BS2 Final Report. Ontonosis. Group Members. Supalak Sirikutjatuporn Punnakhun Bungate Advisor: Dr. Boontawee Suntisrivaraporn

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1 BS2 Final Report Group Members Supalak Sirikutjatuporn Punnakhun Bungate Advisor: Dr. Boontawee Suntisrivaraporn School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University Semester 2, Academic Year May 2015

2 Table of Contents 1 Introduction Background Objectives Outputs and Expected Benefits Outputs Benefits Literature Review Methodology Approach Tools and Techniques Project Schedule Technical Description References... 19

3 Statement of Contribution By submitting this document, all students in the group agree that their contribution in the project so far, including the preparation of this document, is as follows: Supalak Sirikutjatuporn ( ) 50% Punnakhun Bungate ( ) 50%

4 1 Introduction Presently, there are a lot of websites uses for search something, for example, Google.com which user can search everything in the world as long as you connected to the internet. There are also a lot of offline programs too such as dictionary use for search the meaning of vocabulary. So we get the inspiration from both of them to create program that could find the similarity measure of something by user provided the keywords. But in case of that we have to use an ontology which is the representation of knowledge as a set of concept and relationships as the database. The ontology we are going to use is in the OWL file, web ontology language, which is the language based on RDF (resource description framework). The structure is like XLM, therefore it is easy to use and there also free software for understand ontology structure known as Protégé. Our program named as, which mean ontology plus diagnosis. is the program that user can search diseases and the related diseases by type some keywords or click the pictures and the list of diseases will be appear. But the problems are we are not cover all of the diseases in the world we just provide only some basic diseases, and the details of every disease will not be provide because our purpose is to show the similarity between disease and other diseases as percentage (0-100). We expect that our program would solve the problem that you don't clearly know what you were and reduce time to go to see the doctor in case you were not seriously sick. This report is divided into 9 sections, Section 2 explains about the background. Section 3 explains the objectives of this project. Section 4 lists the outputs and expected benefits of the project. The accomplished works of other people that are beneficial to this project are included in literature reviews in section 5. Methodology contains two subsections which are the approach and the tools and techniques, and they are in section 6. Section 7 is the project schedule which lists the tasks that are needed to be done and their deadlines. Section 8 is the technical description of the project we have done so far. References are lists on section 9, they are the source of information that we have gathered. School of ICT, SIIT 1

5 2 Background As this project we use the ontology to contain the data, we have to study what is an ontology. Ontology is a collection of vocabulary, which is represent with logic-based language such as, Description Logics language. Ontology is the new concept to use to store the knowledge to solve the abilities of technology like computer that we use as a medium to manage knowledge, for examples, searching, analysing, display or computing the knowledge since these are not good and fast enough. We can say that ontology is a collection of vocabulary, which is represent with logic-based language such as, Description Logics language, which is use to determine the relationship of the concept in ontology. Table 1: syntax and semantic of the Description Logics ℇL From the syntax of Description Logics ℇL, it can recognize the description into the primitive definition that defines concepts which only condition are known; see, e.g., (ω1) and the full definition that defines concepts which several condition are known; see, e.g., (ω2 ). The symbol of definition has meaning as is. Starting with the set of concept name, which presents as a vocabulary in capitalized (e.g. Father), we are called the concept name in the left hand side of concept definition as define concept and the other concepts as primitive concept. The relationship between the concepts, we define as role names are written in lower case (e.g. child). The totally purpose of this project is to show the similarity between one disease to another diseases, which represents as percentage. We provided the pictures of the body part for user to see category, instead of thinking for keywords by their selves and we also provided user to enable to type both English and Thai language. The user interface was a basic designed to be easy to use and font's format we used is a formal format. However, we found many problems while we are doing this project but the most wasted time is OWL file, web ontology language, which we have to connect. School of ICT, SIIT 2

6 3 Objectives The aim of the project is to develop tool of similarity measure, which allow user to search the probability of disease. There are the objectives we have considered: 1) To enable to find the probability of disease. 2) Show the related diseases as much as possible. 3) User friendly interface. 4) Able to type both English and Thai language. 5) Can be used in the real world situation. 4 Outputs and Expected Benefits 4.1 Outputs Design the good looking interface, easy to visualize the output from similarity measure. The development of the program or tool is created in order to allow user to find the probability of diseases. The following are the planned putouts for the project: 1) Provide the custom search: user can type any keywords. If the program matched any keywords, it will show the disease and the related diseases. 2) Presenting result in percentage: to show how much similarity they are (0-100%), sort by larger percentage first. 3) Category search: we provide a category for user by pictures of the body part, to be helpful for users who don t know what to type. 4) Both English and Thai language: user can search both of the language, but the result remains in English. 4.2 Benefits This project will give benefits to patients, doctors, or medical student because it provides a diseases knowledge. Including medical field, for example, this program can be use in diagnosis; to reduce complexity for similar relationship of diseases of contemporary existing diseases. The proposed scheme is easily applicable for users because if they just identify the fundamental ailment, the user can also know the probability of diseases and the related. To extend that, we intend to show degree of the term similarity as percentage The percentage support user to know how the relationships between them are; the larger percentage is more similar. School of ICT, SIIT 3

7 5 Literature Review In this project, we have been looking for the documents and websites that relate with the work. We found some useful resources that might help us doing this project, which will be mentioned here: Similarity Measure We are mainly focusing on the document written by Dr. Boontawee Suntisrivaraporn about A Similarity Measure for the Description Logic ℇL, with Unfoldable Terminologies [1]. From this source, it let me know about the background on DL ℇL, unfoldable TBoxes, and structural subsumptiom algorithm. Moreover, we study the notion of homomorphism likelihood that is useful for our work to apply this method on the web application. ELSim Tutorial We use the data which is written from Mr.Suwan Tongphu [4] we mainly use the concept of similarity measure which is semantically measure degree of similarity between two ELH concept descriptions w.r.t. a terminology. For an example, Mother Woman child.person and Father Man child.person, if you can see that there is no similarity between Father and Mother. However, it is obvious that the two concepts have shared some commonality (i.e. both are person and has some child person). Unlike classical reasoners which merely give either positive or negative conclusion, ELSim measures a numerical value based on a shared commonality therefore the result is the degree of similarity ranging between 0 and 1. BioPotal is a repository of biomedical ontologies, the largest such repository with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other formats, as well as a large number of medical. Figure 1: Visualization by BioPortal School of ICT, SIIT 4

8 Protégé is a free, open source ontology editor and knowledge acquisition system. [5] Protégé provides a graphic user interface to define ontologies. It also includes deductive classifier to validate that models are consistent and to infer new information based on the analysis of an ontology. Like Eclipse, Protégé is a framework for which various other projects suggest plugins. Figure 2: Interface of Protégé program Hozo is graphical ontology editor especially created to produce heavy-weight ontologies. Hezo is an environment for ontology development. The features of Hozo include Supporting to role concepts, Visualization of ontologies in well considered format, and Distributed development based on management of dependencies between ontologies [6] Figure 3: Visualization by Hozo School of ICT, SIIT 5

9 Ontologies and the sematic web the first resource we would like to present is Ontologies and the sematic web. It cover about RDF and OWL that have been developed as standard formats for the sharing and integration of data knowledge, which in the form of rice conceptual schemas called ontologies. We have known the tools and languages use to support them. This gives us an idea on the general concept of ontology that represented in the form of OWL ontology language standard. SWOOP is a tool for creating, editing, and debugging OWL ontologies. It was produced by the MIND lab at University of Maryland, College Park, but is now an open source project with contributors from all over. Figure 4: Interface of Swoop program School of ICT, SIIT 6

10 6 Methodology 6.1 Approach According to our work is a continuation of the experiment which implements by Ph.D. candidate student, in this part we separate into 3 part. First, we study the concepts of the ontology and how to use OWL API, NetBeans, ELSIM and Protégé. We set the scope of what we are going to do. We expect to have a window application to diagnose diseases which will contain at least 100 symptoms of 30 diseases. Second, we plan what feature that will have in application and we design the step to do application. We describe the flowchart of this work for easy to coding this application. In figure 5, is the flowchart to show step to do this application. Start End Design interface and feature that program will have Evaluation by medical student Coding program to connect with PharmaOntology.owl and keep data to hash table for easy to use later. Test program and debug it Evaluation Coding to accept input from the user and check it is symptom or not. Coding auto text complete that will easy to use Coding to check the input what diseases can be and rank it Coding program to support Thai language. Figure 5: Step to do application School of ICT, SIIT 7

11 Next, we also find the probability of diseases from the characteristics of the diseases in PharmaOntology.owl. For example, the class Cold, which is subclass of Disease, it have Fever and Sneeze as its symptom. Thus, we uses ELSIM to find similarity between symptom input and diseases characteristics. In figure 6, show step of program work it will easy to understand how program work and code program. Start Input from user Ex: Fever and Sneeze Change to Manchester syntax C1 = (Fever and Sneeze) No Characteristic of Diseases EX: D1 have S1 and S2 Change to Manchester syntax C2 = (S1 and S2) All disease No Yes END ELSIM computeconceptsimilarity(c1, C2) More 50% Yes List disease and percentage to be diseases C2 = (S1 and S2) Figure 6: Step of program work School of ICT, SIIT 8

12 6.2 Tools and Techniques Tools All tools are open source NetBeans is an integrated development environment (IDE) for developing primarily with Java, but also with other languages, in particular PHP, C/C++, and HTML5. [3] It is also an application platform framework for Java desktop applications and others. [6] Protégé is a free, open source ontology editor and knowledge acquisition system. Protégé provides a graphic user interface to define ontologies. It also includes deductive classifier to validate that models are consistent and to infer new information based on the analysis of an ontology. Like Eclipse, Protégé is a framework for which various other projects suggest plugins. [5] Pharmaceutical Ontology is implemented as an output. The implementation of the ontology will be done with quality so the ontology will be useful and reliable. The data sources will include the drugs themselves, the symptoms, the diseases, and some sample patients. The expected number of symptoms is approximately one hundred, thirty diseases, and drugs corresponding to the symptoms. [7]. JavaScript is programming language and worked with HTML. It can improve website by using movement to interface and increasing responded to users [8] JSON or Java Script Object Notation, It uses JavaScript syntax for describing data objects, storing and exchanging text information [9]. Adobe Photoshop is the predominant photo editing and manipulation software on the market. Its uses range from full featured editing of large batches of photos to creating intricate digital paintings and drawings that mimic those done by hand. School of ICT, SIIT 9

13 6.2.2 Techniques Knowledge about how to develop android application design. The basic background of ontology Implementation of Java programming skill The proposed research has been implemented by java programming language to design our window application. According to our adequate knowledge about how to use the java programming, we also use other coding skills, which are fundamental information about ontology knowledge and designing about window applications and their tools, to do this project. We also compile techniques of creating application ontology using Pharma Ontology. School of ICT, SIIT 10

14 7 Project Schedule The information in the table shows task, description about the task, person responsible for each task, duration, deadline and status. Note that some tasks are tentative and may be changed later to make them more appropriate. Task Description Person Duration Deadline Status 1 Study the basics of ontology and a similarity measure for the description logic 2 Learn the homomorphism likelihood function 3 Select platform to do this application, tool for programming, programming language, and study data structure. BS 1w 7 Oct 14 Complete BS 1w 14 Oct 14 Complete SS,PB 1w 21 Oct14 Complete 4 Draft proposal SS,PB 1w 2 Oct 14 Complete 5 Prepare draft proposal SS,PB 1w 13 Oct 14 Complete 6 Study how to code android application SS,PB 1w 20 Oct 14 Complete 7 Design slides for practice presentation SS,PB 1w 10 Nov 14 Complete 8 Study how to include API SS,PB 1w 17 Nov 14 Complete 9 Study about Java collections framework SS,PB 1w 24 Nov 14 Complete 10 Choose sample for test program and edited pattern of output SS,PB 1w 1 Dec 14 Complete 11 Design proposal for presentation SS,PB 1w 7 Dec 14 Complete 12 Proposal SS,PB 1w 15 Dec 14 Complete 13 Plan how to code program SS,PB 1w 23 Jan 15 Complete 14 Learn and test ELSIM SS,PB 1w 30 Jan 15 Complete 16 Connect with PharmaOntology by use owl API SS,PB 1w 6 Fab 15 Complete 17 Do midterm presentation SS,PB 1w 13 Fab 15 Complete 18 Midterm presentation SS,PB 1w 20 Fab 15 Complete 19 Midterm Report SS,PB 1w 27 Fab 15 Complete 20 Design new interface SS,PB 1w 6 March 15 Complete 21 Code for accept input, design result panel SS,PB 1w 13 March 15 Complete 22 Code for find diseases from input symptom SS,PB 1w 20 March 15 Complete 23 Code for auto text complete SS,PB 2w 3 April 15 Complete School of ICT, SIIT 11

15 Task Description Person Duration Deadline Status 24 Code for click organ picture to find symptom SS,PB 2w 17 April 15 Complete 25 Test and debug SS,PB 1w 24 April 15 Complete 26 Change interface SS,PB 1w 12 May 15 Complete 27 Final Report SS,PB 1w 19 May 15 Complete School of ICT, SIIT 12

16 8 Technical Description 8.1 Ontology Review To use ontology, we have to study and understand its structure. First, ontology is a knowledge representation that is kept in a directed acyclic graph. The top of it is the class named Thing and the bottom is a united nothing class. The other classes are the subclasses of Thing, and arranged in a hierarchical manner, and they are parts of TBox. The entity called an object property can be established to define the role or relationship between classes. Information can be kept in the ontology as an individual, or ABox. The ontology we selected was used is PharmaOntology.owl in OWL/XML format. In figure 7, Show about diseases ontology in PharmaOntology.owl. In figure 8, show code to connect to owl API. Thing Diseases Patient with diseases Symptom 1 Symptom 2 Symptom 3 Symptom 4 Figure 7: The ontology of diseases Figure 8: Connect owl API code School of ICT, SIIT 13

17 8.2 System Overview We using MVC design pattern, we separate into Model, View, Controller (Figure 9). JAVA SWING GUI ( Form) JAVA (Controller) ELSIM API OWL API ELSIM API PharmaOntolo gy.owl View Controller Model Figure 9: The MVC architecture of The central component, the Model, represents application data, logic, and functions. A View renders the contents of model. It accesses application data and display data in any output representation. A Controller accepts input and translates it into actions to be performed by the model. The is developed by Java. The platform provides JAVA SWING GUI. The advantages of using JAVA are platform-independent (cross platform), ease of use and security features. 8.3 Flow Chart We describe the flowchart of this work for easy to coding this application. Start Find similar degree between C1 and C2 More 50% No Connect to PharmaOntology.owl and keep data to hash table for easy to use later. Connect to hash table and keep symptom of diseases to C2 Yes List diseases that have over 50% Create function to check input are symptom or not No User Input Create function to get similar degree between 2 concept All Diseases Yes Print input not symptom No Is symptom Yes Change input to Manchester syntax and keep it to C1 End Figure 10: The flowchart to show step for coding School of ICT, SIIT 14

18 8.4 Requirements Functional Requirements Find diseases from symptom input function, this function will show the percentage of probability to be diseases. In figure 11 is example for this function and figure 12 show code of this function. Figure 11: Find diseases function Figure 12: Code of find diseases from symptom function School of ICT, SIIT 15

19 Auto Text Complete, this function will help for type symptom easier and correctly. In figure 13 is example for this function. Figure 13: Auto Text Complete Function Thai Language Symptom Function, this function will support Thai language symptom. In figure 14 is example for this function. In figure 15, show code how to add Thai language code. Figure 14: Thai Language Symptom Function Figure 15: Add Thai language symptom code School of ICT, SIIT 16

20 Picture for find symptom, this function will easy to find the symptoms. Just click organ picture it will have symptom list about that organ. In figure 16 is example for this function. In figure 17, show example code for this function. Figure 16: Picture for find symptom function Figure 17: Picture for find symptom function code School of ICT, SIIT 17

21 8.4.2 Non-Functional Requirements User-friendly interface Easy to use Support for English and Thai Language Process within few minute Developed using free and open source software Not use internet School of ICT, SIIT 18

22 9 References [1] B. Suntisrivaraporn, A Similarity Measure for the Description Logic ℇL with Unfoldable Terminologies, Sirindhorn International Institute of Technology, 2012, published. [2] Dr.Boontawee Suntisrivaraporn, Prof. Dr.-Ing. Franz Baader, Prof. Ian Horrocks, Prof. Dr. rer. nat. habil. Ralf Mo ller. Polynomial-Time Reasoning Support for Design and Maintenance of Large-Scale Biomedical Ontologies. Technische Universita ẗ Dresden, University of Oxford, Technische Universita ẗ Hamburg-Harburg. [3] Keet blog. Logical and ontological reasoning services?.applies Ontology, [4] Mr.Suwan Tongphu ELSim Tutorial [5] Protégé, open source ontology editor, [6] NetBeans, [7] Pharmaceutical Ontology for Medical Prescription, Sirindhorn International Institute of Technology, 2011 [8] Sumbydesign.net. What is JavaScript. Sumbydesign, Whatis.html, 2013 [9] JSON. Wikimedia Foundation, [10] Adobe Protoshop, School of ICT, SIIT 19