AWERProcedia Information Technology & Computer Science

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1 AWERProcedia Information Technology & Computer Science Vol 04 (2013) rd World Conference on Innovation and Computer Sciences 2013 Searching under uncertain conditions Bogdan Walek *, Department of Informatics and Computers, University of Ostrava, 30. dubna 22, Ostrava , Czech Republic. Pavel Smolka, Department of Informatics and Computers, University of Ostrava, 30. dubna 22, Ostrava , Czech Republic. Jiri Bartos, Department of Informatics and Computers, University of Ostrava, 30. dubna 22, Ostrava , Czech Republic. Cyril Klimes, Department of Informatics and Computers, University of Ostrava, 30. dubna 22, Ostrava , Czech Republic. Suggested Citation: Walek B., Smolka P, Bartos j. & Klimes. Searching under uncertain conditions. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp Available from: Received December 06, 2012; revised February 28, 2013; accepted March 17, Selection and peer review under responsibility of Prof. Dr. Fahrettin Sadıkoglu, Near East University Academic World Education & Research Center. All rights reserved. Abstract In present days, there are a lot of structured data stored in information systems. On the other hand, information systems store huge amount of not clearly structured data. For not clearly structured data, the full-text search algorithms have some limits. Wide range of information sources, together with vaguely defined structure of resources, is the reasons for which this area can be claimed to be the search under uncertainty. For this reasons, the methodology for searching information sources under uncertain conditions is proposed. The paper identifies the fundamental sources of uncertainty in this problem domain. Subsequently, a model representing the search methodology over resources with static, dynamic and dynamic character with authorization is proposed. Then, the expert system with knowledge base composed of IF-THEN rules, is proposed. The model deals with uncertainties on several levels, so the paper is also identifying them. Keywords: fuzzy, uncertainty, search, information source, expert system, information system, data resources; *ADDRESSES FOR CORRESPONDANCE: Bogdan Walek, Department of Informatics and Computers, University of Ostrava, 30. dubna 22, Ostrava , Czech Republic, Address: bogdanwalek@gmail.com

2 1. Introduction Given the scope of information sources, search algorithms are one of the key elements of the current information system. With the growing scope of the information system is also the creation of data repositories that consist of structured data in the form of database structures and increasingly data that are not clearly structured, but whose value is yet noticeable. Character of the above sources of information limits search algorithm to full-text tools. Wide range of information sources, together with vaguely defined structure of resources, is the reasons for which this area can be claimed to be the search under uncertainty. 2. Problem formulation To search and analyse relevant data belonging to customer, product or project we can use full-text. When working with information resources in addition to extensive static storages, there are also resources that are dynamically generated and based on interaction with a specific user interface for this resource. The components that belong to this set of resources are ideally resources that are available to the user only after successful login of the user. According to a study by BrightPlanet [1] is the volume of this information five times higher than the volume of indexed information. The basic objective of each search process is to narrow the number offered addresses (or links) to resources based on keywords specified by user. The user then receives a set of available data, which applies to the final selection of addresses of information sources based on an assessment of its relevance for a search. From this perspective, every search engine solves the following problem areas: Knowing the content of all sources both structured and unstructured data. Being able to address these sources together with the user specified key words, with emphasis on the auxiliary apparatus of classification of these words. Dealing with uncertainties when entering keywords in the form of typos, bad spelling and mainly wrong choice of keywords. Returning a set of records and determining the relevance of matching addresses to individual sources and based on this relevance evaluation rearranging the target set. The first and the second addresses point is today provided by processes that provide download of the source (e.g. document), classification of its contents and definition of index. Index is such data structure that identifies each keyword and connects these words with addresses of relevant information sources. The evaluation of static information source is another process that takes place during indexing phase. Algorithm for evaluation and design of data structure that stores this assessment is generally dealing with the following range of parameters: Meta-information of the files. Relevance of resources based on network addresses and owners of resources. Technical quality of the information resource. Commercial validity of resource. Internal evaluation systems such as RANK addresses. 629

3 3. Problem solution As stated above it is obvious that the current search process must be very complex and involves several levels of sub-searches. Only one of these levels is search of resources. Comprehensive searching is possible to formalize by the following formula: FR = FRs + FRdf+ FRda, where: FR sum of all resources that were found FRs sum of resources of static nature that were found FRdf sum of dynamically generated resources that were found and are freely available FRda sum of dynamically generated resources that were found and are available after successful authentication Resource - the result of address that are linked to the actual data 3.1. Identification of the uncertainties in searching process There can be found several areas that are burdened by vagueness in the search processes. The main reason for the occurrence of vagueness in this area is the need to work with natural human language, especially with his grammar, although the grammar is often very clear and cover by logical rules. However, the grammar of natural language is not the only cause of the occurrence of uncertainty (in addition to coping with this kind of uncertainty is done typically by tools of NLP). In terms of the process of data indexing, the first occurrence of uncertainty can be found in indexing of addresses of resources. Because of the wide range of tools that acquire these resources, there is another potential source of vagueness during indexing. For these sources it is quite difficult to determine the proper structure and focus on key areas, yet the information value of these problematic resources is not so null. The second area where you the presence of uncertainty can be found is the area of classification (evaluation) of resources in terms of its relevance. The third and for us the most interesting area is the behavior of end users, respectively his inability to clearly and definitively specify keywords that are related to the search brings a great portion of uncertain information. 630

4 3.2. Methodology proposal Figure 1. Diagram of proposed methodology Proposed methodology itself is based on a general model for decision-making process. This model is described in more detail here [3]. The general model was also published in [2], [4]. Process M1 M1: S x T x L Process of completion performs selecting of relevant data. Static resources are captured by the crawler on the basis of an existing source address S in time T and in the scope of trusted sources L [5]. Process M2 M2: M1 x P M2 is essentially the process of creating search engine index. Following the standard approaches the mapping of keywords to address sources is stored in a structure. An important feature of this phase is the evaluation of the relevance of information sources using the rules from the P set [2]. The rules of P are determining the credibility of the source (credibility c) on the grounds of three basic input variables: f frequency of key words in the source defined by the number from interval in the range <0.10> with discretization 200, p importance of place of occurrence, r technological relevance of the resource. 631

5 A set of IF - THEN rules for this area has been designed and tested using LFLC (Linguistic Fuzzy Logical Controller) developed by the Institute for Research and Applications of Fuzzy Modeling University of Ostrava [6]. Figure 2. IF-THEN for the credibility assessment of the resource The basic set of values that are being used for linguistic evaluation of inputs and outputs is shown below: sm small, me medium, bi big For these elementary set following linguistic modificators are used: ro roughly, vr very roughly, ml more or less, ve very The rules show in Figure 2 can be represented by following IF-THEN rules: IF frequency is small AND importance of place of occurrence is small AND rank is small THEN credibility is small IF frequency is big AND importance of place of occurrence is small AND rank is big THEN credibility is big Process M3 M3:M2xF Transformation from M2 to M3 is realized according to equation M2 x F -> M3 [5], where F is a set of all degreases of existence. The existence of all degreases of existence is the connection between records of indexes and outputs that are dynamically generated together with common user input that manifests when key words is being searched. The goal of this methodology is to propose fuzzy approach to selecting the keywords. The proposed approach can be shown as searching using keyword and rank of the keyword that is represented by linguistic variable. The set of ranks consists of following values: im (important) pa (partly) ma (marginally) Process M4 M4 process encapsulates the visualization of results. The need correlate set of results that exited the M3 process is the essential part goal of process M4. The very own sorting of results is done in accordance to the already gained rank and/or other commercial parameters (e.g. paid links etc.). 632

6 4. Conclusion The result of this paper is a comprehensive model representing the search methodology using combined information sources. The model integrates the work with the uncertainties that were identified in the analytical part. For the construction of knowledge base in the process of evaluation of static sources LFLC tool was used. Increased involvement of this tool in the examined problem domain is also a topic of further research direction. Acknowledgements The presented topic is also a part of the internal grant SGS10/PřF/2012, called Fuzzy-modeling tools for analysis and design of information systems, at the Department of Informatics and Computers, University of Ostrava. References Union, International Telecommunications. Internet World Stat. Internet World Stat Bartoš J., Walek B., Smolka P., Procházka J., Klimeš C., Fuzzy modeling tools for information system testing, 17th International Conference on Soft Computing Mendel 2011, Brno 2011, pp Klimeš C., Model of adaptation under indeterminacy, Kybernetika, Vol.47, No.3, 2011, pp Walek B., Klimes, C., Fuzzy tool for conceptual modeling under uncertainty, Fourth International Conference on Machine Vision, Bellingham, Walek B., Klimeš C., Conceptual modeling under uncertainty, 2nd World Conference on Information Technology, Antalya, 2011, to be published. Habiballa H., Novák V., Dvořák A., Pavliska V., Using software package LFLC 2000, 2nd International Conference Aplimat 2003, Bratislava, 2003, pp