Ontology-Driven Analytic Models for Pension Management and Decision Support System

Ontology-Driven Analytic Models for Pension Management are sophisticated approaches that integrate the principles of ontology and analytics to optimize the management and decision-making processes within pension systems. While Ontology-Driven Analytic Models offer significant benefits for pension management, there are also challenges associated with implementing and utilizing the models. Developing a comprehensive and accurate ontology for pension management requires a deep understanding of the domain, including regulatory frameworks, investment strategies, retirement planning, and integration of data from heterogenous sources. Integrating these data into a cohesive ontology can be challenging. This research work leverages on semantic ontology as an approach for structured representation of knowledge about concepts and their relationships, and applies it to analyze and optimize decision support for pension management. The proposed ontology presents a formal and explicit specification of concepts (classes), their attributes, and the relationships between them and provides a shared and standardized understanding of the domain;enabling precise communication and knowledge representation for decision-support. The ontology deploys computational frameworks and analytic models to assess and evaluate data, generate insights, predict future pension fund performance as well as assess risk exposure. The research adopts the Reasoner, SPARQL query and OWL Visualizer executed over Java IDE for modelling the ontology-driven analytics. The approach encapsulated and integrated semantic ontologies with analytical models to enhance the accuracy, contextuality, and comprehensiveness of analyses and decisions within pension systems.

RDF(S) repository privacy protection via inference control

The privacy protection of resource description framework （schema） （RDF（S） ） repository is an emerging topic in database security area. In this paper, entailment rules are investigated based on RDF（S） repository firstly. Then, an idea that uses reasoning closure to judge whether the privacy disclosure caused by inference is existed is proposed. Furthermore, the definitions of impli- cation conditions and information measure of triple statements which gains data hiding algorithm with combining proposition logic reasoning theory are introduced. Meanwhile, a conversion method from conjunctive normal form to disjunctive normal form based minimal hitting sets of set cluster is aiso proposed. Finally, the experimental results show that our algorithm can prevent privacy disclosure of RDF（S） repository effectively.

Monitoring of Unaccounted for Gas in Energy Domain Using Semantic Web Technologies

Smart Urbanization has increased tremendously over the last few years,and this has exacerbated problems in all areas of life,especially in the energy sector.The Internet of Things(IoT)is providing effective solutions in gas distribution,transmission and billing through very sophisticated sensory devices and software.Billions of heterogeneous devices link to each other in smart urbanization,and this has led to the Semantic interoperability(SI)problem between the connected devices.In the energy field,such as electricity and gas,several devices are interlinked.These devices are competent for their specific operational role but unable to communicate across the operational units as required for accounting and monitoring of gas losses due to heterogeneity in device communication standards.To overcome this problem,we have proposed a model and ontology by applying semantic web technologies and cloud storage to address the tracking of customers to observe Unaccounted for gas(UFG)in the gas domain of energy.Semantization is achieved by replicating heterogeneous devices Sensor Model Language(SenML)data into Resource description framework(RDF)without human interventions.As semantic interoperability is used to efficiently and meaningfully share the information from one location to another.Therefore,the proposed ontology and model focus more efficiently on customer tracking,forecasting,and monitoring to detect UFG in gas networks.This also helps to save Gas Companies from financial gas losses.

Towards Efficient SPARQL Query Processing on RDF Data

Efficient support for querying large-scale resource description framework （RDF） triples plays an important role in semantic web data management. This paper presents an efficient RDF query engine to evaluate SPARQL queries, where the inverted index structure is employed for indexing the RDF triples. A set of operators on the inverted index was developed for query optimization and evaluation. Then a main-tree-shaped optimization algorithm was developed that transforms a SPARQL query graph into the op-timal query plan by effectively reducing the search space to determine the optimal joining order. The opti-mization collects a set of RDF statistics for estimating the execution cost of the query plan. Finally the opti-mal query plan is evaluated using the defined operators for answering the given SPARQL query. Extensive tests were conducted on both synthetic and real datasets containing up to 100 million triples to evaluate this approach with the results showing that this approach can answer most queries within 1 s and is extremely efficient and scalable in comparison with previous best state-of-the-art RDF stores.

Exposing vocabularies for soil as Linked Open Data

Standards to describe soil properties are well established,with many ISO specifications and a few international thesauri available for specific applications.Besides,in recent years,the European directive on "Infrastructure for Spatial Information in the European Community(INSPIRE)"has brought together most of the existing standards into a well defined model.However,the adoption of these standards so far has not reached the level of semantic interoperability,defined in the paper,which would facilitate the building of data services that reuse and combine data from different sources.This paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply Linked Data technologies to existing standards and data in order to improve the interoperability of soil datasets.The main result of this work is twofold.First,an RDF vocabulary for soil concepts based on the UML INSPIRE model was published.Second,a KOS(Knowledge Organization System)for soil data was published and mapped to existing relevant KOS,based on the analysis of the SISI database of the CREA of Italy.This work also has a methodological value,in that it proposes and applies a methodology to standardize metadata used in local scientific databases,a very common situation in the scientific domain.Finally,this work aims at contributing towards a wider adoption of the INSPIRE directive,by providing an RDF version of it.

An Effective Discrete Artificial Bee Colony Based SPARQL Query Path Optimization by Reordering Triples

Semantic Web has emerged to make web content machine-readable,and with the rapid increase in the number of web pages,its importance has increased.Resource description framework(RDF)is a special data graph format where Semantic Web data are stored and it can be queried by SPARQL query language.The challenge is to find the optimal query order that results in the shortest period of time.In this paper,the discrete Artificial Bee Colony(dABCSPARQL)algorithm is proposed,based on a novel heuristic approach,namely reordering SPARQL queries.The processing time of queries with different shapes and sizes is minimized using the dABCSPARQL algorithm.The performance of the proposed method is evaluated on chain,star,cyclic,and chain-star queries of different sizes from the Lehigh University Benchmark(LUBM)dataset.The results obtained by the proposed method are compared with those of ARQ(a SPARQL processor for Jena)query engine,the Ant System,the Elitist Ant System,and MAX-MIN Ant System algorithms.The experiments demonstrate that the proposed method significantly reduces the processing time,and in most queries,the reduction rate is higher compared with other optimization methods.