Many people are working on the Semantic Web with the main objective being to enhance web searches. Our proposal is a new research strategy based on the existence of a discrete set of semantic relations for the creation and exploitation of semantic networks on the web. To do so, we have defined in a previous paper (Álamo, Martínez, Jaén) the Rhetoric-Semantic Relation (RSR) based on the results of the Rhetoric Structure Theory. We formulate a general set of RSR capable of building discourse and making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. These knowledge nodes can then be elaborated in the same way. This network structure in terms of RSR makes the objective of developing automatic answering systems possible as well as any other type of utilities oriented towards the exploitation of semantic structure, such as the automatic production of web pages or automatic e-learning generation

Alamo Lobo, Francisco Javier del

Jaen Gallego, Jose Alberto

Martínez Fernández, Raquel

English

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

THE INTELLIGENT WEB 
F. Javier del Álamo, Raquel Martínez and José Alberto Jaén 
Department of Control, Electronics and Computer Science 
E.T.S. Ingenieros Industriales-Universidad Politécnica de Madrid, Spain 
frandelalamo@gmail.com, raquelm@etsii.upm.es, jjaen@etsii.upm.es 
Keywords: Rhetoric structure theory, Rhetoric-semantic relation, Semantic network, Knowledge node. 
Abstract: Many people are working on the Semantic web with the main objective being to enhance web searches. Our 
proposal is a new research strategy based on the existence of a discrete set of semantic relations for the 
creation and exploitation of semantic networks on the web. To do so, we have defined in a previous paper 
(Álamo, Martínez, Jaén) the Rhetoric-Semantic Relation (RSR) based on the results of the Rhetoric 
Structure Theory. We formulate a general set of RSR capable of building discourse and making it possible 
to express any concept, procedure or principle in terms of knowledge nodes and RSRs. These knowledge 
nodes can then be elaborated in the same way. This network structure in terms of RSR makes the objective 
of developing automatic answering systems possible as well as any other type of utilities oriented towards 
the exploitation of semantic structure, such as the automatic production of web pages or automatic e-
learning generation. 
1 BASICS: SUMMARY OF THE 
RHETPORICAL SEMANTIC 
RELATIONS (RSR) 
The primary objective of computational linguistics is 
the study and computerized treatment of human 
language, with the goal of providing a natural 
language model. 
Based on the significant contributions of the 
Rhetorical Structure Theory (William Mann, Sandra 
Thomson 1999), we have formulated a set of 
Rhetorical Semantic Relations (RSR) for knowledge 
representation. 
The RST provides an explanation of the coherence 
of the discourse. We assume the results of RST and 
propose a set of relations capable for representing 
knowledge. 
In short, the RST defends the principle that the 
reading of a text does not always produce an 
expression of coherence. There are texts that are 
syntactically and semantically correct but difficult to 
understand. The theory explains the coherence of the 
discourse in terms of the existence of a kind of 
relation between blocks of text: the nucleus-satellite 
relations and the multinuclear ones. Without going 
into further detail, RST explains that the fact we can 
understand some texts is related both to the presence 
of rhetorical relations and to the distance between 
the fragments of text. (William Mann, Sandra 
Thomson 1999),(Bosma, 2005). 
1.1 RSR Formulation 
Based on RST, we wonder if, as there is a finite set 
of rhetorical relations between two different blocks 
of text, there is also a reasonable bound set of 
relations (semantic primitives) in the same way for 
any knowledge representation. This set must include 
the rhetorical relations as a subset. We will call this 
set RSR (Rhetorical Semantic Relations), and our 
goal is to build the semantic networks definitions in 
terms of RSR.  
There are situations, such as those studied by 
Katheleen McKeown in Text Generation 
(McKeown, 1985), in which there are rhetorical 
structures nested within others. According to our 
results, by analyzing this structure in a large number 
of cases, we have seen that there is a certain point 
when the fragment has a semantic structure that does 
not correspond to the relations proposed by the RST. 
For this reason, as a second step, we propose to 
continue analysing the blocks of text obtained from 
the rhetorical analysis by the detection of semantic 
relations, such as “is_a”, both in “is_a (individual-
category)” such as “is_a (category-category)” forms 
the “is_part_of” relation and the “causal” ones. 
662
 In “Basic Method of instruction” (Reigeluth, 2007), 
the author makes a distinction between common 
features, referred to as those features shared by all 
the members (or subclasses) of the class, and 
differential features, such as those which 
differentiate the individual or subclass from the rest 
of the individuals (or subclasses) of the same class. 
The summarized results are shown in the table 
below, where we have included the canonical 
expression, showing the representative fragment of 
text for all the rhetorical-semantic relations 
including both the relation to be used and the type of 
content of the child node in capital letters  
Table 1: RSR Canonical expression. 
Nr. Denomination  Canonical expression (*) 
1 Transformation Changes the  ‘OBJECT’ … 
2 Feature Shows the ‘FEATURE’…  
3 Function Performs the 
‘FUNCTION’… 
4 Location Places in the 
‘LOCATION’… 
5 Objective Pursues the 
‘OBJECTIVE’… 
6 Classify Belongs to the ‘CLASS’… 
7 Coincidence Shows the 
‘COINCIDENCE’… 
8 Difference Shows the 
‘DIFFERENCE’… 
9 Part Shows the ‘PART’… 
10 Effect Produces the ‘EFFECT’… 
11 Result Yields the ‘RESULT’… 
12 Activity Develops the 
‘ACTIVITY’… 
13 Method Is reached by the 
‘METHOD’… 
14 Comparison Is compared to the reference 
‘OBJECT’… 
15 Taxonomy Is organized in ‘CLASSES’  
16 Cause Because of the ‘CAUSE’… 
17 Evaluation Has the ‘VALUE’… 
18 Condition Has the ‘CONDITION’… 
19 Elaboration Is elaborated in the 
‘OBJECT’… 
   
Table 1: RSR Canonical expression (cont.). 
20 Antithesis Is opposed to the 
‘OBJECT’… 
21 Summary Is summed up in the 
‘OBJECT’… 
22 Restatement Can be expressed as 
‘OBJECT’… 
23 Background Is understood because of the 
‘OBJETCT’… 
24 Instrumental 
relation 
Is related to the ‘OBJECT’ 
… 
25 Interpretation Must be interpreted in the 
‘CONTEXT’ 
26 Concession Although the ‘PREDICATE’ 
can be true … 
27 Justify Is justified by the ´THESIS’ 
… 
28 Motivation Is interesting because of the 
‘REASON’… 
29 List Includes the ‘OBJECT 
/CLASS’… 
30 Following Follows the ‘ELEMENT’… 
(*) Note that some of the relations make sense 
only in singular or only in plural. A plural 
expression is equivalent to a set of several identical 
relations between the same knowledge father node 
and different knowledge child nodes. 
In this way, the definition of C= {x, y, z} is 
equivalent to:  C includes x object; C includes y 
object; C includes z object; (The LIST relation in 
the Table 4). An alternative formulation in RSR is 
by means of the inverse relation: x Є C; y Є C; z Є 
C. That is the CLASS relation. 
1.2 RSR Verification 
For the set of RSR verification, we decided to test 
the behavior of the defined set of relations with the 
categories of questions defined in the classic theory 
of QA (Question Answering) proposed for the 
generation of automatic systems. 
Of the 13 different categories of questions defined 
by in the conceptual categorization in question 
answering (Lehnert, 1978), we have marked the ones 
that are supported in the proposed set of RSR, 
followed by the corresponding RSR in brackets. 
1. Causal antecessor. (Cause) 
2. Objective oriented. (Objective) 
3. Enabling. (Condition) 
THE INTELLIGENT WEB
663
 4. Causal consequent. (Effect) 
5. Verification 
6. Disjunctive. (List) 
7. Instrumental / Procedural.(Method) 
8. Complete concepts. 
9. Prospects. (Explanation) 
10. Judgment. (Evaluation) 
11. Quantification. (Evaluation) 
12. Specification de characteristics. (Features) 
13. Request. (Result) 
Regarding verification question support, once we 
have expressed a discourse in terms of RSR, it is 
possible, for example, to have a direct translation in 
terms of prolog predicates. The use of an inference 
mechanism over this knowledge, as a prolog query, 
it is the base for the implementation of a question 
answering system. If the result of this query is true, 
this implies that the facts are true. If false, it is not 
possible to confirm that the proposition is true or 
false with the available knowledge. 
Completing concepts requires a recompilation 
process of all the semantic networks including this 
node and the corresponding predicates. 
The rest of the possible questions are completely 
supported by the set of rhetorical-semantic relations 
defined in the present article, concluding that our 
model supports in a reasonable way the questions 
that an agent can make, either by means of the 
relation or by means of its inverse. 
1.3 Innovation Aspects 
The main innovation aspect of the proposed 
approach is the semantic enhancement of the 
resulting representation. We can represent a set of 
linked pages and resources as a set of knowledge 
nodes interconnected solely by means of RSR (or 
specific RSR synonyms in specific domains). The 
set of web pages will then be rhetorical-semantic 
networks based on these special relationships. 
Ontology corresponds just to one specific RSR 
(classify), but it is not the only one: In this paper, we 
propose a set of 30 basic relations to be used as an 
alternative and more complete treatment of 
ontology, by using the same technological support. 
An important contribution of the RSR approach to 
the semantic web exploitation is to provide an 
instrument for automatic building knowledge bases 
for different applications such as intelligent question 
answering, by replacing node names as variables and 
RSR as relationships of the knowledge base. 
The main applications are in the field of automatic e-
consulting, e-learning generation or automatic 
document production. 
2 PROOF OF CONCEPT 
We have developed a large number of different 
examples to test our proposal. As important as the 
correct interpretation in the stage in which we find 
ourselves, is the didactic feature of the example. 
From this point of view, we have developed 
examples in the field of mechanical engineering, 
instructional design and e-learning production. 
We will show here an example in the field of 
physical sciences and the role of RSR in explaining 
the Archimedes Principle: 
 
Figure 1: Archimedes Principle. 
By automatic identification of nodes, we can obtain 
a set of Node IDs, for example: 
 
Figure 2: Archimedes Principle Node Identification. 
The set of RSR is valid for knowledge 
representation, and it supports the questions 
categories in Q&A theory. We can express any 
content as a network composed of nodes and 
relations of the defined set, and the use of the 
appropriate synonyms It means we can translate 
ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence
664
 directly as a set of Horn clauses in a knowledge 
database: 
Produces_the_EFECT(id_node_1, id_node_11), 
Takes_the_VALUE(id_node_11, id_node_111), 
Produces_the_RESULT(id_node_11, id_node_112),  
Produces_the_RESULT(id_node_11, id_node_113), 
Shows_the_FEATURE(id_node_11, id_node_114) 
This approach is the basis for the e-consulting 
application. It is solved like a query to the 
knowledge database yielding the node identification. 
By using the resultant id_node, it recovers the 
associated content (text, text + graphics, animation, 
web page, equations, etc…). 
3 THE INTELLIGENT WEB 
Many people are working on the semantic web, with 
the main objective being to simplify and to enhance 
web searches. This work is based on the presence of 
certain kinds of tags specifying ‘ontologies’. 
That is finally the main idea of the semantic web: if 
we can specify the classes to which a word belongs 
and tag it, we are establishing absolute relationships 
between words and categories in such a way that we 
have an ‘implicit relationship of ‘belongs to the 
class’ between the tagged word and all classes to 
which it belongs. Certainly, the inverse relation is 
present by means of the implementation of a 
mechanism for recovering all the words belonging to 
a certain class. 
What we propose is a way for tagging the existent 
RSR in an explicit way on every text in a web page. 
As far as we satisfy this proposal, we will exploit the 
resultant text in different ways.  
In our goal of representing knowledge, we must 
begin by wonder what is knowledge? This is 
probably one of the most difficult questions we can 
ask, and the most profound philosophical answer is 
surely out of the scope of this paper, but we can 
agree that “knowledge is a representation of the 
reality in our mind”. 
We think in terms of ideas that we usually express in 
different ways, such as texts, drawings, equations, 
images, sequences of memories, such as videos, etc. 
The important thing here is that they are connected 
in our mind by means of certain kinds of relations. 
Our intention is integrate different contributions 
proceeding from different theories, such as the idea 
of building mental maps in Meaningful Learning 
Theory (Novak, Ausubel, 2002). 
4 CONCLUSIONS 
The set of RSR is valid for knowledge 
representation and it supports the question categories 
in Q&A theory. We can express any content as a 
network composed of nodes and relations of the 
defined set, and the use of the appropriate 
synonyms. It means we can translate directly as a set 
of Horn clauses in a knowledge database. 
We can use different synonyms for different domain 
applications without losing the semantic 
connectivity. It provides a means for the 
development of natural language answering systems. 
It can be a means for the definition of general 
ontology and relations on the semantic web. 
It is possible to automatically generate e-learning 
lessons, documents or Q&A systems from any 
knowledge base generated automatically from an 
RSR expression of contents. 
5 FUTURE LINES OF RESEARCH 
The main lines of research in which we are 
interested and in which we are intensifying our 
efforts include the following: 
• Operations on RSR (RSR Inverses and 
plurals, RSR combinations, The treatment 
of verbal trends in RSR) 
• Creation of a knowledge representation and 
storage model and data architecture capable 
of supporting the definition of knowledge 
networks based on RSR at the same time. 
• Fundamental Cognitive Networks: 
Formulation of a molecular structure of 
knowledge by using the patterns most 
frequently used by people, for discourse 
construction. 
• The elaboration of Knowledge 
Representation Methodology, by using 
rhetoric-semantic networks.  
• The application of Walter Bosma’s results 
regarding rhetorical distance application 
and treatment as semantic weighted 
networks. 
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THE INTELLIGENT WEB
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ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence
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The Intelligent Web

Archivo Digital UPM

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Applications of Rhetorical Structure Theory.

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Description: Diverse linguistic analyses of a fund-raising text”. Pragmatics & Beyond,

Knowledge Modeling and Automatic Web exploitation”.

Meaningful Learning: The Essential Factor for Conceptual Change in Limited or Inappropiate Propositional Hierarchies Leading to Empowerment of Learners.

Pre-publication version, in pdf

Relational Discourse Structure: A Comparison of Approaches to Structuring Text by 'Contrast”. Language in Context: Essays

Rhetorical Structure Theory and Text Analysis. Discourse Description: Diverse linguistic analyses of a fund-raising

Rhetorical Structure Theory: “Toward a functional theory of text organization”.

Rhetorical-Semantic Relations: A proposal for Atomic Representation of Knowledge”.

Text Generation”,

The Process of Question Answering. A Computer Simulation of Cognition”. Yale University, Lawrence Erlbaum Associates,

The Rhetorical Parsing, Summarization,

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