Mye M. Sohn

Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment

To provide context-based personalized services utilizing smart appliances in a smart home environment, we propose a framework for PersonAlized Service disCovery Using FuZZY-based CBR and Context Ontology (PASCUZZY). Basically, the PASCUZZY framework is implemented on case-based context ontology. To generate and manage the case instances on the case-based context ontology, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment. The context is transposed to linguistic-type context instances on the context ontology. In addition, to formalize and manage the context and services as multi-attributed data, the context ontology was developed reflecting the structure of cases borrowed from case-based reasoning. Furthermore, we propose adaptation methods to adjust the generic fuzzy membership functions depending on the inhabitants’ context. It is performed by modifying the values of the membership number and/or modifying the numbers of the linguistic terms that are based on the inhabitants’ context to affect the membership numbers. The adapted membership functions return the personalized degree of memberships depending on the specialized context of a specific fuzzy variable. Inevitably, the number of cases on the case-based context ontology will be increased from time to time. We apply Ward’s method not only to reduce the search effort via a hierarchical clustering on the case-based context ontology but also to find the most similar service as a solution to the new context. To verify the superiority of the PASCUZZY framework, we perform two kinds of evaluations. First, we evaluate the effectiveness of the adaptation of the fuzzy membership functions. Second, we verify the effectiveness of the application of a clustering method to the case instances of the case-based context ontology to identify the most similar service. Results of the experiment verified the effectiveness and superiority of the PASCUZZY framework.

Self-Evolved Ontology-Based Service Personalization Framework for Disabled Users in Smart Home Environment

Providing context-aware personalized service in smart home environment is very important to improve the quality of the disabled life. To reaching the goal, it needs to challenge due to diversity of needs: type of disability, degree of disability, preferences, tasks, and context. We propose a framework to recommend context-aware personalized services for the disabled in smart home environment called SOLVE-D. Main module of SOLVE-D is ontology repository that is consisted of three ontologies: generic service ontology, personalized service ontology, and service context ontology. The ontologies interact with each other to recommend context-aware personalized services, and to increase the quality of personalized services for disabled person in smart home environment. In addition, to support self-evolved ontologies and to provide the optimal service, SOLVE-D has follow modules such as Standard Service Recommendation Module (SSRM), Personalized Service Recommendation Module (PSRM), Personalized Service Ontology Generation Module (PSOGM), and Context and PSO Mapping Module (CPMM). The proposed ontologies and various modules of SOLVE-D are in perfect harmony to provide context-aware personalized services for` disabled users.

Energy-aware optimal cache consistency level for mobile devices

Power consumption in a mobile device is an important performance metric. It is strongly required for the mobile device to access wireless links efficiently because wireless communication expends a significant amount of energy per second. By using cache schemes, we can reduce communication costs and enhance performance by storing and reusing recently used data. However, the cached data on the mobile device should be updated in sync with the original data on the server as they are being modified. With a @d consistency policy, we can provide the energy-efficient cache consistency level by setting @d in accordance with various environmental parameters. This paper proposes a method to determine the optimal @d that minimizes the overhead energy expenditure for mobile devices in wireless web environments. We derive the optimal @d from a mathematical model and conduct a simulation for validation. We also implement a test application for the proposed optimal @d method on a test-bed and assess its performance.

Context-Based Hybrid Semantic Matching Framework for E-mentoring System

In this paper, we propose a context-based hybrid semantic matching framework for e-mentoring system. The framework adopts ontology technologies to define the attributes of mentors and mentees, the matching results, and satisfaction values of the mentoring, and to recommend adequate mentors via semantic reasoning. In the developed ontology named mentoring-matching ontology (M2O), the pairs of mentor-mentee and the satisfaction values of mentoring are managed as cases. To increase the satisfaction of matching, the framework performs rule-based filtering and two kinds of semantic matching to find appropriate mentees and the most similar mentor with the new mentee. We implemented a mobile e-mentoring system named Mint Story with the proposed matching framework. Also, we show the superiority of our framework in the evaluation section.

Crowdsourced healthcare knowledge creation using patients’ health experience-ontologies

In this research, we developed CHEKC framework for creation and integration of crowdsourced healthcare knowledge using experience-ontologies. The purpose is to provide patients’ healthcare information which contains similar healthcare experiences including conditions and symptoms and integrates the features and relations in the particular patients’ data according to users’ queries. To do this, we developed three modules and ontologies. The modules are Crowdsourced Health Data Manipulation Module (CHMM), Health Ontology-based Relevant Patient Finding Module (HRFM), and Ontology-guided Healthcare Knowledge Integration Module (OKIM). CHMM is developed to transform patients’ data to structured cases with problem-solution. The cases are stored in CHEKC Patient Ontology. HRFM is developed to find relevant cases according to the user’s query using CHEKC Upper Ontology. To do this, ensemble semantic similarity is proposed using semantic similarity and fuzzy C-means clustering and the relevant cases are stored in Interim Ontology. OKIM is developed for the integration of the relevant cases using SWRL rule-base. However, it is not guaranteed to find suitable rules and generate necessary knowledge from the rule-base. To relieve the problem, ontology-guided knowledge integration is proposed, which supports the inferring relations among classes in CHEKC Interim Ontology. CHEKC framework provides the integrated healthcare information and knowledge which are generated through the illustrated processes using the selected similar healthcare cases with users’ query. In particular, the cases are constructed by crowdsourcing on healthcare-featured social media and are based on patients’ healthcare experiences from the perspectives of patients. Through the conducting of two experiments, we proved the effectiveness of CHEKC framework. The conducted experiments proved the efficiency of CHEKC framework by the reduction in search volumes and the improvement in accuracy of query results.

Keyword-Based SPARQL Query Generation System to Improve Semantic Tractability on LOD Cloud

As the number of RDF triples on the Linking Open Data (LOD) Cloud has been exponentially increased, the difficulties of information query have been increased. To query information on the LOD Cloud, the users have to have some capabilities for developing SPARQL and/or RDQL query statements and exact knowledge for web resources with RDF such as URI, DB title, and name of things. However, it is almost impossible to require the capabilities and/or knowledge to the users who are familiar with keywords search. So, we propose fully automated keyword-based SPARQL query generation system. In our system, the users can query information on the LOD Cloud without having capabilities about structured query language and having prior knowledge of web resources with RDF. The users should type a set of keywords into our system. To do so, we developed a property-based path finding algorithm and an automated SPARQL query generation that can be used to provide query recommendations for the users. In the experimental section, we illustrate an example to validate the effectiveness of our system, and perform a simulation to show the superiority of the algorithms. The experiment results are not bad for a first attempt. If we improve the algorithms, we can expect a better result.

Ontology-based Dynamic and Semantic Similarity Calculation Method for Case-based Reasoning

In this paper, we propose an Ontology-based DYnamic and Semantic similaritY computation method for CBR (ODYSEY). ODYSEY is developed for the computation of dynamic similarity as well as semantic using dynamically changed ontology structure according to occurred context using CBR. Context is defined as any information that can be used to characterize the occurrence situation of a new problem and cases. To compute dynamic and semantic similarity, ontology is restructured by the context. The domain ontology is developed by consideration of contexts, and partial ontologies are extracted from the ontology including a set of shared contexts with same features and values between a new problem and cases. We implemented a mobile e-mentoring system, named MintStory© to show the applicability and feasibility of ODYSEY. As experimental results show, the satisfaction value of similarity of MintStory© is higher than the coordinator-recommended result. It shows that the ODYSEY is significantly efficient in a similarity ...

Tag-Based Integrated Semantic Ontology Construction and Evolution

In this research, we propose a methodology for ontology construction and evolution using tags from tag-cloud to improve availability of analysis and utilization of large scale of data. It is possible to construct semantic relationships among tags. To do this, one way of building of relationships among tags is use of a popularized tag as a thing of ontology. However, it is not easy to select the popularized tag from tag cloud and to build a complete ontology with all of tags in the tag-cloud because tags are timely and dynamically updated added with tremendous numbers of tags. Therefore, we construct Primitive Ontologies (POs). A general tag is used to as a thing of a PO. The general tag is matched into one of requested keywords by users. In addition, we propose a methodology to evolve ontology using tags. To construct and evolve a PO, it defines classes and relationships between given tags by a user. Finally, the constructed several POs are integrated into a Tag-Based integrated Semantic ontology (TBiSont) by building relationships between classes that are included in each different PO. The construction of relationships among tags depends on linked resources into tags. The properties of tags are usually characterized by linked resources. It causes to extract semantic relationships among tags, because tags are comprised of meta-information that represents properties of resources. Thereby, the proposed PO is constructed by a general tag as a best-referring tag and relationships among tags including the general tag. The TBiSont as an integration of POs was applied into data analysis and a search process to verify efficiency of as in application systems. When we applied TBiSont into searching, it shows that TBiSont is more efficient than the other statistical-based search systems to increase accuracy or diversity of search results.

Energy-efficient carpool policy for wireless interfaces of mobile devices in ubiquitous environments

Abstract Nowadays, power consumption is a big concern for mobile devices because battery power of mobile devices is one of the most crucial resources. Unfortunately, the performance of battery power fails to meet the power needs of high-end mobile devices. Increased data to be transferred/received through wireless communication incur high power consumption, because the wireless communication interface is one of the most dominant modules of mobile devices in terms of power consumption. Therefore, efficient power management of wireless interfaces should be employed for mobile devices. Dynamic power management is widely employed in order to support multiple power modes such as active modes (e.g., transmit, receive, and idle mode) and inactive modes (e.g., sleep mode and power off). Therefore maximizing the staying time of the wireless interface in an inactive mode is an essential scheme to reduce energy expenditure. However, required overhead energy and time for turning on/off the wireless interface are not negligible. Most of recent works have been trying to enhance the hardware architecture or network/MAC protocols. In this paper, we present an energy-efficient carpool policy that turns on the wireless interface and transmits all awaited data only when the predefined threshold is exceeded. We propose two kinds of thresholds–time and space. For practical evaluation, we conduct not only simulations but also experimental measurements by implementing a test program on a real test bed. Results of simulation and experimental measurements show that our proposed scheme incurs less energy expenditure than legacy power management schemes do.

Construction of Tag-Based Dynamic Data Catalog (TaDDCat) Using Ontology

In this research, the proposed Tag-based Dynamic Data Catalog (TaDDCat) supports dynamic search of resources depending on users requirements using tags from social web driven resources. It is possible for tags as meta data to match into some resources. Consequently, semantic relationships between tags can be extracted by dependency of relationship between tags and resources. TaDDCat using tags constructs hierarchical and associative relationships among tags for effective search of solution. Information template (ITM) is defined by extracted tags according to users requirements and appropriately searched resources by the tags. In addition, primitive tree is extracted by hierarchical relationships among tags which are defined on semantic-tag cloud. The associative relationship is defined by shared resources between tags. Finally, new class which is generated by merged tags highly associative. The class creates new hierarchy with Hyper/hypo relationship between the class and former tags which are not merged. At last, integrated ontology (IO) is developed with relationships among tags. resources are matched into the IO are named by integrated information resource (IIR). The TaDDCat is completed by ITM with combination of Tags(T) and integrated information resource, and reasoning by relationships(R). The TaDDCats supports improvement of correctness and agility of search and decreasing of search effort by reduction of quantity of search data.