentosan polysulphate <treatmentFor>
bovine
spongiform encephalopathy Not in AGROVOC
4.3.6 X <harmfulFor>
Y / Y <harmedBy>
X
Found in AGROVOC only indirectly. For example, from:
preharvest sprouting <causes>
crop losses
one can conclude
preharvest sprouting <harmfulFor>
crop yield
4.3.7 X <growsIn>
Y / Y <growthEnvironmentFor>
X
Halophytes <growsIn>
saline soils RT in AGROVOC
4.3.8 X <hasProperty>
Y / Y <propertyOf>
X
fertilization <hasProperty>
application rate RT in AGROVOC
blood circulation <hasProperty>
blood pressure NT in AGROVOC
4.3.9 X <similarTo>
Y / Y <similarTo>
X
bovine spongiform encephalopathy <similarTo>
Creutzfeld-Jakob syndrome RT in AGROVOC
4.3.10 X <oppositeTo>
Y / Y <oppositeTo>
X
crop losses <oppositeTo>
crop yield Not in AGROVOC
4.3.11 Concluding comment
This preliminary analysis shows that AGROVOC contains many relationships
that can be made more specific in a reengineering project but that some
relationship types are missing so relationships need to be added from scratch.
Also it is hardly possible to compile a complete inventory of relationship
types. Thus, a generic relationship Related
Term should still be kept to express residual relationships;
such relationship instances should be accompanied by a note that specifies
the meaning or intention of the relationship. This will facilitate a later
deduction of new relationship types from these residual relationships.
5 Exploring the rules-as-you-go approach for the case of
AGROVOC
We explored the applicability of the rules-as-you-go approach to transforming
AGROVOC into an ontology. We looked for examples
of patterns that could be used. The results are shown in Table 6.
Table 6:
Examples for the rules-as-you-go approach
Pattern: plant <growsIn>
soil type
Rice RT moist soil →
rice <growsIn>
moist soil
Pattern: geographical entity <spatiallyIncludedIn>
geographical entity
Benin BT West Africa → Benin
<spatiallyIncludedIn>
West Africa
Pattern: geographical entity <isa>
geographical entity
Benin RT Francophone country → Benin
<isa>
Francophone country
Pattern: body part <containsSubstance>
(substance | small particle)
blood NT blood gas →
blood <
containsSubstance > blood gas
blood NT blood cell →
blood <
containsSubstance > blood cell
|
From this exploration it appears that the approach is promising. On
the other hand, the rules-as-you-go approach is not error-free; results
must be checked by an ontology editor. In some cases, it may not be possible
to define a rule.
Another difficulty is illustrated by the concept of Francophone Africa.
Does this term refer to the set of Francophone African countries, in which
case it refers to a type of geographical entity, or does it refer
to the area (not necessarily contiguous) covered by these countries, in
which case it refers to a geographical entity (just as West Africa).
UF (Used For) may refer to a synonymous or quasi-synonymous term or
to a narrower concept (generally includesSpecific).
Since the terms on both sides of the UF most often refer to concepts that
have the same entity type, it is difficult to formulate a rule. One might
use an external knowledge source, such as a large dictionary, to detect
synonymity and treat the other cases as includesSpecific,
always subject to editor verification.
The rules-as-you-go approach implies that the reengineering effort should
start with the top-most concepts so that entity types and patterns can
be detected early.
6 Implications and further work
On the basis of the ideas presented in this paper, we plan to undertake
the reengineering of AGROVOC into an ontology system that will also serve
the functions of a traditional thesaurus. This involves creating a complete
inventory of domain-relevant entity types and relationship types, which
we will base on further analysis of AGROVOC and related vocabularies and
on existing inventories, such as the UMLS
Semantic Network. We tend towards using a frame representation of relationships,
as presented in Slaughter and Soergel (2003).
Based on the encouraging results of our exploration of the applicability
of the rules-as-you-go approach to AGROVOC, we plan to develop a system
that streamlines the reengineering process. We expect that this system
will drastically reduce the effort required and thus make the reengineering
effort feasible.
The resulting knowledge base will enable
-
improved user interaction with the vocabulary for improved query formulation
and more user learning about the domain;
-
intelligent behind-the-scenes support for query expansion;
-
intelligent support for human indexers and automated indexing/categorization
systems;
-
support for artificial intelligence and semantic
Web applications for agriculture, food processing, and food safety.
The approach will be incremental, starting with providing a proof of concept
through pilot application that demonstrates that the expected benefits
will in fact result. This will provide the basis for planning and efficient
implementation of the large-scale effort to reengineer AGROVOC into a full-fledged
ontology. We hope this effort can be carried out in a cooperative, distributed,
and coordinated environment to promote active participation and ultimately
use of a widely accepted Knowledge Organization System for the domain of
food and agriculture.
Acknowledgements
We particularly thank all the participants of an expert workshop on the
AGROVOC thesaurus and especially all the users and developers for their
valuable feedback provided over the years. Their feedback contributed in
great part to this proposal. We also thank the anonymous reviewers for
their helpful comments.
References
Lakoff, George (1987) Women, fire, and dangerous
things (University of Chicago Press)
Links to RDF and XML thesaurus formats and related topics http://www.w3c.rl.ac.uk/SWAD/thes_links.htm
Matthews, B.M., Miller, K. and Wilson, M.D.
(2002) "A Thesaurus Interchange Format in RDF". Submitted to the Semantic
Web Conference 2002
Schmitz-Esser, W. (1999) "Thesaurus and Beyond:
An Advanced Formula for Linguistic Engineering and Information Retrieval".
Knowledge Organization, Vol. 26, No. 1, 10-22
Schmitz-Esser, W. (1999) "Gedankenraumreisen - neue Thesaurusstrukturen,
multimedial präsentiert, machen Anregung, Spielen, Lernen,
Finden möglich für jedermann". In Proc. DGI-Jahrestagung,
Hamburg (Frankfurt/M.: DGI), pp. 347-353
Slaughter, Laura and Soergel, Dagobert (2003)
"How physicians' answers relate to health consumers' questions". Proc.
66th ASIST Annual Meeting, Long Beach, CA, Oct. 19-22 (Medford, NJ:
Information Today), pp. 28-39
Appendix: Towards an XML/RDF specification for
KOS
Proposals and schemes for encoding KOS data range from general schemes,
such as RDF (with the OWL extensions) and the Topic Map standard to much
simpler and specific schemes for encoding thesaurus data as specified in
ISO 2788, which inherit all the limitations of this standard and are of
equally limited usefulness for the new tasks ahead. Something in between
is needed. After some preliminary remarks on the functions of such a standard,
we make a proposal that is parsimonious but allows for encoding very rich
data. For this reason, it may seem a bit opaque at first. The first author
will be happy to entertain any questions or comments.
Functions to be served by standards for machine-readable KOS
1.
Input
of KOS data into programs / transfer of thesaurus data from one program
into another
1.1 Format for original input files (but XML difficult for
that, use a more user-friendly format, such as inputting a hierarchy with
levels specified by the number of dots at the beginning of a line)
1.2 Transfer from one KOS development program to another
1.3 Transfer from a KOS development program to an information system
that uses a KOS for authority control, query expansion (synonym and /or
hierarchic), display/browse/search, or other purposes
1.4 Transfer from a KOS development program to a KOS display / browse
/ search program
2. Querying
KOSs and viewing results (for example, using Z39.50)
2.1 By people
2.2 By systems to use data from external KOSs for query term expansion,
etc.
3. Identifying
specific terms/concepts in specific KOSs
This requires rules for URIs that uniquely identify
specific term/concept records in specific KOSs. Probably requires some
sort of name resolution service (such a KOS registry)
3.1 Links from one KOS to another
3.2 Indexing terms/concepts in the metadata for an object, or any other
reference to a term/concept in a text/object
Elements of an XML KOS data specification
This schema is parsimonious yet allows the recording of many types of data.
It gives enough information to derive a full XML specification.
This specification assumes that data from each source are grouped, so
that source attribution is not needed for each element; otherwise the structure
would be much more complex. This works for a communications format but
not for an internal database format.
The term itself is indicated in a relationship of type TERM. This allows
for terms in multiple languages for the same concept and simplifies the
schema since elements in term would be the same as in relationship target.
Addition of the scope element was inspired by the Topic
Map Standard.
Most schemes advanced for KOS data hard-code the permissible relationship
types as tags. This makes it very hard to introduce new relationship types.
The scheme proposed here is based on a more elegant principle: it simply
provides a generic syntax for recording relationships and makes the relationship
type a data element recorded in an appropriate tag. The scheme needs a
method (not given here) for indicating a relationship set defined elsewhere
and used within the source or for defining a relationship set for the source.
A relationship must specify the relationship types and domain and range
for each. RDFS could be used for this specification (RDF object classes
are entities, RDF properties are relationship types). A system processing
data organized in this scheme would process a relationship instance as
follows: look up the relationship type in the relationship set and get
the proper entity type for domain and range, respectively. Then check the
entity type in the source (domain) slot and the target (range) slot to
verify agreement with the restrictions. The scheme is neutral as to how
concepts, terms, and relationship types are identified. The identifiers
could be URIs, system-assigned numbers, character strings representing
codes or character strings representing terms.
Default is minOccurs="1" maxOccurs="1"
Source (minOccurs="0" maxOccurs="unbounded")
Pointer to or definition of relationship set used
Unit: Concept or term or group of terms (minOccurs="0" maxOccurs="unbounded")
Unique identifier
Type
of concept/term [from list of values, to include facetHead]
Hierarchy position (minOccurs="0" maxOccurs="unbounded")
Hierarchical level
Class number / notation
Scope for which this concept/term holds (minOccurs="0" maxOccurs="unbounded")
Relationship (minOccurs="0" maxOccurs="unbounded")
Relationship type
Relationship target
/* See below for structure. */
Relationship strength (minOccurs="0" maxOccurs="1")
Audience level /* Of this relationship */ (minOccurs="0"
maxOccurs="unbounded")
Perspective /* Of this relationship */ (minOccurs="0"
maxOccurs="unbounded")
Scope for which this relationship holds (minOccurs="0" maxOccurs="unbounded")
Relationship, added information (minOccurs="0" maxOccurs="unbounded")
/* This could be a scope note explaining
the relationship, an image illustrating the relationship, another term,
etc. */
Type of added information /* Relationship types
might be reused here. */
Relationship target
Audience level /* Of this piece
of info. */ (minOccurs="0" maxOccurs="unbounded")
Perspective /* Of this piece of information */
(minOccurs="0" maxOccurs="unbounded")
Where relationship target has this structure (unifying term, text, images,
multimedia document)
Relationship target
Type
/* Includes types of terms (descriptor,
other preferred term, non-preferred term and types of texts and other documents,
may be an elaborate hierarchy. */
Target value (a term or a document)
Term
Term variant (minOccurs="0" maxOccurs="unbounded")
Type of variant
/* Such as Preferred Spelling, other SPelling, ABbreviation,
Full Term. */
Term form (complete term or Stem plus suffix)
Complete term
Stem plus suffix
Stem
Suffix
Document
Language (zero to many, exactly one for terms)
Audience level /* Of this relationship target */
(minOccurs="0" maxOccurs="unbounded")
Perspective /* Of this relationship target */
(minOccurs="0" maxOccurs="unbounded")
Scope for which this/term holds (minOccurs="0" maxOccurs="unbounded")
