=Paper=
{{Paper
|id=Vol-464/paper-7
|storemode=property
|title=WIKITAAABLE: A semantic wiki as a blackboard for a textual case-base reasoning system
|pdfUrl=https://ceur-ws.org/Vol-464/paper-12.pdf
|volume=Vol-464
|dblpUrl=https://dblp.org/rec/conf/semwiki/CordierLMNST09
}}
==WIKITAAABLE: A semantic wiki as a blackboard for a textual case-base reasoning system==
https://ceur-ws.org/Vol-464/paper-12.pdf
WIKITAAABLE: A semantic wiki as a blackboard for a
textual case-based reasoning system
Amélie Cordier1 , Jean Lieber2 , Pascal Molli2 ,
Emmanuel Nauer2 , Hala Skaf-Molli2 , Yannick Toussaint2
1
Université de Lyon, CNRS,
Université Lyon 1, LIRIS, UMR5205, F-69622, France.
{firstname.lastname}@liris.cnrs.fr
2
INRIA Nancy -Grand Est
Nancy Université
LORIA – UMR 7503. B.P. 239, F-54506 Vandœuvre-lès-Nancy cedex, France.
{firstname.lastname}@loria.fr
Abstract. Semantic wikis enable a community of users to produce for-
malized knowledge readable and usable by machines. To take one step
further, one can use a semantic wiki as a blackboard allowing humans
and machines to interact in order to build knowledge that is useful for
both humans and machines. In this paper, we present a case study of the
use of a semantic wiki (Semantic Media Wiki) as a blackboard to manage
culinary data and knowledge. This case study is performed within the
context of the TAAABLE application, a case-based reasoning web system
aiming at solving cooking problems on the basis of existing recipes. With
WIKITAAABLE, an evolution of TAAABLE based on a semantic wiki, we show
how a semantic wiki assists users in their knowledge management tasks
by taking into account user feedback. The issues related to the integration
of several knowledge management mechanisms in a single application are
discussed at the end of the paper.
1 Introduction
Wikis have demonstrated how it is possible to transform a community of
strangers into a community of collaborators. By integrating Semantic Web tech-
nologies, semantic wikis [1–3] allow this new community of contributors to
produce formalized knowledge readable by machines. Maybe the next step is
to use semantic wikis as a blackboard where humans and machines can interact
together for producing knowledge that is updatable by humans and machines.
We investigate in this paper how this can be achieved using the TAAABLE
system [4] as a case study. The TAAABLE system3 allows users to query a cooking
recipe base to solve cooking problems. For example, a user can submit the
following request: “I want a dessert with rhubarb but without chocolate”. If
no recipe exists with the specified characteristics, an existing recipe is adapted
3
The TAAABLE system is accessible online at http://taaable.fr/
in order to answer the request. The system relies on a case-based reasoning
(CBR [5]) engine to perform adaptation. The CBR engine uses different data and
knowledge sources: a set of indexed recipes, an ontology of ingredients, types of
dishes, geographical origins of dishes and types of diets (vegetarian, nut-free,
no alcohol) and a set of adaptation rules. The indexed recipes are computed
from recipes written in natural language. An indexing tool uses the different
ontologies to index the recipes.
If the system is working fine, the maintenance of recipe base and knowledge
is cumbersome. The indexing tool performs natural language processing which
is error prone. In addition, the system is not able to capture the users feedback
to improve its internal adaptation capabilities.
In this paper, we study how a semantic wiki can be helpful in the TAAABLE
system for managing data (e.g. cooking recipes, terminological information in
the domain of cooking) and knowledge (e.g. ontology and adaptation rules)
feeding the CBR engine.The semantic wiki is used as a blackboard where hu-
mans and computers interact to produce knowledge. Humans can add new
recipes, correct indexing of existing ones and give feedback about the results of
adaptation. Machines can perform adaptation of users’ queries and indexing of
recipes. Machines can also take into account user feedback in order to improve
adaptation and indexing.
The paper is organized as follows: section 2 describes the current application.
Section 3 describes how we are building the WIKITAAABLE system based on
Semantic Media Wiki. The last section concludes the paper and points out
future works.
2 The TAAABLE project
The TAAABLE project was initially designed to participate to the Computer Cook-
ing Contest4 (CCC) challenge, an international scientific challenge that aims the
confrontation among systems able to solve cooking problems. Candidate sys-
tems must be able to answer queries expressed in natural language by retrieving
or adapting existing recipes given the user constraints. The recipe book, com-
mon to all participated systems, is a set of textual recipes described by a title, an
ingredient list, and a set of preparation instructions. User requests may include
constraints on ingredients, dish types and dish origins.
The TAAABLE project involves researchers interested in various knowledge-
based systems fields such as case-based reasoning (CBR), ontology engineering,
data and text mining, text indexing and hierarchical classification. TAAABLE
entered the CCC in 2008 and won the vice-champion award. The ongoing re-
searches on this project aim at improving the efficiency and the possibilities of
evolution of the TAAABLE application.
In the next section, we detail how the TAAABLE application addresses the
retrieval and the adaptation of recipes.
4
http://www.wi2.uni-trier.de/eccbr08/index.php?task=ccc
2.1 The TAAABLE application
Fig. 1. TAAABLECBR web user interface.
The web user interface allows the user to enter her query. The system answers
a query by returning a set of recipes satisfying the user’s query. If adaptations are
needed, they rare displayed to the user. Figure 1 shows an example of response
for a "pie with orange”.
Adapting a recipe consists in replacing some ingredients by some others.
By clicking on a given recipe, the user will reach the recipe, including the list
of ingredients that have to be substituted. In figure 2, the Key Lime Pie recipe is
adapted by replacing key lime juice and key lime by orange.
The architecture of the TAAABLE system is composed of two distinct parts.
The offline part of the system focuses on the management of the knowledge
base and the indexing of the CCC recipes. Specific tools have been developed
for that purpose. The result of the offline part is a set of data that will be used
for bootstrapping the CBR engine plugged behind the web user interface (in the
online part).
The design of these two parts is detailed in the next section.
Fig. 2. An example of recipe adaptation.
Fig. 3. Architecture of the TAAABLE system.
2.2 Knowledge Organization
A hierarchy of ingredients. The CBR engine adapts a recipe by substituting some
ingredients by other ones. However, for sake of simplicity, each ingredient
involved in the adaptation process of a recipe is replaced by one ingredient
(1 to 1 substitution). The underlying idea is that a recipe can be adapted by
substituting an ingredient by another “close” ingredient. Therefore, ingredients
are organized in an ontology. This ontology is used by the CBR engine to calculate
the cost of a substitution: the closer the ingredients, the lower the cost. For
instance, orange is closer to lemon than apple.
Hierarchies of recipe types. In order to type recipes, three other hierarchies are
defined. Dish moments such as appetizer, dessert, . . . , dish types such as cake,
pizza, . . . and finally dish origins such as Mediterranean, Chinese, . . . .
We call ontology in the following, the four above hierarchies. The ontology
describes concepts of the domain. The link between the conceptual structure
(the ontology) and the linguistic level (recipes) is performed thanks to the ter-
minological base: each concept of the ontology is associated with its linguistic
forms, i.e. a set of term variations. For instance, the concept litchi is associated
to the terms litchi, lichi, lychee, leechee, and laichee.
The ontology and the terminological base have been built jointly by the
experts. Depending on the results of the indexing process (see below) which
may highlight lacks in the terminology, the expert manually decides which
terms and concepts (associated to terms, if needed) should be added and the
place of the concept in the hierarchy.
In addition to the ontology, the CBR engine may use adaptation rules to
adapt a recipe. An adaptation rule is an ordered pairs of ingredients sets (s1 , s2 )
with a cost given by the expert. (s1 , s2 ) stating that the set of ingredients s1 can
be replaced by the set of ingredients s2 .
Adaptation rules and the ontology form the knowledge base.
2.3 Indexing the recipes according to the terminological base
The indexing process is an automatic process which creates the indexing of
the recipes according to the terminological base. It takes as entry recipes in
their initial textual form. For each recipe, the output of the indexing process is
(1) tagged recipes: an XML (textual) form of the recipe where terms from the
terminological base are tagged, (2) a list of concepts indexing recipes written in
propositional logic (3) Error reports: a set of ingredient lines in the initial recipe
where no term from the terminological base has been identified (possible lack
in the base). We detail below these points.
Tagged recipes. The CCC recipes are given in a loosely structured XML format:
tags are used for identifying the recipe title (TI element), the ingredients (IN
elements), and the preparation (PR element). The indexing process adds tags to
the ingredient part of recipes. It searches into the ingredient lines (tagged IN) the
presence of terms of the terminological base, and introduces for each term the
concept involved. The ingredient line 300 g mashed bananas will be
tagged as banana300gmashed
where banana is the concept associated with the term ba-
nanas, 300 is the quantity, g is the unit, mashed is
a “qualifier”, is the rest of the ingredient line not recognised by the parser.
Here it is empty. The tagged line is used by the expert to control the correctness
of the parsing.
Types (dish types, origins, and moments) of a recipe are not explicitly men-
tionned in the initial form of a recipe. It is automatically computed by three
steps process. First, it searches if a recipe with the same title exists in the
recipesource.com web database with some type information. If it fails, it searches
if the title of the recipe contains terms that represents a type of dish or an origine
(e.g. Banana Butterfinger cake). Finally, if step 2 fails, the process uses a set of
association rules ingredient(s) → type or origin (e.g. mascarpone ∧ coffee → tiramisu)
to type the recipe. Association rules have been previously extracted from the
complete recipesource.com web database.
Type indexation is quite noisy. 30% of recipes are not assigned to any type,
some types (moment . . . ) are missing, and there are some errors (e.g., "pizza
sauce” is not a pizza). The experts have to check manually these tags for each
recipe.
Indexing recipe in a propositional form. The knowledge representation lan-
guage used by the CBR engine is a fragment of propositional logic. The ontology
is encoded as a set of axioms a ⇒ b. For example, the axiom apple ⇒ fruit of O
states that any recipe with apples is a recipe with fruits.
All recipes of the recipe book are indexed by a conjunction of literals. For
example, the recipe of the apple pie (denoted by R) is indexed by:
Idx(R) = apple ∧ pie ∧ sugar ∧ pastery
The set of indexed recipes constitutes the case base of the system.
The indexed recipes resulting from the indexing process and the ontology
are exploited by the CBR engine to answer user queries.
Error reports. We deal here with two main types of errors. One is coming from
bad writing of recipes. The other one is due to concept missing in the ontology.
All these errors should be corrected for the CBR engine to run properly.
Bad writing of recipes. Parts of recipes need corrections because of different
typographic mistakes:
– 4 ts Baking power should be corrected into 4 ts Baking
powder,
– the two consecutive ingredient elements 1 lb Boneless pork, cut
in 3/4Inch cubes should be merge in one ingredient line
1 lb Boneless pork, cut in 3/4 inch cubes,
– Salt; pepper, Worcestershire and lemon juice should be
split into SaltpepperWorcestershire
lemon juice).
Most of these errors have been detected by the experts while checking the
tagged recipes.
Missing concepts in the ontology. Some ingredient lines were not indexed by any
terms of the terminological base. This means that no ingredient concept was
recognised in this line. The expert has to check first that the line is correctly
written. If it is correct, the error comes from missing concepts in the ontology or
from missing terms in the terminological base. For example, if the spam concept
5
does exist in the ingredient hierarchy then the ingredient line 1/2 cn
Spam, in 3/4" cubes cannot be indexed. The expert has to add the new
concept spam in the ontology, defines its position in the hierarchy, and associates
this concept to a list of terms (spam . . . ) in the terminological base. Otherwise,
if the concept exists in the ontology but the term is not recognised. Then, only
the terminological base has to be updated. This error will be corrected at the
next run of the indexing process. The same problem exists for the hierarchies of
types, moments and origins.
2.4 Case-based reasoning
Querying the system. A request expressed in natural language is processed
through the web interface and is formalized in the system by a conjunction
of literals. For example, in TAAABLE, the request “I want a dessert with rhubarb
but without chocolate” is transformed in a query, denoted Q:
Q = dessert ∧ rhubarb ∧ ¬chocolate
Retrieval and adaptation mechanism. The first step of the CBR process consists
in retrieving among the available recipes, a recipe "similar” to the query. The
retrieval is performed by classification on the basis on the query and the recipes
indexes. First, a strong classification is applied: the system searches for a recipe
whose index matches exactly the index of the query. If strong classification fails,
a smooth one is applied: the query is generalized until a satisfactory solution is
found [6]. Smooth classification leads to an approximate matching of the results
and implies an adaptation of the retrieved recipe for answering the query.
The adaptation of a recipe uses adaptation knowledge. In the first version
of TAAABLE, adaptation knowledge is only given by ingredient substitutions.
For example, an apple pie recipe can be adapted in a rhubarb pie recipe by
substituting apples by rhubarb in the recipe. However, one might like to perform
a more complex adaptation of this recipe. This will be possible in a future version
of the application. For example, the adaptation of the apple pie recipe will be
performed by substituting rhubarb to apples and by adding sugar to the recipe
in order to make the recipe less sharp.
2.5 Synthesis
Obviously, the knowledge base used by the CBR engine is neither correct nor
complete and needs to be updated and improved. However, due to the inde-
pendence of the two parts of the TAAABLE system, it is impossible to take into
5
Spam stands for spiced ham, a kind of precooked canned meat.
account interactively the user feedback to make evolve the knowledge base of
the system. This is a significant limitation of the current architecture. From a
more practical point of view, it has also been a limitation during the develop-
ment of the first version of TAAABLE: we were not able to improve the knowledge
base online and any modification was time-consuming.
Hence, for the next version of the application, the goal is to link the two
parts, i.e., the CBR engine and the knowledge base management tools, in order
to be more efficient and to be able to take into account user feedback in the
application.
3 WIKITAAABLE: A semantic wiki for TAAABLE
Fig. 4. WIKITAAABLE components.
In this section, we present the new generation of the TAAABLE system, called
WIKITAAABLE.6 In WIKITAAABLE, we address many problems of the TAAABLE
application by using Semantic Media Wiki (SMW) [1] as a blackboard. The
semantic wiki allows users to browse, query, edit, and validate the knowledge
base as pointed out in [7]. In addition, the knowledge base can be updated by
results produced by the CBR engine and by the automatic indexing tool. The
architectural view of the system is presented in figure 4.
6
WIKITAAABLE is not yet available for public because of the Computer Cooking Contest.
Fig. 5. WIKITAAABLE ingredient ontology.
Semantic Media Wiki. Semantic Media Wiki is used as a blackboard by users,
the CBR engine and the recipe indexing bot. The CBR engine and the recipe
indexing bot rely on a set of predefined semantic queries to gather their
inputs. The knowledge base is represented by a graph of semantic wiki
pages. We have represented as semantic wiki pages the indexed recipes, the
ingredient ontology and the ontology of dish types (see figure 5).
Mediawiki Web User Interface. This is the regular user interface of semantic
media. Through this interface, users can add new recipes and modify the
ingredients, the types of dishes and the origins of dishes.
Recipe Indexing Bot. The recipe indexing bot crawls the recipe pages, extracts
ingredient information, and updates recipe pages with semantic indexings
and categorization of recipes. The crawling and updates of recipes is done
using the mediawiki API, accessing the knowledge base is done using pre-
defined semantic queries. The following example illustrates the input of the
Recipe Indexing Bot:
== Ingredients ==
* 1 c rice
* 2 c water
* 1/2 c sugar
* 1 ts salt
* 2 c evaporated milk
* 1 c raisins
* 3 eggs separated
* 3/4 ts vanilla
Fig. 6. indexed recipe of “ARROZ DULCE”.
* 1/4 ts cinnamon
* 1/4 ts nutmeg
== Preparation ==
* combine the rice water sugar and salt in a large saucepan bring the
water to a boil and cover the saucepan reduce the heat to low and
continue to cook for 12 - 15 minutes or until the water is absorbed
combine the milk and egg yolk add them to the rice then mix in the
raisin vanilla and cinnamon simmer for five minutes remove from the
heat beat the egg white until stiff fold them into the rice chill and
garnish with nutmeg before serving also taste good warm
The above recipe is indexed in the semantic wiki as presented in figure 6.
We used the n-ary relationship of Semantic Media Wiki to represent an
ingredient line.
CBR. The CBR engine retrieves its knowledge base through predefined semantic
queries. Next, it is able to answer requests issued using the CBR web interface
Fig. 7. TAAABLE user feedback.
(cf. figure 1). Our idea is that the CBR user interface proposes recipes based
on adaptation of existing recipes. Users are invited to give feedback about
recipes. The figure 7 illustrates how we can capture the feedback of the user.
This interface is currently not functional.
We imagine that the WIKITAAABLE will work like this: Suppose a user
requests "pie” and "orange” thinking about an "orange pie”. The system
presents results as in figure 7. The user chooses “I don’t like it” for the first
proposal because replacing lemon juice by orange in an "apple crumble pie"
does not transform an "apple crumble pie" into an "orange Pie". However,
the proposal 2 and 3 are acceptable. So the user select “I like it” for these
two proposals.
If the feedback is positive, then the new recipe is added to the knowledge
base and a new semantic wiki page is created for it. The new recipe is marked
as "generated”. If the user feedback is negative, then the computed recipe is
also added to the knowledge base and a new page is created. However,this
new page belongs to the category "refused”. These recipes are kept for future
reuse. For instance, experts can analysis refused recipes and use them as a
basis for a failure driven knowledge acquisition [8].
TAAABLE Import Scripts. We have written scripts to import the current knowl-
edge base of TAAABLE into Semantic Media Wiki. The recipe base contains
about 888 recipes, the ingredient ontology contains 8506 different classes of
ingredients. We used RAP - RDF API for PHP to parse the ontology.
4 Discussion and future work
The strength of the semantic wiki comes from the facility for the community
to update and enrich a set of annotated recipes as well as the ontology. The
WIKITAAABLE system has many advantages compared to the original one:
– Users can add new recipes.
– Users can correct indexation of recipes.
– Users can browse and navigate through the ontology.
– Ontology maintainers can quickly modify the ingredient ontology or the
dish type ontology and test the effects on the CBR engine.
– The feedback about adaptation of recipes can now be captured and repre-
sented in the semantic wikis. The knowledge base of the system increases
just by using it.
One of the reasons of the success of CBR systems is that they are theoreti-
cally able to "learn from experience” by acquiring additional knowledge with
each problem solving session. In TAAABLE, however, learning from experience
is difficult because of the lack of an embedded mechanism allowing to use feed-
back for improving existing knowledge bases. A strength of the use of Semantic
Media Wiki in WIKITAAABLE is that it facilitates this process by enabling the en-
richment and the update of data and knowledge by a community of users. The
management of the ontology and the annotated recipes have many advantages
compared to this of the previous version of the application. Indeed, users can
add new recipes, correct indexing of recipes, and browse and navigate through
ontologies. Ontology maintainers can as well easily modify the ontologies and
test the impact of the modifications on the results of the CBR engine. Besides, a
major advantage is that the feedback on the adaptations made by the CBR engine
can be captured and represented in the wiki.
However, the development of WIKITAAABLE raises several issues that are
mainly related to the coherence of the system. How can we guarantee the co-
herence of the systems while several users, often having different viewpoints,
are allowed to modify the knowledge coded in the system? How can we effi-
ciently combine a semi-automatic procedure and a manual enrichment process
to build an ontology? These issues are of major importance because the ontology
plays a central role in the TAAABLE system and is mainly used at two different
levels. It guides the indexing process by identifying concepts involved in each
recipe, and it is used by the CBR system to adapt recipes. If any user can freely
modify the ontology, then the CBR engine and the recipe indexing bot might
produce unpredictable results. Several strategies can be envisioned to tackle
this problem:
– Restrict the update of ontologies to "administrators”. This is a limitation to
the collaborative work. Moreover, the improvement of the ontology strongly
depends on the availability of the administrators.
– Validation of changes before they are integrated in the running system.
This strategy has also several limitations: poor process support by seman-
tic wikis, synchronization problems between several versions of a single
system, and time consuming for "administrators”. Moreover, this does not
solve the problem of conflicts between several concurrent changes.
– Adaptation of continuous integration approaches used in software engineer-
ing in the context of the WIKITAAABLE system. For example, if an adaptation
of a recipe has been validated by several users, then the ontology should
be modified in order to preserve this adaptation (in this case, user feedback
collected by WIKITAAABLE should generate non-regression tests).
– Toward a peer-to-peer WIKITAAABLE? In such an approach, each user would
have his/her own version of the application, relying on a common knowl-
edge base, and would be able to perform personal adaptation of the knowl-
edge. Adaptation would be shared between users on the basis on the confi-
dence they have in their peers.
The next step of the development of WIKITAAABLE is to fully integrate the
CBR engine to the Semantic Wiki and to set up interaction possibilities at several
levels. One particular focus will be put on the ability of the semantic wiki to
represent and manage complex adaptation rules, including Boolean constraints.
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