Ontology for Feature Based Selection of Web Development Tools

Ontology for Feature Based Selection of Web Development Tools Neelam Mukhtar, Sara Shahzad, Mohammad Abid Khan, Shah Nazir Department of Computer Science, University of Peshawar sameen_gul@yahoo.com, sara@upesh.edu.pk, abid_khan1961@yahoo.com, shahnzr@upesh.edu.pk Abstract- Organizations and individuals are mainly concerned to get opinions about different products, services and events for making suitable choices. The main contribution of this research is the design of an ontology that will help a user to select appropriate (popular) tools for web designing on the basis of certain characteristics and features of tools. The ontology is based on experts' opinions of software engineering practitioners and researchers (scholars). I. INTRODUCTION Ontologies are used to structure information. Ontologies can be reused and shared. A common vocabulary is provided by the ontologies that defines the meaning of different terms, attributes and the relationships between them [1]. The scope of ontologies are not limited to a hierarchical relationship among the concepts only, other forms of relationships like synonymy, or more complex relationships can also be described by ontologies in a better way. Ontologies provide a lot of semantic information. Concepts, relationships and entities are easily defined using ontologies [2]. While deciding tools for web designing, people select an appropriate tool on the basis of its features. A researcher thus finds other people s opinion about the tools features. Extracting opinions from text is called Opinion Mining (OM). OM aims to extract knowledge automatically from user generated contents [3]. An idea, vision, or approach that is based on emotion rather than reason is a sentiment. Therefore opinion mining is usually called sentiment analysis [4]. Searching of opinion thus combines retrieval of information and sentiment analysis [5]. Main aim is the identification of product features, upon which opinions are expressed by the customers in different reviews and feed backs. Features may be for example product subparts, product attributes and product functions [6]. The details about the different aspects of the product under consideration are focused. A feature basically means attributes or components of the product. Feature extraction is very important in opinion mining as customers usually do not give opinions on the product as a whole but they give opinions, according to its individual features [7]. In feature based opinion mining the features of an object or a product that are expressed through opinions are extracted and then summarized [8]. In this research we have presented the design of ontology for web tool selection based on feature extraction in opinion mining. The process of analysis of information collected is also presented in the paper. The proposed ontology will help in the selection of appropriate tool (according to user requirements) on the basis of certain features (about which the opinions were collected from the young software engineers). This paper is arranged in the following way. Related work is presented in section 2. In Section 3, data collection methodology that is used is discussed. In section 4 the analysis and results are discussed. Ontology for web tool selection is presented in section 5. Conclusion is provided in section 6. II. RELATED WORK Ontologies are used by different researchers in feature based opinion mining. Cadilhac et al. [8] used domain ontologies for structuring and extracting object features. Aciar et al. [9] utilized ontology while proposing a feature extraction method for opinion mining. Their method semantically worked smoothly but difficulties arise while maintaining the ontology in case of expansion of data. Lazhar and Yamina [2] focused on domain ontologies, particularly for structuring of features, extraction of explicit and implicit features and for producing summaries. Pe nalver-mart ınez et al. [1] proposed a methodology for feature based opinion mining that used ontologies for feature selection and provided a vector analysis based method for sentiment analysis. Sureka et al. [10] presented an algorithm for automatically building an ontology that is domain specific. This ontology can be used as a lexical resource while performing sentiment analysis (which is target specific) in specific time. Phyu Shein [11] used the combination approach for sentiment classification. In this approach, domain ontology and supervised learning technique is used for extraction of features and opinions from reviews or comments. Somprasertsri [12] used ontology to distinguish between different features of the product. Product ontology is designed manually that is used to group

terminologies by using patterns for regular expression. Table 2.User views about characteristics of different tools. III. DATA COLLECTION FOR WEB TOOL SELECTION ONTOLOGY To get an idea about the tools, commonly used for web designing and development, a structured questionnaire (with closed ended questions) was designed. The list of tools, available in the market, was placed different categories under the following headings: a) front end tools b) back end tools c) client side scripting d) server side scripting e) web Server. After that a list of characteristics of the tools, according to ISO 9126 [13] was provided. In ISO 9126 quality model, different characteristics (Table 2) are categorized under the following main headings: a) usability b) portability c) functionality d) efficiency e) reliability f) maintainability. The questionnaire was filled by 20 young software engineers related to an academic institution. These software engineers were domain experts as they had much expertise about the given tools. The data taken from questionnaires in the form of selected tools and their characteristics was carefully observed and recorded. The frequency for all the tools and characteristics (being selected) was obtained as shown in Table 1 and Table 2. Table 1. User views about usage of different tools. IV. ANALYSIS AND RESULTS Tools and their characteristics as selected by the respondents with a frequency of at least eight (out of twenty) were considered (as popular ones) while the remaining (less popular tools and characteristics) were discarded. In the next phase, the tools and the characteristics were arranged in a contingency table to check each popular (commonly used) tool with respect to its different characteristics (commonly required). Chi- square test was applied to check whether the two criteria of classification are independent or they are associated with each other (people select tools on the basis of their characteristics or they select randomly). The result (in the form of P-value less than 0.05) indicated that there is association between the two criteria. Charts were drawn to see which characteristics are highly possessed by particular tools.

Fig. 1. Comparison of characteristics of Dream weaver and Adobe Photoshop Fig. 4. Comparison of characteristics of PHP and ASP Fig. 2. Comparison of characteristics of My SQL Fig. 5. Comparison of characteristics of Apache and WAMP After carefully observing Fig.1 to 5, it is noted that there are few characteristics that are popular and are mostly required by the tools that are mentioned in these figures. Table 3 provides a quick overview of these figures. Table 3. Tools with mostly required characteristics Fig. 3. Comparison of characteristics of Java Script and HTML+CSS Mostly required characteristics were then considered. Efforts were made to chalk out the popular tools that are having those characteristics.

Fig. 6. Comparison of Freely availability and understandability Fig. 9. Comparison of Resource behavior and flexibility Fig. 7. Comparison of Adaptability and Install ability Fig. 10.. Comparison of Cross platform and Recoverable. Fig. 8. Comparison of Suitability and Accuracy Fig. 11. Comparison of changeability and stability. Analysis of the charts as seen in Fig. 6 to 11, helped in identifying the popular tools and is having the characteristics that are mostly required. Table 4 provides a quick overview of these figures.

Table 4. Tools with frequently required characteristics. Both Table 3 and Table 4 are used in designing specific ontology (see Fig. 13). V. ONTOLOGY FOR WEB DEVELOPMENT TOOL SELECTION On the basis of these observations first a general ontology (Fig. 12) is designed in Protégé 2000 [15] that will help the user to find commonly used tools for web designing on the basis of commonly required characteristics. Fig.12. General ontology to select the appropriate web designing tools on the basis of certain characteristics that are commonly required. As already discussed ontology is a precise description of concepts, properties, attributes of concepts and certain constraints on properties. Ontology given in Fig. 12 is designed in Protégé 2000 which is a graphical tool for ontologydevelopment that supports a rich knowledge model. The ontology is designed by utilizing the data from Table 1 and Table 2 (as already discussed in section IV, 8 out of 20 frequencies were considered). A main class "commonly used web designing tool" with two sub classes "mostly required characteristics" and "mostly required tools" was formed. Tools with high frequency are placed under the "mostly required tools" class. Similarly the high frequency characteristics were placed under the headings. The headings were further categorized. These desirable characteristics were further classified according to certain tools that possess these characteristics. From Table 3 and Table 4, most Popular tools (on the basis of data collected from the questionnaire) i.e. Front End (Dream Weaver), Back End (My SQL), Client Side Scripting (Java Script), Server Side Scripting (PHP), Web Server (Apache, WAMP) and most popular characteristics (Freely available, Installable, Suitable, Flexible, Cross platform, Recoverable, Changeable) were arranged and chi square test was applied. In all the cases P-value was less than.05 that showed that there is association between these tools and characteristics. Thus it may be concluded that if the above mentioned characteristics are required then these mentioned

tools may be selected. Specific Ontology (Fig. 13) is thus designed in Protégé 2000. Fig. 13. Specific Ontology for most popular tools on the basis of most popular characteristics on the basis of collected data. Ontology in Fig. 13 is designed by using the data in Table 3 and Table 4. Specifically, ontology is designed for the most popular characteristics that are usually required by the most popular tools that possess these characteristics. VI. CONCLUSION Tool selection is a very important design decision for software engineers. Expert opinion of practitioners is definitely a big help and is highly appreciated by young software engineers. Access to experts may not always be easy. In this case an ontology based on the expert opinion is the better option, compare to selecting a tool blindly. The idea of presenting this ontological model for tool selection is twofold. One is that it provides a benefit to the young web developers in deciding over the tools for developing websites. Secondly, it provides an insight on how to design ontology for feature extraction in opinion mining. This is a model which can be taken further to an advanced level by including more tools and more characteristics and collection the opinions of experts in the field. References [1] I. Peñalver-Martínez, R. Valencia-García, and F. García-Sánchez, "Ontology-Guided Approach to Feature-Based Opinion Mining," in Natural Language Processing and Information Systems. vol. 6716: Springer Berlin Heidelberg, pp. 193-200. [2] F. Lazhar and T. G. Yamina, "Identification of Opinions in Arabic Texts Using Ontologies," Information Technology & Software Engineering, pp. 1-3, 2012. [3] A. Harb, M. Plantié, G. Dray, M. Roche, F. Trousset, and P. Poncelet, "Web opinion mining: how to extract opinions from blogs?," in Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology, Cergy-Pontoise, France, 2008. [4] K. Khan, B. B.Baharudin, A. Khan, and Fazal-e- Malik, "Mining opinion from text documents: A survey," in 2009 3rd IEEE International Conference on Digital Ecosystems and Technologie, 2009, pp. 217-222. [5] B. Liu, "Sentiment Analysis and Subjectivity " in Handbook of Natural Language Processing, N. I. a. F. J. Damerau), Ed., 2010. [6] Y. W. Lo and V. Potdar, "A review of opinion mining and sentiment classification framework in social networks," in 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, 2009, pp. 396-401. [7] H. Jeong, D. Shin, and J. Choi, "FEROM: Feature Extraction and Refinement for Opinion Mining," ETRI, vol. 33, pp. 720-730., 2011. [8] A. Cadilhac, F. Benamara, and N. Aussenac- Gilles, "Ontolexical resources for feature based opinion mining : a case-study," in Proceedings of the 6th Workshop on Ontologies and Lexical Resources (Ontolex 2010) Beijing, 2010, pp. 77 86. [9] S. Aciar, D. Zhang, S. Simoff, and J. Debenham, "Informed Recommender: Basing Recommendations on Consumer Product Reviews," Intelligent Systems, IEEE, vol. 22, pp. 39-47, 2007. [10] A. Sureka, V. Goyal, D. Correa, and A. Mondal, "Generating Domain-Specific Ontology from Common-Sense Semantic Network for Target- Specific sentiment Analysis," in Fifth International Conference of the Global WordNet Association (GWC), 2010, 2010. [11] K. P. P. Shein, "Ontology based combined approach for sentiment classification," in Proceedings of the 3rd International Conference on Communications and information technology Vouliagmeni, Athens, Greece: World Scientific and Engineering Academy and Society (WSEAS), 2009. [12] G. Somprasertsri and P. Lalitrojwong, "Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization," Journal of Universal Computer Science, vol. 16, pp. 938-955, 2010. [13] I. S. Organization, "Software Engineering Product 2001. [14] http://protege.stanford.edu/ Quality," Parts 1-4, ISO/IEC9126,