Keywords: Clustering, Relational ontology, Ontology relation, Contributed attributes, Concepts, Combined relations. RELATED WORK I.

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1 Generating Ontology Relations through Clustering for Preventive Health Care Sang C. Suh Lavanya Gaddam Tarun Bheema Department of Computer Science Texas A&M University Commerce Commerce, TX Abstract- Ontology is a technique that represents data as a set of concepts within a domain and the relativity of those concepts [16]. It is used to give the rationale to the entities within the domain, and can also be used to describe about it [21]. Clustering organizes data into individual clusters. In this article we are proposing a model which uses the hierarchical relationships. We group related item sets to form clusters utilizing relational ontology. Relational ontology varies from domain to domain. As the name suggests relational ontology is more about relations. It represents the relativity of attributes and concepts. By performing clustering on the relational ontology we derived new relations through which, we can generate the new rules learnt from the database. In this article we have chosen the preventive health care domain through nutrition as an instance to show how effective is relational ontology to represent the existing data and to generate the further knowledge in the database. Furthermore, potential application of this technique on ontology relations clustering will be in the area of search engine based on semantic representation through ontology relations. While search engine process the task the link table look up the relations in the table for appropriate result. Keywords: Clustering, Relational ontology, Ontology relation, Contributed attributes, Concepts, Combined relations. I. INTRODUCTION Data clustering is a technique through which we can develop cluster of objects that possess identical or similar characteristic in one way or the other. The criterion for ensuring the similarity is implementation dependent [16]. To meet the peculiar requirements like high volume of data, high dimensionality, meaningful cluster labels for clustering documents and ease for browsing we propose CBRO (clustering based on relational ontology). In which, we can ignore the number of clusters since this method is robust enough to handle different types of domains in a real-world environment. Furthermore, imprecise estimation of the number of clusters often yields poor clustering accuracy. CBRO enhances clustering accuracy by establishing related item sets. Health care is a core component of one's life. Not to our surprise most tragic ill health conditions of our life like diabetes, cardiovascular diseases, obesity and malnutrition are preventable if taken proper measures. In several instances these problems arose out of negligence, improper care, they start as small health issues and when we ignore preventive measures, they turn into serious life threatening conditions and even fatal if they go undetected and untreated. The enduring nutritional transition expressed through increased utilization of high fat and excessive sodium containing products. It contributes to the rising burden of heart disease, stroke, obesity and diabetes. Preventive health care through proper nutrition is one of the most efficient means of triggering these issues and to stay out of most costly and disabling conditions mentioned above. We are aspiring to implement our application on domain which is useful for variety of people. Here we are proposing a related item set based clustering, utilizing the relational ontology (has, avoids, causes and occursin etc). HAC is a hierarchical model tool for human cognitive concepts [8]. Our application CBRO is the reforge of HAC employing relational ontology. Related Item Set (RIS) is a set of related contributed attributes, applying RIS and relational ontology we build relationship between contributed attributes and concepts. We also implemented the concept of HAC (Hierarchy of Attributes and Concepts), but here we maintain the hierarchy based on relation. We have shown clusters in different levels using relational ontology. We are also presenting PHC through nutrition domain sample input and the output of it implementing our application. II. RELATED WORK The goal of clustering is to reduce the load of data by classifying it into groups depending on similarity of its objects. Such grouping is pervasive in the way humans process information, and one of the motivations for using clustering algorithms is to provide automated tools to help in constructing categories or taxonomies. The methods may also be used to minimize the effects of human factors in the process. Clustering methods can be divided into two basic types: hierarchical and partitional clustering. Within each of the types there exists a wealth of subtypes and different algorithms for finding the clusters. Hierarchical clustering proceeds successively by either merging smaller clusters into larger ones, or by splitting larger clusters. Partitional clustering, on the other hand, attempts to directly decompose the data set into a set of disjoint clusters [3]. HAC is both hierarchical and conceptual clustering system that organizes data so as to maximize inference ability [4]. The idea of hierarchical clustering is to begin with each point from the input as a separate cluster. We then build clusters by merging clusters that are close to each other: repeatedly merge two or more clusters that are closest to each other out of all pairs [8]. Visual Data Analytics (VDA) system represents the hierarchy of

2 attributes and concepts graphically. VDA is designed to represents attributes, concepts and their relationships visually [12, 13]. Figure 1 shows the example of HAC with concepts and the related attributes. Pink and green buttons shows the attributes and concepts respectively. Lines between them show the relationship between the concept and attributes. In this paper, we propose clustering based on the semantics (i.e. meaning), according to which we define ontology relation for every attribute. In a cluster every attribute is related based on that relation. Transitive means if A is related to B and B is related to C, then A is related to C Reflexive means the relation is bidirectional. I.e., if A is related to B, then B is related to A. Anti symmetric means if A is related to B and B is related to A, then A is equal to B In this paper we have taken four relational ontology relations from the above-mentioned relational ontology. They are has, avoids, causes and occurs in. Using these ontology relations, contributed attributes are clustered as concepts in PHC domain. We combined two ontology relations and defined a new relation to represent a combined ontology relation. For example: AVOIDS OCCURS IN GOOD FOR Blindness come under the concept vitamin A according to ontology relation avoids and it also comes under the concept eyes according to ontology relation occurs-in. From these two ontology relations, a new ontology relation good for derived between vitamin A and eyes. IV. CLUSTERING ON RELATIONAL ONTOLOGY Figure 1: Example of HAC III. RELATIONAL ONTOLOGY Relational ontology varies from domain to domain. Ontology, a cornerstone of the semantic web, have gained wide popularity as a model of information in a given domain that can be used for many purposes, including enterprise integration, database design, information retrieval and information interchange on the World Wide Web. For our work, we describe OBO (Open Biological and Biomedical Ontologies) relational ontology [2, 6, 7 ]. TABLE 1 BASIC RELATIONAL ONTOLOGY RELATIONS Transit ive Reflexive Anti symmetric Is a x x X Part of x X X Integral part of X X X Proper part of X Located in X X Preceded by X Has X Avoids Causes Occurs-In In existing clustering methods, there has been no attempt to form clusters using relations. In this paper we build a database on PHC, using word net from which we define definition of each relation for each and every contributing attribute. Word net is a lexical database for English language [14]. We build an input table which is shown in Table 6 in results section. We built contributed attribute table directly from the source table using the fields, ca-id and ca-name. We used an algorithm to build concept table. The algorithm is shown below on how the concepts and relations are generated from the source table. For example, calcium is a concept and its related attributes are cheese, molasses, vegetables according to ontology relation has. INPUT: Definition of each contributed attribute of every property OUTPUT: Concepts generated PROCEDURE: 1. Initialize property[]; 2. Repeat Until each property from the defined properties { 3. Repeat until the end of each property (column ) { 4. Repeat Until(j<n) { 5. Compare the first string with all other strings if they are equal consider it as concept { 6. Add the String to the group of concepts 7. Make the property of new string as OR } } } }

3 Clustered data is shown in 2 different ways. One way is according to concept, based on ontology relation of first level as illustrated by Figure 2. Figure 2 shows the cluster of concept protein. For example eggs, milk, rice, whole grain and corn comes under the concept protein according to the ontology relation has. Second way is according to each relation. For example, for ontology relation avoids in Figure 3(a), fissured tongue and neural tube disease can be avoided by folic acid, so we cluster them as one cluster. Osteoporosis and bone disorders can be avoided by calcium, so we cluster them as another cluster under avoids ontology relation. Here we combined two ontology relations and by combining them we defined one new relation between two concepts. TABLE 2 NEWLY GENERATED RELATIONS THROUGH RELATIONAL ONTOLOGY Combined relation New relation Has + Avoids Prevents Has + Causes May cause Causes + Occurs-in May effect Avoids + Occurs-In Good for Has + Causes + Occurs-In Not good for Has + Avoids + Occurs-In Good for R 1 : a 1 a 2 R 2 : a 3 a 1 Then, R 3 = R 1 R 2 => a 3 a 2 For example, since vitamin C avoids scurvy and scurvy occurs in gums as shown below, from these relations, a new relation, good for, between cancer and loss of vision is derived, as shown below and also in Figure 4. Vitamin C avoids scurvy Scurvy Occurs in gums So, Vitamin C good for gums In this way we derived new ontology relations which are shown in combined relation table (Table 2). Figure 2: Clusters based on concept Protein Here we combine two HACs (built based on existing ontology relations) to form a new HAC representing a new relation as shown in Figure 4. (a) AVOIDS (b) OCCURS IN Figure 3: Clusters based on existing relations AVOIDS and OCCURS IN

4 clusters based on that new relation. CBRO shows the new relation with green links between two concepts like in Figure 7(a). For example when we combine causes and occurs-in we get a cluster like in Figure 7(a), here green links show the new ontology relation may effect in between two connected concepts, red links shows the first level ontology relations causes and occurs-in and all the connected concepts with their relation are shown below the circle. Figure 4: New relation GOOD FOR TABLE 3 CONTRIUTED ATTRIBUTED TABLE Concept_ID CA_ID Relation C23 S34 Occur in C16 S31 Occur in C1 S4 Has C10 S47 Has C1 S35 Avoids C10 S19 Avoids C10 S32 Avoids C13 S35 Causes C5 S32 Causes V. IMPLEMENTATION We have chosen PHC through nutrition domain as the input database. Figure 6(a) shows the entire domain using circles with buttons on the circle representing the contributed attributes and buttons inside the circle representing concepts. Preventive health care takes measures to prevent diseases (or injuries) rather than curing them. From our PHC domain, we only consider different food types, minerals, diseases caused by vitamin deficiency and diseases caused by over consumption of minerals, and the parts affected by different diseases among many others. The most important part of preventive health care is in maintaining good healthy diet. CA_ID S12 S13 S14 S15 S16 S17 S18 S21 TABLE 4 CONCEPT TABLE CONCEPT_ID CONCEPT_NAME C1 calcium C10 vitamin E C11 vitamin B C12 vitamin K C13 protein C14 vitamin A TABLE 5 LOINK TABLE CA_NAME vegetable oil sunflower seeds leafy vegetables orange, lemon banana scurvy blindness cheddar When we click on the concept we will get the related contributed attributes of that concept and it will also displays the relation between the concepts and attribute. Figure 5 shows the cluster of concept vitamin H. Example: Concept: Vitamin H Related Contributed Attributes: skin disease, hair loss. Relational Ontology: avoids User also has an option to select the relational ontology from the drop down list which is shown in Figure 6(a). When user selects the particular relational ontology then it shows all clusters which are built based on that relational ontology. Figure 6(b) shows the clusters based on the ontology relation occurs-in. Neural tube disease occurs-in spinal cord and brain. The database for any domain to implement our application needs four tables: contributed attribute table, concept table, link table and combined relation table. The Contributed Attribute (CA) table contains CA_ID and CA_Name as shown in Table 3, concept table contains Concept_ID and Concept_Name as shown in Table 4, link table contains Concept_ID, CA_ID and Relations as shown in Table 5, combined relation table contains combined relations and new relations as shown in Table 2. Using these tables we represent the clusters visually in different ways. Initially it shows the circle with contributed attributes on the circle and concepts inside the circle. When we click on any concept it shows the related attributes by linking each and every related attribute with that concept. CBRO allows the user to select the relation from the dropdown list which they want, and then it shows the clusters based on that relation. CBRO combines two relations to get the new relation and forming next level Figure 5: Cluster of Concept VITAMIN H

5 VI. RESULTS Here is our sample input table which we used for CBRO. We built this table using information from various websites about the food, minerals and diseases and the parts which affected by those diseases. By considering food habits we built this table as shown in Table 6. To implement CBRO on PHC we derived four tables those are contributed attribute table, concept table, link table and combined relation table from the input table. (a)view of CBRO (b) Clusters of ontology relation OCCURS-IN Figure 6 We used an algorithm to get the concept table and link table from the input table. CBRO derives the new ontology relation between concepts from the existed ontology relation between concepts and contributed attributes. Output of CBRO contains the derived new ontology relation between two concepts. Heart attack comes under concept heart based on ontology relation occurs-in and it also comes under concept cholesterol based on ontology relation causes, using these two we built a new ontology relation between two concepts cholesterol and heart that is may effect. Leafy vegetables have Vitamin A and vitamin A avoids Blindness, from this we derived a new ontology relation prevents. Leafy vegetables prevent blindness. Table 7 Sample output table shows some of the newly derived ontology relations. Figure 7 shows the derived ontology relations and their clusters. Figure 7(a) shows ontology relation may effects which is determined by combining causes and occur-in. Examples of derived relation may affects: cholesterol may effects heart, over consumption of vitamin C may effects blood etc. Figure 7(b) shows the ontology relations good for which is derived by adding has+ avoids + occur-in. In the Figure red line shows the 1 st level relation has, green lines shows the 2 nd level relation which is determined by adding avoids and occurs-in and the black lines shows the actual third level relations which are determined by adding has and good for. Examples of derived relations good for: eggs good for lungs, leafy vegetables good for eyes etc. TABLE 6: SAMPLE INPUT TABLE Concept_ID Relation Concept Contributed Attribute C1 Has Calcium S1,C4,S54,S53,S52,S44,S43,S24,S25,S26 C21 Has Zinc S9,S11,S12,S55,S56 C3 Avoids Vitamin C S8,S10 C6 Causes Cholesterol S1,S16 C7 Occurs In Heart S20,S21 C8 Occurs In Eyes S1,S3,S14 C10 Good for Gums S23,S45 TABLE 7: SET OF NEWLY GENERATED RELATIONS Relation Concept-Name Has (Broccoli,sodium) (Pecans,vitamin A) Avoids (Vitamin C, Scurvy), (Vitamin F, Fissured Tongue) Good for (protein, hair),(vitamin H, hair)(vitamin A, eyes),(folic acid, spinal cord),(folic acid, brain),(calcium, bones),(zinc, lungs),(vitamin C, Gums) Occurs-in (Diabetes, Blood) May effect (Cholesterol, heart),(protein, kidney),(over body weight, heart), (Vitamin C, blood),(protein, bones) Not good for (Fatty food, heart), (Deep fried food, heart) Good for (nuts, hair),(corn, hair),(rice, hair),(citrus fruit, gums),(vegetables, gum)

6 (a) may effect VII. CONCLUSION AND FUTURE WORK In this paper, clustering of data using ontology relations in PHC domain was discussed and illustrated. Few tables like contributed-attribute table, concept table and link tables were built to implement CBRO for the PHC domain. First, data was clustered in concept level. Second based on the existing ontology relations has, avoids, causes and occurs-in new ontology relations are generated through this process. Ontology relations may cause, prevents, good for and may effects are derived between two concepts by implementing CBRO application in PHC domain (Data base). Then, we integrated HAC s of three related ontology relations. Ontology relations good for, not good for are derived between three concepts by implementing CBRO application in PHC domain (Data base). In the future, further work can be done to expand this HAC by allowing more than three ontology relations to be combined as a single relation. While our application CBRO is domain dependent, it will be expanded to be suitable for any domain (domain independent) as future work. VIII. ACKNOWLEDGMENT This research has been partially supported by a grant from the US Department of Energy Grant (Funding #: DE-SC ). It has also been supported in part by an interdisciplinary research enhancement grant from the graduate school of A&M University- Commerce. This paper is the extension of role of clustering of ontology relations for preventive health care through nutrition. IX. REFERENCES 1. Benjamin C.M. Fung, Ke Wang, Hierarchical Document Clustering Using Frequent Item sets Martin Ester SIMON FRASER UNIVERSITY, BC, Canada, September Sudipto Guha, Rajeev Rastogi, Kyuseok Shims ROCK: A Robust Clustering Algorithm for Categorical Attributes, Information Systems Volume 25, No. 5,pp , Elsevier Science Ltd, Britain, Sang C. Suh and Kalyani Komatireddy, Clustering of Ontology in Preventive Health Care Through Relational Ontology, IKE 11, World comp 2011, Las Vegas, Nevada, USA. Figure 7: Clusters of newly generated relation (b) Good for 4. Ji-Rong Wen, Jian-Yun Nie, Hong-Jiang Zhang Query Clustering Using Content Words and User Feedback, SIGIR 01, New Orleans, Louisiana, USA, Sang C. Suh, Sam I. Saffer, Nikhil Goel, Young S. Kwon, Generating Meaningful Rules Using Attribute Concept Hierarchy, Intelligent Engineering Systems through Artificial Neural Networks, Volume 16, ASME Press, E. A. Freigenbaum and H. Simon, EPAM-like Models of Recognition and Learning, Cognitive Science, Volume: 8, pp , R.T.Ng and J.Han Efficient and Effective Clustering Methods For Spatial Data Mining. In Proceedings of the VLDB conference, , Santiago, Chile, Sang C. Suh and Gouthami Vudumula The Role of Conceptual Hierarchies in the Diagnosis and Prevention of Diabetes, NCM 7 th International Conference, Sang C. Suh and Jhansi Baireddy, Visual Representation of Hierarchical Attributes and Concepts as an Ontology for Semantic Reasoning, Intelligent Engineering Systems through Artificial Neural Networks, Volume 20, ASME Press, Fellbaum, WordNet: an electronic lexical database Cambridge, MIT Press, wordnetweb.princeton.edu/perl/webwn 12. en.wikipedia.org/wiki/ontologies_(computer_science) 13. Jan Jantzen, Tutorial on Fuzzy Clustering, Technical Report, DTU, August H. G. Wilson, B. Boots, and A. A. Millward, A Comparison of Hierarchical and Partitional Clustering Techniques for Multispectral Image Classification, Geoscience and Remote Sensing Symposium, IEEE International, Vol.3 pp , Holmes Finch, Comparison of Distance Measures in Cluster Analysis with Dichotomous Data, Ball State University, Journal of Data Science 3, ,