General Design Structure of Ontological Databases in Semantic Web

Aarti Singh et. al. / International Journal of Engineering Science and Technology Vol. 2(5), 2010, 1227-1232

General Design Structure of Ontological Databases in Semantic Web AARTI SINGH*[1] , DIMPLE JUNEJA[2] , A.K.SHARMA[3] [1] Research Scholar, Maharishi Markendeshwar University,Mullana Lecturer,Tilak Raj Chadha Institute of Management & Technology, Yamuna Nagar-135001, Haryana, India [2] M.M. Institute of computer Technology & Buisness Management M.M.University, Mullana(Ambala), Haryana, India [3]Y.M.C.A Institute of Engineering Faridabad, Haryana,India *

Email id: [email protected][1], [email protected][2] , [email protected][3] Abstract - Ontologies are useful tool for data integration across heterogeneous data sets and the importance of ontologies is already widespread, yet ontology building is comparatively a less absorbed concept. Ontology specifies a set of vocabulary for a particular problem domain emphasizing on the meaning of terms. The motive of employing ontologies in semantic web directly relates to unabated change in the languages, which are being used to describe a problem. This switch among various languages directly changes the terms used in the definition of problem assuming that the meaning conveyed will be similar to former. However the semantics might change inevitably and the purpose of upgrading a language gets defeated. Therefore the ontological databases were introduced to overcome this issue. Although the general design structure of ontological database is still pending. The premise of this paper is to propose a design structure for the development of ontological database in general. Also, a case study has been presented to analyze the proposed structure. Keywords: Semantic Web, Ontology, Ontological commitment, Contextual information, Software Agents. 1.

Introduction

Ontology is an explicit specification of a conceptualization where, conceptualization is an abstract, simplified view of the world that describes the objects, concepts and other entities, existing in a domain along with their relationships [4]. In Artificial Intelligence ontologies are categorized according to three key dimensions namely, Formality, Purpose and Subject [15] where Formality refers to the design structure and meaning of vocabulary and Purpose and subject matter defines the intended use of the ontology and the nature of the subject matter that the ontology is characterizing respectively. The detailed classification of these dimensions is illustrated in Fig. 1.

ISSN: 0975-5462

1227

Aarti Singh et. al. / International Journal of Engineering Science and Technology Vol. 2(5), 2010, 1227-1232

Dimensions of Ontologies Formality

Purpose

 



 

Highly informal Structured informal Semi-formal Rigorously formal

 

Subject Matter

Communication between people Inter-operability among systems System engineering benefits  Re-Usability  Knowledge Acquisition  Reliability  Specification

  

Domain Ontology Task/Problem solving ontology Representation or Meta-Ontology

Fig. 1 Classification Details of Key Dimensions

Ontologies [10] have emerged as a significant component of Semantic Web [17, 13]. Use of semantics can not be materialized without use of ontology. Due to this reason, development of ontology needs proper attention. Studies [3, 7,14, 15 ] indicated that many techniques have been proposed and are in use, but most of them are application specific. At the time of citation no one general technique is available for the ontology development, which is flexible enough to be applied to any application. This drawback provided motivation for writing of this paper. This paper is divided into four sections. Section 1 provides introduction to ontology. Section 2 explores work of eminent researchers in area of semantic web and ontologies. Section 3 presents the proposed design structure of ontology database in detail. Section 4 concludes the paper with directions for future work. 2. RELATED WORK This section enlightens existing literature and points out challenges in existing ontological database designs. Gruber [4] described the role of ontologies in knowledge sharing activities and proposed a set of design criteria to guide the development of ontologies. Clarity, coherence, extendibility, minimal encoding bias and minimal ontological commitment [11] has been stated as set of design criteria for ontologies whose purpose is knowledge sharing and interoperation among programs based on a shared conceptualization. However the evaluation of design decisions against the criteria depends on the knowledge available and the applications anticipated for a domain. Also the ontological commitment to a strong theory is required for the sharing of valuable mathematical models. Juneja et. al in [5] proposed an agent based semantic matchmaking algorithm that is based on the problem of heterogeneous ontology at user end. However the work is based on the assumption that ontological databases already exist and did not describe the ontological deign structure. Stuckenschmidit et. al [14] described an approach for exploiting partially shared ontologies in multiagent communications. However this approach still needs sophisticated communication protocols that agents can use to find out what are the ontologies shared among them and also the options for re-writing queries. Kerschberg et. al in [6] introduced the knowledge-Sifter agent based architecture to access heterogeneous data sources. The architecture still needs to develop tools for dynamic configuration of new sources into it. Zheng et. al [18] proposed a learnable focused crawling framework based on ontology using Artificial Neural Networks. The main drawback of their work is that its performance depends upon which ontology is used. Sanchez et.al in [12] elaborated the necessity for integrating intelligent agents and semantic web and analyzed the potential benefits of this amalgamation. They proposed SEMMAS (SEMantic web services and Multi-Agent System) framework, which is an ontology based framework for

ISSN: 0975-5462

1228

Aarti Singh et. al. / International Journal of Engineering Science and Technology Vol. 2(5), 2010, 1227-1232 seamlessly integrating intelligent agents & Semantic web services. The evaluation of its performance & usability in several domains of practical interest is yet pending. Chandrasekaran et. al in [1] provided a conceptual introduction of ontologies and their role in information systems and artificial intelligence. In their survey paper they also discussed how ontologies clarify the domain structure of knowledge and enable knowledge sharing. They emphasized gap between knowledge based problem solving and knowledge representation community and indicated that ontologies can serve as sharable knowledge resource. But the actual implementation of this idea is left for future research. Kitamura et. al in[7] discussed the concept of functional ontology. including the functional concepts of fluid related systems only. The evaluation and extension of ontology is left as future work. Fensel et. al [3] provides an ontology based tool environment to speed up knowledge management, dealing with large number of heterogeneous, distributed and semi-structured documents. Uschold in [15] identified two methods for ontology development and presented a framework for comparing and unifying them. Extension of their method and also refinement in level of granularity for different methods is left as future work. Maedche et. al in[9] proposed a framework for ontology learning from legacy ontologies, from free text or from dictionaries or even from existing XML documents. However the usage semantics for imported ontologies are not clear. Refinement of methods for importing legacy ontologies is left for the future. A critical look at the above literature highlights the fact that very few researchers have made an attempt to explore the dimension of ontology development, especially the evaluation of various existing frameworks is still a problem of research. Thus there is still potential for improvement in this dimension. This research makes an attempt to propose a general design structure of ontology database and also an evaluation strategy has been proposed. 3. Proposed Design Structure of Ontology Database The premise of this work is to throw light on structural design details and working of ontology database. This section provides details of design structure of ontology database. The importance of ontology as a conceptual model for capturing and reusing information is clearly reflected through literature survey. For the purpose of constructing ontology the research group at Edinburgh university proposed the first ontology construction method in 1996[16] also known as the ‘Skeletal method’ as shown in Fig. 2. Now turning our attention once again to design structure of ontological database, most ontology construction methods concentrate on modeling aspects, rather than how domain concepts and relationships are to be elicited. This work proposes a technique for ontology development based on LEL (Language Extended Lexicon) technique [8]. The underlying principle of the lexicon is contextualism, according to which context of use of a system must be clearly understood before requirements can be derived and also the motivation of deploying LEL in developing ontological database is the systematization for the elicitation, modeling and analysis of ontology concepts. LEL is a representation of the symbols in the application language anchored on the simple idea of understanding the language of the problem without understanding the problem to be solved, i.e. major emphasis is on understanding the semantics of the problem in hand. Central to LEL are two principles, principle of closure and principle of minimal vocabulary where:  Principle of closure: is to maximize the use of other lexicon terms when describing the notion and behavior response of a new term.  Principle of minimal vocabulary: states to minimize the use of terms external to the Universe of Discourse (UoD). If unavoidable we must ensure that they belong to the basic vocabulary of the natural language in use.

ISSN: 0975-5462

1229

Aarti Singh et. al. / International Journal of Engineering Science and Technology Vol. 2(5), 2010, 1227-1232

Fig. 2 High Level View of Skeletal method proposed by Uschold [15, 16]

Next section discusses the proposed model of ontological database. 3.1 The proposed design structure The proposed ontology is expressed as a set of six tuple (C, R, t, CH, rel_terms, rule_set) where:  C and R are disjoint sets, called the set of concepts & set of relations respectively.  CH  C  C is a concept hierarchy or taxonomy.  t is the set of lexicon terms/tokens.  rel_terms: R→t×t is a function that defines the relation between different terms in the UoD.  rule_set: is a set of axioms for ontology, expressed in appropriate logical language. The process of developing ontology comprises of three major steps:  Identify the UoD for which ontology is required and the sources of information for that.  Identify and prepare a list of relevant terms for the UoD.  Classify the terms in the domain. The terms can be classified as: subject(s), object (o), verb (v) and state (st). Basically each term in the ontology is composed of function/notion and behavior /connotations. At this stage the major emphasis is on extracting the meaning of each term clearly. When describing the terms, we should enforce the principle of closure and minimal vocabulary. The classified terms are than mapped to define a specific ontology as depicted in fig.3 Mapping

C

Procedure

CH

ts, to, tv, tst Term set

R rel_terms rule_set

Ontology Database Fig. 3 Translation of terms in ontology knowledge base

Next section discusses the mapping procedure of term set to ontology set.

ISSN: 0975-5462

1230

Aarti Singh et. al. / International Journal of Engineering Science and Technology Vol. 2(5), 2010, 1227-1232 The mapping procedure This section proposes the mapping procedure (MPo) which performs the translation of terms to ontology. MPo is a three step process described as below: 1) Maintaining the term list (term_list): Chronological list of terms is maintained according to their type (subject, object, verb, state), called as term_list for reference here. 2) Maintaining the behavior list (behavior_list): A list of behavior is maintained for each term where; Behavior is defined as temporal changes of parameter values. The behavior model of a component is ideally independent from the context which the component is used in [7]. 3) Maintaining the function list: A list of functions is maintained for each term where; Function: A function is defined as a result of interpretation of a behavior under an intended goal [7]. Every term is first matched against the term_list for a match. If true, context-based behavior is chosen form the behavior_list and then mapping from behavior to function list is performed. Here it is essential to identify mapping primitives between behavior and function that enable the reasonable and effective interpretation of the behavior. Algorithm for the ontology development is depicted in Fig. 4 given below. procedure onto_develop { identify UoD; activate tokenizer( ) { generate lexicon tokens relevant to UoD; } activate classifier ( ) Input token { for (  token) {Create subject list; Create object list; Create verbs list; Create states list; Create behavior_list; Create function_list; }} activate mapping_procedure( ) { Input from the user; Convert to standard format  token input; Search (token, tokenlist); If(token_input==token_in_list) {choose context_based_behavior  behavior_list; behavior  function_list; output relevant context from function_list; }

Fig. 4 Algorithm for ontology development

An Example Following example illustrates the proposed algorithm. Consider a UoD say animal. Let the animal to be searched is mouse. This term can have two behaviors for instance, either an animal i.e. biological being or input component of computers i.e. mechanical thing. Now on the basis of the context in which above term appears appropriate behavior can be opted. Depending on the behavior of the term, appropriate function can be chosen to satisfy the context. This example can be expanded and generalized using bottom up approach and a broad ontological database can be formed.

ISSN: 0975-5462

1231

Aarti Singh et. al. / International Journal of Engineering Science and Technology Vol. 2(5), 2010, 1227-1232

Mouse

Term to be searched Behavior based search

Biological mouse?

Mechanical mouse? Mapping from behavior to function

Appearence

features

Sense

Point

Click

Fig. 4 Mapping of term in ontology knowledge base

4.

Conclusions & Future Work

In this paper we have tried to throw light on key concepts for the development of ontological database. This work provides a general flexible model for the development of ontological database, which can work well in almost all situations. However the performance of above model is yet to be evaluated and is left as part of future work. REFERENCES: [1] [2] [3]

[4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

Chandrasekaran B., Josephson R.J., Benjamin R.V. (1999): What are Ontologies and Why do we need them?. IEEE Intelligent Systems, vol. 14(1), pp. 20-26, Jan-Feb. 1999. Elmore M., Potok T., and Sheldon F. (2003): Dynamic Data Fusion Using an Ontology-Based Software Agent System. In Proc. of the IIIS Agent Based Computing, Orlondo, 2003. Fensel D., van Harmelen, F., Klein, M., Akkermans, H., Broekstra, J., Fluit, C.,van der Meer, J., Schnurr, H.-P., Studer, R., Hughes, J., Krohn, U., Davies, J., Engels, R., Bremdal, B., Ygge, F., Lau, T., Novotny, B., Reimer, U., and Horrocks, I. (2000): On-To-Knowledge: Ontology based Tools for Knowledge Management. In Proceedings of the eBusiness and eWork 2000 (EMMSEC2000) Conference, Madrid, Spain. Gruber R. Thomas (1993): Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In International Journal Human-Computer Studies, vol. 43, pp. 907-928, 1993. Juneja D., Iyengar S.S., Phoda V.V. (2009): Fuzzy Evaluation of Agent Based Semantic Match-Making Algorithm for Cyber Space. In International Journal of Semantic Computing Vol. 3(1), pp. 57–76. Kerschberg L., Chowdhury M., Damiano A., Jeong H., Mitchell S., Si J. and Smith S.(2004): Knowledge Sifter: Ontology-Driven Search over Heterogeneous Databases. In SDBM 2004, International Conference on Scientific and Statistical Database Management, (Santorini Island, Greece, 2004), IEEE. Kitamura Y. and Mizoguchi R.(1998): Functional Ontology for Functional Understanding, In Twelth International Workshop on Qualitative Reasoning (QR-98), Cape Cod, USA, May 26-29, AAAI Press, pp 77-87, 1998. Leite J.C.S.P. and Franco A.P.M.(1993): A Strategy for Conceptual Model Acquisition’. In proceedings of the IEEE International Symposium on Requirement Engineering, San Diego, CA, USA, IEEE Computer Society Press, pp 243-246. Maedche A. and Staab S. (2001): Ontology Learning for the Semantic Web. In IEEE Intelligent Systems, vol. 16(2), pp. 72-79, March 2001. Mika P. (2007): Ontologies are Us: A Unified Model of Social Networks and Semantics. In Journal of Web Semantics, vol. 5, pp. 5-15, 2007. Nodine M. and Fowler J. (2002): On the Impact of Ontological Commitment. In Proceedings of Second International Workshop on Ontologies in Agent-Systems, Bologna, Italy, pp 15-19, July 2002. Sanchez G.F., Garcia R.V., Bejar M.R., Breis T.F.J. (2009): An Ontology, Intelligent Agent-Based Framework for the Provision of Semantic Web Services. In Expert Systems with Applications, vol. 36, pp. 3167-3187. Shadbolt N., Hall W., Lee B.T. (2006): The Semantic Web Revisited. In IEEE Intelligent Systems, vol. 21(3), pp 96-101, 2006. Stuckenschmidit H., Timm J.I. (2002): Adapting Communication Vocabulary using Shared Ontologies. In Proceedings of Second International Workshop on Ontologies in Agent-Systems, Bologna, Italy, 15-19 July, 2002. Uschold M. and King M. (1995): Towards a methodology for Building Ontologies. In: Skuce, D. (Ed.) IJCAI’95 Workshop on Basic Ontological Issues in Knowledge Sharing. Montreal, Canada, pp. 6.1–6.10. Uschold M. (1996): Building Ontologies: Towards a Unified Methodology. In Proceedings of Expert System’96. 16th Annual conference of the British Computer Society, Cambridge (UK), Dec. 16-18, 1996. Vacharasintopchai T., Barry W., Wuwongse V., Nukulchai K.W. (2007): Semantic Web Services Framework for Computational Mechanics. In Journal of Computing in Civil Engineering (ASCE), vol. 21(2), pp 65-77, April 2007. Zheng T.H., Kang Y.B. and Kim G.H. (2008): An Ontology Based Approach to Learnable Focused Crawling. In Journal of Information Sciences, vol.178 (23), pp. 4512-4522, Dec.2008

ISSN: 0975-5462

1232