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Intelligent Search Engine to a Semantic Knowledge Retrieval in the Digital Repositories

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Intelligent Search Engine to a Semantic Knowledge Retrieval in the Digital Repositories International Journal on Advances in Intelligent Systems, vol 8 no 1 & 2, year 2015, http://www.iariajournals.org/intelligent_systems/ 67 Intelligent Search Engine to a Semantic Knowledge Retrieval in the Digital Repositories Antonio Martín Department of Electronic Technology Higher Technical School of Computer Engineering Sevilla, Spain [email protected] Abstract— Currently, an enormous quantity of heterogeneous necessary to develop new intelligent and semantic models and distributed information is stored in the current digital that offer more possibilities [1]. libraries. This data abundance has made the task of locating There are researchers and works in related fields, which relevant knowledge more complex. Such complexity drives the include ontology retrieval methods. The study [2] presents a need for intelligent systems for searching and for knowledge system, which uses an ontology query model to analyze the retrieval. Access to these collections poses a serious challenge. usefulness of ontologies in effectively performing document The present search techniques based on manually annotated searches. This work proposes an algorithm to refine metadata and linear replay of material selected by the user do ontologies for information retrieval tasks with preliminary not scale effectively or efficiently to large collections. The positive results. [3] uses a medical ontology to improve a Artificial Intelligence and Semantic Web provide a common Multimodal Information Retrieval System by expanding the framework that allows knowledge to be shared and reused. In this paper, we propose a comprehensive approach for user's query with medical terms. The study [4] combines discovering information objects in large digital collections. The swarm intelligence and Web Services to transform a process is based on analysis of recorded semantic metadata in conventional library system into an intelligent library system those objects and the application of expert system technologies. with high integrity, usability, correctness, and reliability We suggest a conceptual architecture for a semantic and software for readers. The research [5] proposes meta- intelligent search engine. We concentrate on the critical issue concepts with which the ontology developers describe the of metadata/ontology-based search. More specifically, the domain concepts of parts libraries. The meta-concepts have objective is investigated from a search perspective possible explicit ontological semantics, so that they help to identify intelligent infrastructures form constructing decentralized domain concepts consistently and structure them digital libraries where no global schema exists. We have used systematically. The study [6] presents a formulation and case Case Based-Reasoning methodology to develop a prototype for studies of the conditions for patenting content-based retrieval supporting efficient retrieval knowledge from digital library of processes in digital libraries, especially in image libraries. Seville University. The work suggests a conceptual architecture The paper [7] focuses on methods for evaluating different for a semantic and intelligent search engine and we also have symbolic music matching strategies, and describes a series of developed a prototype and tested it for supporting efficient experiments that compare and contrast results obtained using retrieval knowledge from digital libraries. three dominant paradigms. The research [8] proposes organizational memory architecture and annotation and Keywords-Ontology; Semantic Web; Retrieval; Case-based Reasoning; Digital Library; Knowledge Management. retrieval information strategies. This technique is based on domain ontologies that take in account complex words to I. INTRODUCTION retrieve information through natural language queries. There are a lot of researches on applying these new A Digital Library (DL) enables users to interact technologies into current information retrieval systems, but effectively with information distributed across a network. no research addresses Artificial Intelligence (AI) and These network information systems support search and semantic issues from the whole life cycle and architecture display of items from organized collections. In the historical point of view [9]. Although search engines have developed evolution of digital libraries the mechanisms for retrieval of increasingly effective, information overload obstructs precise scientific literature have been particularly important. searches. Our work differs from related projects in that we Traditional search engines treated the information as an build ontology-based contextual profiles and we introduce an ordinary database that manages the contents and positions. approaches used metadata-based in ontology search and The result generated by the current search engines is a list of expert system technologies [10]. We presented an intelligent Web addresses that contain or treat the pattern. The useful approach for optimize a search engine in a specific domain. information buried under the useless information cannot be This study improves the efficiency methods to search a discovered. It is disconcerting for the end user. Thus, distributed data space like DL. The objective has focused on sometimes it takes a long time to search for needed creating technologically complex environments digital information. Although search engines have developed repositories domain. It incorporates Semantic Web and AI increasingly effective, information overload obstructs precise technologies to enable not only precise location of public searches. Despite large investments and efforts have been resources but also the automatic or semi-automatic learning made, there are still a lot of unsolved problems. Thus, it is [11]. 2015, © Copyright by authors, Published under agreement with IARIA - www.iaria.org International Journal on Advances in Intelligent Systems, vol 8 no 1 & 2, year 2015, http://www.iariajournals.org/intelligent_systems/ 68 Our approach for realizing content-based search and automatically based on some higher level of understanding retrieval information implies the application of the Case- are required. We make an effort in this direction by Based Reasoning (CBR) technology [12]. Thus, our investigating techniques that attempt to utilize ontologies to objective here is to contribute to a better knowledge retrieval improve effectiveness in information retrieval. Thus, in DL field. This paper describes semantic interoperability ontologies are seen as key enablers for the Semantic Web. problems and presents an intelligent architecture to address We have proposed a method to efficiently search for the them, called OntoSDL. Obviously, our system is a prototype target information on a digital repository network with but, nevertheless, it gives a good picture of the on-going multiple independent information sources [16]. The use of activities in this new and important field. We concentrate on AI and ontologies as a knowledge representation formalism the critical issue of metadata/ontology-based search and offers many advantages in information retrieval [17]. In our expert system technologies. More specifically, the objective is investigated from a search perspective possible intelligent work, we analysed the relationship between both factors infrastructures form constructing decentralized public ontologies and expert systems. repositories where no global schema exists. We focus our discussion on case indexing and retrieval The contributions are divided into next sections. In the strategies and provide a perception of the technical aspects first section, short descriptions of important aspects in DL of the application. For this reason, we are improving domain, the research problems and current work in it are representation by incorporating more metadata within the reported. Then, we summarize its main components and information representation [18]. We discuss an opportunity describe how can interact AI and Semantic Web to improve and challenge in this domain with a specific view of the search engine. Third section focuses on the ontology intelligent information processing that takes into account the design process and provides a general overview about our semantics of the knowledge items. In this paper, we study prototype architecture. Next, we study the CBR framework architecture of the search layer in this particular dominium, jColibri and its features for implementing the reasoning a web-based catalogue for the University of Seville. The process over ontologies [13]. Finally, we present conclusions hypothesis is that with a case-based reasoning expert system of our ongoing work on the adaptation of the framework and and by incorporating limited semantic knowledge, it is we outline future works. possible to improve the effectiveness of an information retrieval system [19]. More specifically, the objectives are II. MOTIVATIION AND REQUIREMENTS decomposed into: In the historical evolution of DL, the mechanisms for • Explore and understand the requirements for retrieval information and knowledge have been particularly rendering semantic search in an institutional important. These network information systems support repository. search and display of items from organized collections. • Investigate how semantic technologies can be used Reuse this knowledge is an important area in this domain. to provide additional semantic properties from The Semantic Web provides a common framework that existing resources. allows knowledge to be shared and reused
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