GEOSPATIAL SEMANTIC QUERY BASED ON CASE-BASED

REASONING SYSTEM

Kay Khaing Win

University of Computer Studies, Yangon,Myanmar

Keywords: Geospatial data, geospatial semantic, semantic query, spatial reasoning, geospatial semantic web.

Abstract: In today’s fast-growing information age, currently available methods for finding and using information on

the Web are often insufficient. Today’s retrieval methods are typically limited to keywords searches or sub-

string matches, therefore, users may often miss critical information when searching the web. After

reviewing the real world Semantic Web, additional research is needed on the Geospatial Semantic Web. We

are rich in geospatial data but poor in up-to-date geospatial information and knowledge that are ready to be

used by anyone who wants to use. In this paper, we implement a framework of geospatial semantic query

based on case based reasoning system that contributes to the development of geospatial semantic web. It is

important to establish a geospatial semantics that support for effective spatial reasoning for performing

geospatial semantic query. Compared to earlier keyword-based and information retrieval techniques that

rely on syntax, we use semantic approaches in our spatial queries.

1 INTRODUCTION

Today’s retrieval methods are offering no support

for deeper structures that might lie hidden in the

data: therefore, users may often miss critical

information when searching the Web. At the same

time, the structure of the posted data is flat, which

increases the difficulty of interpreting the data

consistently. There would exit a much higher

potential for exploiting the Web if tools were

available that better match human reasoning. In this

vein, the research community has begun an effort to

investigate foundations for the next stage of the

Web, called Semantic Web.

A rich domain that requires special attention

is the Geospatial Semantic Web. In the future, the

Geospatial Semantic Web will allow the returning of

both spatial and non-spatial resources to simple

queries, using a browser. However, in the same way

as with the Semantic Web, in order to approach the

Geospatial Semantic Web it is necessary to solve

several problems.

In this paper, we implement a framework of

geospatial semantic query based on case-based

reasoning system that contributes to the

development of geospatial semantic web. We intend

to develop a simple and powerful framework for

people to interpret the semantics of geospatial entity

classes. The remainder of this paper is organized as

follows. Section 2 describes about the geospatial

semantic web and geospatial semantic. Section 3

describes the role of semantic knowledge in case

searches. We describe measuring semantic similarity

in section 4. Section 5 describes case-based

reasoning system. We describe conceptual

framework of geospatial semantic query in section 6.

Finally, we describe conclusion in section 7.

2 GEOSPATIAL SEMANTIC WEB

AND GEOSPATIAL SEMANTIC

Geospatial Semantic Web is a natural extension of

the current geospatial systems and applications that

enable users to query more precisely the data they

need. To accomplish the Geospatial Semantic Web,

two research issues are apparent: 1

is geospatial

data query and 2

is method to assess the semantics

of available data sources. The retrieval methods of

semantic web are developed by incorporating the

data’s semantics and search process. Such a

development needs the development of multiple

spatial and terminological ontologies. The needs of

all the above mentions enforce to the development

of Geospatial Semantic Web.

The Geospatial Semantic Web will be a

significant advancement in the meaningful use of

356

Khaing Win K. (2006).

GEOSPATIAL SEMANTIC QUERY BASED ON CASE-BASED REASONING SYSTEM.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 356-359

DOI: 10.5220/0002469203560359

 SciTePress

spatial information. With the flexible incorporation

of geospatial information retrieval; will become

precise to the level that the results of user queries

will be immediately useful, without weeding out

irrelevant hits. In order to address geospatial

semantic, one needs computational methods that go

beyond syntax comparisons. In the case of the

Geospatial Semantic Web, three types of geospatial

semantics are distinguished, each requiring different

computational methods (Egenhafer 2002):

• semantics of geospatial entity classes

• semantics of spatial predicates

• semantics of geospatial names

The classification of geographic entities is

geospatial, even when no geometry is involved.

Non-geometric concepts, such as building, road,

and place are geospatial concepts that are used for

describing the semantics of geospatial objects.

Semantic relations are a typical way to describe

knowledge about concepts. We refer to geospatial

entity classes by words or sets of synonym

interrelated by hyponymy and metonymy relations.

3 ROLE OF SEMANTIC

KNOWLEDGE IN CASE

SEARCHES

In order to understand and appreciate, the role and

importance of semantic knowledge and sentence

structure in case-retrieval process, we need to

understand the working of systems that do not use

this information and rely only on the word

knowledge. Based on the inputs given by the user, a

relevant case is retrieved and output to the user. A

set of questions are posed to the user, the answers to

which are compared to the ones listed out under the

relevant cases that are retrieved. A question answer

pair typically behaves as an attribute value pair. The

answers provided by the user to the questions posed

are compared to these values and a match is found.

In our method, similar concepts are playing

similar roles in the sentences. A sentence is

represented using an Interlingua called, Universal

Networking Language (UNL). Information in every

sentence is captured at three levels: the concepts that

are involved, the role they play in the sentence, and

attributes that describe their properties. Universal

Networking Language (UNL), proposed by United

Nations University, represents natural language in

the form the links among them see in figure 1. UNL

represents information sentence by sentence. This

hypergraph is also represented as a set of directed

binary relations, each between two concepts present

in the sentence. Concepts are represented as

character strings called Universal Words (UW).

The knowledge within a document is

represented in three dimensions: Universal Words

(UW): describe concepts that are present in a

document; UNL Relations: describe the relations

between the concepts involved in the sentence and

the roles (e.g. subject or object in case of nouns) that

they play in conveying the meaning of a sentence;

UW attributes: capture and represent properties of

concepts like tense of a verb.

4 MEASURING SENTENCE

SIMILARITY

Similarity between two sentences is measured on

two counts: how similar are the concepts involved in

the two sentences and how similar roles do the

concepts play in the sentence? Since relations

describe the roles that concepts play in the meaning

of the sentence, similar structure sentences will have

similar relations in their respective UNL

representations. Taking UNL representation, we

compare each of the concepts occurring in UNL

representation with the concepts that appear in the

UNL representation for the problem sentence. The

similarity score is computed using the method

proposed by (Resnik, 1999) where similarity of two

concepts is determined by the information that they

share indicated by the most specific concept that

subsumes them both in a concept hierarchy. Resnik

used WordNet for this hierarchy of concepts. For

every concept, its likelihood of occurring in the

document is calculated by counting the number of

instances of itself and the concepts subsumed by it in

the document. Therefore, the more general a

concept, the more number of occurrences it will

have. Probability (or likelihood) of occurrence of a

concept is given as

P(c) =Nc/N

where Nc is the number of times a concept C occurs

in the document and N is the total number of words

in the document. Using the Information Content

Theory, the Information Values associated with each

concept C is negative log of the likelihood of

occurrence of the concept.

IC(c) =-ln (P(c))

We too used WordNet to arrange concepts in

a hierarchy and assign them Information Content

Values in the manner proposed by (Resnik, 1999).

However, in Resnik's method, the sense of the

concepts being matched is not known.

GEOSPATIAL SEMANTIC QUERY BASED ON CASE-BASED REASONING SYSTEM

357

Figure 1: UNL graph for “My machine is executing programs”.

Therefore, a similarity score is measured for all

senses of the two concepts and the maximum among

them is chosen. We uses K-Nearest Neighbor (kNN)

algorithm to determine the similarity between cases.

Use of UNL Universal Words helps us restrict our

attention to only one sense of a concept and

therefore produces the most useful similarity score.

If there are N1 and N2 nodes (or words) in the two

sentences S1 and S2 respectively, then the concept

similarity measure is calculated as

(∑

∈

∑

∈

SimScore (n

, n

)) / (N

)

The sum of all the similarity scores over all pairs of

concepts that are matched for two sentences is taken

and averaged over the number of comparisons made.

We use this concept similarity in our geospatial

semantic query system. In our proposed system

(CBR system), the case base reasoner use this

concept similarity measure to retrieve the most

similar case (or cases) comparing the case to the

library of past cases.

5 CASE BASED REASONING

SYSTEMS

CBR is a view of knowledge acquisition method for

problem-solving and interpretation, and a method for

machine learning. CBR is to solve a problem by

remembering a previous similar situation and by

reusing information and knowledge of that situation.

In Case-Based Reasoning (CBR) systems expertise

is embodied in a library of past cases, rather than

being encoded in classical rules. Each case typically

contains a description of the problem, plus a solution

and/or the outcome.

We implement a framework of geospatial

semantic query based on case based reasoning

system that contributes to the development of

geospatial semantic web. In the framework of

geospatial semantic query, spatial relations are

stored in case based library of case based reasoning

system. Spatial relationships between objects

provide details of the retrieve location of objects

e.g., “the train is to the right of the platform.” Such

relationships are especially needed to locate objects

in which case the location of such objects may be

approximate during the spatial relationships between

the objects it is near. It is impossible to determine

the spatial relationship between x and y when x

completely obscures y, as would be the case if x was

inside of y. Since this information is not always

determineable from object co-ordinates, without this

aspect, semantic query cannot be supported. Spatial

relationships may be represented explicitly via

relations. We intend this reasoning system to be

geospatial reasoning using the case based reasoning

methodology. Geospatial reasoning is widely used

by humans to understand, analyze, and draw

conclusions about the spatial environment.

6 CONCEPTUAL FRAMEWORK

OF GEOSPATIAL SEMANTIC

QUERY

To illustrate how the principles of the conceptual

framework of geospatial semantic query based on

case-based reasoning system. Our proposed

framework shown in figure 2 is intended to

implement on the geospatial semantic query system

that contributes to the development of geospatial

semantic web. The sole purpose of this task is to

make the system understand the terms appearing in

the user’s query input. The reasoning capability of

models of spatial relations is critical to complete the

task of geospatial semantic query. To keep pace with

future developments in geospatial reasoning, we

argue that the system should be designed with an

open architecture to allow for new models and

extensions of existing models to be incorporated into

the system easily.

Our proposed framework mainly consists of

case based reasoning system. Spatial relations are

stored in case based library of case based reasoning

system. Case based reasoner matches the current

problem on the query content with the cases in the

case based library, and similar cases are retrieved.

We examine the use of pairs of terms found in close

proximity to each other to be used as the query

terms. Additionally, we are conducting experiments

using a more refined problem-specific sense of

relevance: a case is considered relevant only if it is

actually cited in the actual opinion of the problem

case. The retrieved cases are used to suggest a

solution which is reused and tested for success.

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

358

Figure 2: Conceptual framework of geospatial semantic query.

If necessary, the solution is then revised. Finally the

current problem and the final solution are retained as

part of a new case. The case based reasoner

produces the query result (final solution or outcome)

to the user.

7 CONCLUSIONS

This paper introduces the new concept of geospatial

relations on case-based reasoning and process a

solution to query of geospatial semantic. To handle

geospatial semantic query, we propose a conceptual

framework that takes advantage of case-based

reasoning system. The emphasis of this paper has

been reasoning on geospatial relations to handle

geospatial semantic queries. In our system, we

presented an approach for querying geospatial

semantic based on case-based reasoning system that

contributes to the development of Geospatial

Semantic Web. In our system, we use geospatial

relations to be efficient and effective reasoning

system. This paper describes efforts in developing

for conceptual framework of a geospatial semantic

query system.

REFERENCES

Allen J. F., 1983 “Maintaing knowledge about temporal

interval communications” in ACM, 26 (11): 832-843.

Andrea M. R. and Egenhofer M.J, 2004 “Comparing

Geospatial Entity Classes: An Asymmetric and

context-Dependent Similarity Measure”

International Journal of Geographical Information

Science 18 (3).

Bhattacharyya P.,Choudhury S.R.,Gupta S.S., 1998 "Man

power Planning with Case Based Reasoning: The

Selector", International Conference on Knowledge-

based Computer Systems (KBCS).

Chakraborti S.,Balaraman V.,Vattam S.S., 2000 "Using

CBR inexact search for intelligent data retrieval",

International Conference on Knowledge-based

Computer Systems (KBCS).

Egenhofer, M. 2002, “Toward the Semantic Geospatial

Web” ACM-GIS Mclean, VI (in press)

Jones C.B and Purves R. and Ruas A. 2001 “Spatial

Information Retrieval and Geospatial Information

Theory”, pages 322-335. Springer-verlag.

Jones C.B and Purves R. and Ruas A. and Sanderson M.

and Sester M and Van Kreveld M.J. and Weibel R.

2002 “Spatial Information Retrieval and

Geographical Ontologies: an overview of the SPIRIT

Project”. In Proceeding of the 25

Annual

International ACM SIGIR Conference, Pages

387_388

Jun-ichi A., Koioru H., Akira S., Yutaka Y. and Testsuji S.

2002 “Towards the Real World Semantic Web-Web

Search based on Spatial and Temporal Metadata”

vol.1.No.3 June 2002.

Mcauley K. 2001 “Geospatial Mapping and Navigation of

the Web.” In proceedings of Tenth International

World Wide Web Conference, page Session P7.ACM

Press.

Mclean, Virginia, 2002. “Toward the semantic geospatial

web” Proceedings of the 10th ACM international

symposium on Advances in geographic information

systems.

Ohlbach, H. J and Lorenz B. 2005 “Geospatial Reasoning:

Basic Concepts and Theory” 31 March 2005

REWERSE.

Resnik P., 1999 “Semantic similarity in a taxonomy: An

infomation-based measure and its application to

problems of ambiguity in natural language”, Journal

of Artificial Intelligence Research.

Sistal P., Yu C. ,and Haddack R. 1994 “Reasoning about

spatial relationships in picture retrieval systems”. In

Proc. Of the 20

Int’t Conf. on very large Data

Bases.

Sharma J. and Flewelling D. M. 1995 “Inferences from

combined knowledge about topology and directions.”

In Proc. Of 4

Int’t Symposium on large Spatial

Databases, Portland, Marine, USA

GEOSPATIAL SEMANTIC QUERY BASED ON CASE-BASED REASONING SYSTEM

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