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

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

Javier del Álamo F., Martínez R. and Alberto Jaén J. (2010).

THE INTELLIGENT WEB.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Artiﬁcial Intelligence, pages 662-666

DOI: 10.5220/0002763106620666

 SciTePress

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.

REFERENCES

Mann, William C. and Thompson, Sandra A. (1999) “An

Introduction to Rhetorical Structure Theory”.

THE INTELLIGENT WEB

665

Marcu, Daniel. (1997) “The Rhetorical Parsing,

Summarization, and Generation of Natural Language

Texts”.

Taboada, M. and Mann, William C. (2006) “Applications

of Rhetorical Structure Theory. Discourse Studies” 8

(4): 567-588. Pre-publication version, pdf

Taboada, M. and Mann, William C. (2006) “Rhetorical

Structure Theory: Looking Back and Moving Ahead”.

Discourse Studies 8(3): 423-459. [ Pre-publication

version, in pdf ]

Mann, William C., Matthiessen Christian M. I. M. (1991)

"Functions of language in two frameworks". Word 42

(3): 231-249.

Mann, William C., matthiessen Christian M. I. M. and

THOMPSON, Sandra A. (1992) “Rhetorical Structure

Theory and Text Analysis. Discourse Description:

Diverse linguistic analyses of a fund-raising text” . ed.

by W. C. Mann and S. A. Thompson. Amsterdam,

John Benjamins: 39-78.

Mann, William C. and Thompson, Sandra A, Eds.

(1992a)”Discourse Description: Diverse linguistic

analyses of a fund-raising text”. Pragmatics &

Beyond, New Series. Amsterdam, John Benjamins..

Mann, William C. and Thompson, Sandra A. (1992b)

“Relational Discourse Structure: A Comparison of

Approaches to Structuring Text by 'Contrast”.

Language in Context: Essays for Robert E. Longacre .

ed. by S. J. Hwang and W. R. Merrifield. Dallas, SIL:

19-45..

Lehnert, Wendy G. (1978) “The Process of Question

Answering. A Computer Simulation of Cognition”.

Yale University, Lawrence Erlbaum Associates, J

Willey & Sons.

Mann, W.C., Y Thompson, S.A. (1988) Rhetorical

Structure Theory: “Toward a functional theory of text

organization”. Text, 8 (3). 243-281.

Mckeown, Kathleen (1985) “Text Generation”,

Cambridge University Press.

Reigeluth, Charles. (2007) “Basic Methods of

Instruction”. Indiana University Website.

http://education.indiana.edu ,

http://www.indiana.edu/~ist/faculty/reigelut.html

Bosma, W.E. (2005) “Query-Based Summarization using

Rhetorical Structure Theory”. University of Temple.

15th meeting of CLIN pp 29-44 ISBN 90-76864-91-8..

Alamo, F. Javier del. (2007) “Knowledge Modeling and

Automatic Web exploitation”. Polytechnic University

of Madrid, Industrial Engineering School.

Alamo, F Javier del, Martínez, Raquel, Jaén, José Alberto

(2009) “Rhetorical-Semantic Relations: A proposal

for Atomic Representation of Knowledge”. ITA 09.

Novak, J.D. (2002) Meaningful Learning: The Essential

Factor for Conceptual Change in Limited or

Inappropiate Propositional Hierarchies Leading to

Empowerment of Learners. Science Education, 4(86),

548-571

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

666