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Visual Knowledge Representation of Conceptual Semantic Networks Leyla Zhuhadar Western Kentucky Univeristy, [email protected]

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Visual Knowledge Representation of Conceptual Semantic Networks Leyla Zhuhadar Western Kentucky Univeristy, Leyla.Zhuhadar@Wku.Edu Western Kentucky University TopSCHOLAR® CIS Faculty Publications Computer Information Systems 2011 Visual knowledge representation of conceptual semantic networks Leyla Zhuhadar Western Kentucky Univeristy, [email protected] Olfa Nasraoui University of Louisville Robert Wyatt Western Kentucky University, [email protected] Rong Yang Western Kentucky University, [email protected] Follow this and additional works at: http://digitalcommons.wku.edu/cis_fac_pub Part of the Databases and Information Systems Commons Recommended Citation Leyla Zhuhadar, Olfa Nasaoui, Robert Wyatt, and Rong Yang. Visual Knowledge Representation of Conceptual Semantic Networks, Social Network Analysis and Mining Journal, 219-229, Volume 1, 2011. This Article is brought to you for free and open access by TopSCHOLAR®. It has been accepted for inclusion in CIS Faculty Publications by an authorized administrator of TopSCHOLAR®. For more information, please contact [email protected]. Manuscript Click here to download Manuscript: Visual_Knowledge_Representation_of_Conceptual_Semantic_Networks.psClick here to view linked References 1 2 3 4 5 6 7 8 9 Leyla Zhuhadar, Olfa Nasraoui 10 11 Knowledge Discovery and Web Mining Lab 12 Department of Computer Engineering and Computer Science 13 14 University of Louisville, KY 40292, USA. 15 16 17 18 Robert Wyatt 19 The Office of Distance Learning 20 21 Division of Extended Learning and Outreach 22 23 Western Kentucky University, KY 42101, USA. 24 25 26 27 28 29 30 31 32 33 Visual Knowledge Representation of 34 35 Conceptual Semantic Networks 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Springer 60 61 62 63 64 65 1 2 3 4 5 Abstract This article presents methods of using visual analysis to visually represent large amounts of massive, 6 dynamic, ambiguous data allocated in a repository of learning objects. These methods are based on the semantic 7 representation of these resources. We use a graphical model represented as a semantic graph. The formalization 8 of the semantic graph has been intuitively built to solve a real problem which is browsing and searching for 9 1 10 lectures in a vast repository of colleges/courses located at Western Kentucky University . This study combines 11 Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their prim- 2 12 itives in order to develop knowledge representation for the HyperManyMedia platform. Also, we argue that 13 the most important factor in building the semantic representation is defining the hierarchical structure and the 14 relationships among concepts and subconcepts. In addition, we investigate the association between concepts 15 using Concept Analysis to generate a lattice graph. Our domain is considered as a graph, which represents the 16 integrated ontology of the HyperManyMedia platform. This approach has been implemented and used by online 17 students at WKU 3. 18 19 20 21 1.1 Introduction 22 23 This study combines Formal Concept Analysis (FCA) with Semantic Factoring to construct and develop a 24 multilingual ontology. In a nutshell, it answers the following question: “How is it possible to visualize an 25 ontology graph which represents knowledge and reasoning of a massive, ambiguous, and vast set of documents 26 using minimum vocabulary?” The model is built upon a variety of principles that we adopt. First, we use Zipf’s 27 law: “The Principle of Least Effort [36]”, Zipf found a clearcut correlation between the number of words and 28 the frequency of their usage, it is presented as r f = c, where r is the word’s rank in a document and f its 29 · 30 frequency of occurrence. We rely on this significant finding by minimizing the amount of effort we put to create 31 the user ontology. The most frequent vocabulary that represent the corpus in our domain (E-learning) is used. 32 In this sense, we observe the most frequent keywords searched by users, this information is obtained from the 33 users’ logs. Our assumption is the following: “if we capture the most frequent words used by an online user, 34 then adding these words to the user ontology, the information retrieval model would provide the user with the 35 most relevant documents in both languages. Second, we use the concept of “ Collocation”, which proved to be 36 important in areas, such as machine translation and information retrieval [18]. Manning and Scutze [18] divided 37 the “Collocation Concept” into three categories: (1) compounds, such as “semantic web”, (2) phrasal verbs, 38 such as “turn on”, and (3) stock phrases, such as “ Introduction to Literature”. The third type is what we used 39 in constructing our ontology. Since our users (students) spent 80% of their time searching for topics related to 40 the following categories: (1) course name, (2) lecture name, and (3) professor name. Therefore, constructing an 41 ontology that consists of collocations (e.g., “Game Theory for Managers”) would increase the precision. Third, 42 we used personalization to decrease the ambiguity of semantic search. Each user activity on to the system 43 defines his/her area of interest (college/courses), therefore, a unique ontology is generated for each user. As a 44 consequence, the search terms used by a user are governed by his domain of interest (e.g., if a user is searching 45 for the keyword “History”, if he is enrolled in Mathematics, the system should retrieve course “History of 46 Mathematics”, but if he is enrolled in the college of History, the same keyword search will retrieve the course 47 48 “History of Civilization”). The effectiveness of our model comes from the synergy between all the previous 49 principles. 50 Before diving into the theory and the methodology of implementing the system, let us begin with some de- 51 scriptive definition of the system: HyperManyMedia is an information retrieval system that utilizes an ontology 52 based model and provides semantic information. This approach uses two different types of ontologies, a global 53 ontology model that represents the whole E-learning domain (content-based ontology), and a learner-based on- 54 tology model that represents the learner’s profile. The implementation of the ontology model is separate from 55 1 56 http//HyperManyMedia.wku.edu 57 2 HyperManyMedia: We proposed this term to refer to any educational material on the web (hyper) in a format that could be a 58 multimedia format (image, audio, video, podcast, vodcast) or a text format (HTML webpages, PHP webpages, PDF, PowerPoint) 3 59 http://www.wku.edu 60 61 62 63 64 65 1 2 3 4 5 the design of the information retrieval system. The architecture of the HyperManyMedia system can provide, 6 manage, and collect data that permits high levels of adaptability and relevance to the learner’s profile. To achieve 7 this objective, an approach for personalized search is implemented that takes advantage of the Semantic Web 8 standards (RDF and OWL) to represent the content and the user profiles. 9 10 The main focus of this paper is the visual representation of the ontology that allows learners to navigate 11 the system visually. The main objective of this research was to provide the user (learner) with a visual search 12 engine to summarize the entire domain (E-learning). This can be considered as a tool to help visualize concepts 13 and subconcepts. This visual exploration of documents enables users to have an overall view of the entire 14 repository, without even clicking on the resources and reading each document. When a user types a query on the 15 visual search engine, the visual search engine dynamically matches the query with the whole visual ontology 16 (concepts, subconcepts, etc). The visual search engine presents all the sectors (concepts/subconcepts) that share 17 the typed letters using different colors than the unmatched concepts. Therefore, the user can find what he/she is 18 looking for immediately. As the user adds more letters to his/her query, the number of matched sectors narrows 19 down to the most similar concepts in the ontology. 20 The primary contribution to the State of the Art made in this research is in the reuse of the domain ontology 21 to build visual search facet s, where the hierarchic ontology structure was converted into a lattice (graph) and 22 presented as nodes and edges, where the final representation of the graph is provided to users as sectors and 23 subsectors. 24 The rest of this paper is divided into the following sections: 25 Section 2 (Background and Related Work) : We give an overview of visual analytics, applications, and related 26 work. 27 Section 3 (Methodology) : This section presents the semantic domain structure and the representation of the 28 29 semantic domain. 30 Section 4 (Implementation) : This section presents the process of building the HyperManyMedia ontology, 31 then adding the ontology to the search engine. It ends with designing a visual ontology search engine. 32 Section 5 (Evaluation) : In this section, we test the usability of the visual search engine. 33 Section 6 (Conclusion) : In this section, we present the novelty of our research and our contribution. 34 35 36 37 1.2 Background and Related Work 38 39 In the section, we introduce the definition of visual analytics , then we define several visual applications, and 40 finally discuss related work and the significance of our visual analytic methods and techniques. 41 42 43 44 1.2.1 Visual Analytics 45 46 Everyday, data is produced with unprecedented rates in variety of fields, examples include scientific data, in- 47 48 ternet information, data management systems, business and marketing data, etc. Visual analytics is the bridge 49 between the human eyes and the machine, it facilitates the process of: a) discovering hidden knowledge, b) 50 summarizing data, c) representing data in a manner that the human cognitive system can perceive, d) helping 51 users find needed information as fast as possible, or e) allowing users to interact with huge amounts of data 52 easily and efficiently.
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