COGOM: Cognitive Theory Based Ontology Matching System

AbstractOntology matching systems take a prominent position in solving semantic heterogeneity problems to facilitate sharing and reuse of ontologies. The process of generating ontology alignments through ontology matching techniques purely lies on how the concepts and relationships are modeled. This paper focuses on designing an ontology matching system in which concepts are modeled based on cognitive units of knowledge comprising of objects, attributes and relationships. The proposed cognitive based ontology matching system(COGOM) identifies semantically related concepts by aggregating the attribute similarity degree, structural similarity degree and semantic conception degree. The similarity computation is adapted from the Tversky psychological model of similarity. The proposed ontology matching system is adaptive in nature because of the cognitive based knowledge expression and the computational overhead of generating alignments is improved by forming quality clusters of semantically correlating concepts thus reducing the concept match space. The precision and recall metrics are used for evaluation of the proposed system using the benchmark data sets of OAEI 2015

Comparative study of game theoretic approaches to mitigate network layer attacks in VANETs

For the past few years, number of accidents have occurred due to the rapid usage of vehicles in the road and the lack of emergency alerts provisioning to the vehicle user during natural disasters. VANETs provide an environment to communicate between vehicles to avoid such accidents. Vulnerabilities in the network layer of VANET delays timely traffic data dissemination to the vehicle user. Ensuring security in VANET is very essential for the construction of a robust network for transmission of data. In this paper the game theoretic approaches like co-operative and non-cooperative games for handling security issues in VANET are discussed. Keywords: Game theory, Cooperative game, Non-Cooperative game, Nash equilibriu

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data

There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi-automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA's applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re) coding tasks and we believe it will prove useful for many more projects

TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and Security

This book contains abstracts of the various research papers of the academic & research community presented at the International Conference on Innovations and Challenges in Computing, Analytics and Security (ICICCAS-2020). ICICCAS-2020 has served as a platform for researchers, professionals to meet and exchange ideas on computing, data analytics, and security. The conference has invited papers in seven main tracks of Data Science, Networking Technologies, Sequential, Parallel, Distributed and Cloud Computing, Advances in Software Engineering, Multimedia, Image Processing, and Embedded Systems, Security and Privacy, Special Track (IoT, Smart Technologies and Green Engineering). The Technical and Advisory Committee Members were from various countries that have rich Research and Academic experience. Conference Title: TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and SecurityConference Acronym: ICICCAS-2020Conference Date: 29-30 July 2020Conference Location: Pondicherry Engineering College, Puducherry – 605014, India (Virtual Mode)Conference Organizer: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.Conference Sponsor: TEQIP-III NPIU (A Unit of the Ministry of Human Resource Development, India)