The author has also granted permission to the University to retain or make a digital copy for similar use and for the purpose of digitally preserving the work. The feasibility of the proposed algorithm is proven by running multiple experimental tests on the same set of questions (414 questions) that were performed on the ontology earlier, then compared with the ontology itself and the official chatbot that was designed by the Government's smart chatbot of Dubai (Rashid).
CHAPTER 1: INTRODUCTION
- Background
- Problem Definition
- Research Objectives
- Research Motivation
- Research Questions
- Dissertation Structure
- Summary
The main purpose of this question is to answer the main objective “the feasibility of building an Arabic chatbot for e-government services based on a previous one. The main purpose of this question is to answer the main objective "the feasibility of building an Arabic chatbot for e-government services".
CHAPTER 2: LITERATURE REVIEW
Historical Overview
For heuristic communication rules, the program uses an XML schema called AIML (Artificial Intelligence Markup Language). A few years later, Alex from Amazon came up with an interactive AI platform that can create a personalized living environment by linking other home appliances to its centralized system (Hoy.
The Turing Test
Chatbot Definition
Human-machine interaction is a system that uses natural language to facilitate contact between users and computers. Natural language technologies are used by chatbots to engage users in a wide variety of applications through text searching for information and task-based dialogues (Lester, Branting & Mott 2004).
Types of Chatbot
- Embodied Chatbots
- Linguistic Chatbots
Human-machine conversation is a technology that supports communication using natural language between users and computers. ECs enhance the human nature of communicating with humans through natural language dialogues to answer questions and perform tasks for the user.
Chatbots Architecture
- Components
- Dataset Models
- Approaches
There are two dataset models to represent the knowledge source in chatbots, the generative-based model and the retrieval-based model (AlHumoud, Al Wazrah & Aldamegh 2018). The simplest patterns were used by old chatbots such as ELIZA and PC Therapist.
Challenges in Arabic Language
- Many Dialects in Arabic Language
- Morphology of Arabic Language
- Ambiguity in Arabic Language
- Grammars of Arabic Language
- Use of Non-Arabic Words in Arabic Language Dialects
Morphology in linguistics can be defined as the study of the internal word structure (El Kholy & Habash 2010). For example, the word "ررق" pronounced as "QARA" has the broad lexical meaning of. Another example is "ة ررق" meaning "reading" is pronounced as "QERA'AH" while the word "رررر َررررم" meaning "desk" is pronounced as "MIQRA".
To overcome the ambiguity of the meaning of the word from the context of the sentence, several disambiguation techniques have been devised, such as: (Li, Szpakowicz & Matwin 1995), (Ide, Erjavec and. For example, the word "تنرتنا" means "internet" and is often used to express online network.
Semantic Similarity Measurement
- Linguistic Challenges
- Technical Challenges
- Conceptual Challenges
The first one is Sentence Similarity based on Semantic Networks and Corpus Statistics (SSSN) which relies on semantic networks such as WordNet, by calculating each word similarity between two sentences, and then calculating the semantic similarity of the whole sentence; which can be performed by calculating the similarity between each pair of words. The rows of the matrix represent the unique words available in each paragraph, and the columns represent each. Incompleteness: According to Fellbaum (2020), the total number of Arabic words in AWN database is (23,481), which represents less than 10% of the Arabic root words.
The sentence similarity measure does not take into account these negated words, which can completely change the meaning of the sentence. The compound nature of Arabic words: due to the affixes added to the Arabic words, this increases the richness of semantic detail.
Evaluation of Chatbots
Ease of use: to measure the ease with which the user can obtain the desired information. Behavior prediction: to determine the degree to which the chatbot is able to meet the user's expectations. A general framework was presented by Walker et al. 1998) named PARADISE for evaluation in order to overcome these limitations and compare the performance of spoken chatbots.
The goal of the comparison is to address the interaction differences between both chatbots by measuring user satisfaction. Phase two: evaluating the natural language scripting used to script the semantic chatbot.
Arabic Chatbots Survey
This was evaluated by analyzing various aspects of the interaction, such as usability and the inherent essence of the dialogue. Shawar and Atwell's (2004b) Quran chatbot was one of the first studies on the use of an Arabic chatbot in a collected survey. The use of a default file is to ensure that when a user asks any question, a mapping reference will be found that points to the initial form of the question that is stored in the knowledge base.
However, the results of the manual analysis on the logs in the improved version proved to be better as it could successfully handle 82% of the utterances. Abdullah uses visual and auditory effects to interact with students so that it can determine the student's knowledge level and thus drive the conversation accordingly.
Applications of Chatbots
- Education
- Healthcare
- Customer Service
- Governments / Public Sectors
Due to some problems in the traditional teaching model for ASD children because teachers are not able to meet the needs of each individual, this model of teaching enables children to carry out practices independently and improve their skills by kinesthetic in addition to the auditory and visual effects. Another example is (Chip), a chatbot that has proven to the city of Los Angeles that it can provide answers to potential police department recruits. In the Republic of Latvia, the Register of Enterprises in the Ministry of Justice has introduced a chatbot called (UNA) which is available 24/7 on their website and Facebook messenger.
(GovBot) is another example of government that was implemented in the city of Bonn in Germany, the goal of GovBot is to help citizens with their administrative services. GovBot is currently being used in the administration of the city of Bonn, North-Rhein Westphalia and the city of
Summary
CHAPTER 3: RESEARCH METHODOLOGY
Introduction
The proposed work adopted the Database Knowledge Discovery (KDD) methodology from Azevedo and Santos (2008). This is shown through (Figure 3.1), which combines all the stages for building the knowledge base resources and implementing the chatbot. Briefly, the main phases covered are: Ontology Knowledge Base Extraction, Chatbot Knowledge Base Construction, and Chatbot Response Algorithm Development.
Extract the Ontology Knowledge Base
- Data Collection
- Data Preparation
The current ontology data source is available at (تاراملا.يبد) which presents 504 services distributed among 32 government units. On the other hand, the chatbot's knowledge base was built based on Artificial Intelligence Markup Language (AIML) files. Before building the chatbot's knowledge base, data validation is required to ensure that the updated data is correct.
It also ensures that the annotators clearly and correctly understand the required validation process and ensures a high level of confidence. This result indicates that domain experts could perceive a correct and common understanding of the services and provides a high level of confidence in the validation process.
Construct Chatbot Knowledge Base
- Hello AIML File
- Entities AIML File
- Services AIML File
- Questions AIML File
- Profile AIML File
- Stem AIML Files
The purpose of this file is to cover the initiation of the conversation between the user and the chatbot by welcoming the user and introducing them to the chatbot. The purpose of this file is to capture input that is not defined in the knowledge base. Therefore, as shown in Table 2, another 55 question types have been added and assigned to the related attribute, generating 27,720 new categories to include in this AIML file and enrich the knowledge base, as shown in (Figure 3.13 ).
The query shown in the examples above is repeated again with different forms, as in the examples below. An example of a chatbot's response to this AIML file is shown in section 3.2.4 (Figure 3.19).
Develop Chatbot Response Algorithm
- Stage 1: Pattern Matching (PM)
- Stage 2: Natural Language Processing (NLP)
- Stage 3: Similarity Measurement (SM)
In other words, if the query entered by the user matches any
The results will be displayed to the user to select the preferred one for his query, as shown in (Figure 3.20). Once the user has selected any of these three options, it will retrieve the associated as a final response as shown in (Figure 3.21).
Evaluate Results
Number of questions that are incorrect and not answered by the algorithm Table 3.5: Confusion Matrix.
Summary
CHAPTER 4: EXPERIMENTS AND RESULTS DISCUSSION
- Introduction
- Experiment Attempt 1
- Stage 1: Pattern Matching
- Stage 2: Natural Language Processing
- Stage 3: Similarity Measurement
- Combined Stages Results
- Experiment Attempt 2
- Stage 3: Similarity Measurement
- Combined Stages Results
- Experiment Attempt 3
- Stage 3: Similarity Measurement
- Combined Stages Results
- Results Comparison
- Summary
As explained in section (3.4), the proposed algorithm is a combination of 3 stages (PM, NLP and SM). The results of performing the second experiment attempt showed the same results for phase 1 and 2. The results of the execution of the third experiment attempt also showed the same results for phase 1 and 2.
However, the Keywords - Synonyms Dataset Mapping has been added to the chatbot application resulting in a significant improvement. The final results of the 3rd experiment were compared with the results of Albarghothi (2018) and a chatbot for public services (Rashid) from Smart Dubai.
CHAPTER 5: CONCLUSION AND FUTURE WORKS
Introduction
Research Questions Answers
- RQ2: Is it possible to build an Arabic Chatbot System that can respond to
- RQ3: Is it possible to build an Arabic Chatbot System that can respond to
RQ3: Is it possible to retrieve accurate response using Chatbot better than the formal Chatbot of Smart Dubai Government. It was possible to outperform the accuracy of the chatbot designed by Smart Dubai Government (Rashid) by achieving 96% compared to 19% accuracy for Rashid, which proves a higher capability of the proposed algorithm compared to another chatbot with the same concept.
Conclusion
It is possible to surpass the accuracy of the chatbot designed by Smart Dubai Government (Rashid) by achieving 96% compared to 19% accuracy for Rashid, which demonstrates the superior performance of the proposed algorithm compared to another chatbot with the same concept. which confirms what Razmerita et al. 2004) argued about the difficulties in chatbot development. In addition to the challenges related to the Arabic language, which also increased this problem. Several attempts have been made to improve the proposed algorithm, as it was initially observed that adopting a single approach to build a conversational agent produces poor results that cannot be released for commercial use.
The fundamental contribution of this dissertation is the construction of a unified chatbot that can respond to all services provided by the Dubai government using the Arabic language. In addition, the feasibility of extracting a knowledge base from an ontology and producing an independent knowledge base that can be used by various applications has been demonstrated.
Future Works
Proceedings - 1st International Conference on Arabic Computational Linguistics: Advances in Arabic Computational Linguistics, ACLing 2015, (juli), s. EACL 2006 - 11th Conference of the European Chapter of Association for Computational Linguistics, Proceedings of the Conference, pp. den 12. internationale konference om informationsbehandling og styring af usikkerhed i vidensbaserede systemer (IPMU '08), s.
Proceedings of the ACL-02 Workshop on Deconstructing Word Meaning: Recent Successes and Future Directions-Volume 8. Available at: https://www.london.gov.uk/city-hall-blog/Making-our- first-chat-bot.
