Decision-making is one of the critical activities ofmanagement in business.However,decision support systems that support management decision-making activities lack the semantics involved in responding to semantic quer...Decision-making is one of the critical activities ofmanagement in business.However,decision support systems that support management decision-making activities lack the semantics involved in responding to semantic queries involving reasoning.We consider an ontology-based knowledge base that covers linked data about organizations,people and activities in a supply chain.In this paper,we explore how to effectively answer semantic queries to support management decision-making,where custom rules are employed for answering qualitative queries.We explore the ontological data representation and a similarity measure for data integration.We present a hybrid reasoning algorithm for answering qualitative queries.This algorithm adapts the reasoner such that,when proving a goal,it does a simple retrieval when it encounters trusted items,and backward-chaining over untrusted items.We provide a case study and evaluate the hybrid reasoning algorithm on scalability,query processing time and support for management decision-making.展开更多
While the FAIR Principles do not specify a technical solution for'FAIRness',it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplif...While the FAIR Principles do not specify a technical solution for'FAIRness',it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources.The FAIR Data Point is a metadata repository that follows the DCAT(2)schema,and utilizes the Linked Data Platform to manage the hierarchical metadata layers as LDP Containers.There has been a recent flurry of development activity around the FAIR Data Point that has significantly improved its power and ease-of-use.Here we describe five specific tools—an installer,a loader,two Webbased interfaces,and an indexer-aimed at maximizing the uptake and utility of the FAIR Data Point.展开更多
Privacy preservation has recently received considerable attention for location-based mobile services. A lot of location cloaking approaches focus on identity and location protection, but few algorithms pay attention t...Privacy preservation has recently received considerable attention for location-based mobile services. A lot of location cloaking approaches focus on identity and location protection, but few algorithms pay attention to prevent sensitive information disclosure using query semantics. In terms of personalized privacy requirements, all queries in a cloaking set, from some user＇s point of view, are sensitive. These users regard the privacy is breached. This attack is called as the sensitivity homogeneity attack. We show that none of the existing location cloaking approaches can effectively resolve this problem over road networks. We propose a （K, L, P）-anonymity model and a personalized privacy protection cloaking algorithm over road networks, aiming at protecting the identity, location and sensitive information for each user. The main idea of our method is first to partition users into different groups as anonymity requirements. Then, unsafe groups are adjusted by inserting relaxed conservative users considering sensitivity requirements. Finally, segments covered by each group are published to protect location information. The efficiency and effectiveness of the method are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against sensitivity homogeneity attacks is small.展开更多
文摘Decision-making is one of the critical activities ofmanagement in business.However,decision support systems that support management decision-making activities lack the semantics involved in responding to semantic queries involving reasoning.We consider an ontology-based knowledge base that covers linked data about organizations,people and activities in a supply chain.In this paper,we explore how to effectively answer semantic queries to support management decision-making,where custom rules are employed for answering qualitative queries.We explore the ontological data representation and a similarity measure for data integration.We present a hybrid reasoning algorithm for answering qualitative queries.This algorithm adapts the reasoner such that,when proving a goal,it does a simple retrieval when it encounters trusted items,and backward-chaining over untrusted items.We provide a case study and evaluate the hybrid reasoning algorithm on scalability,query processing time and support for management decision-making.
基金supported by Czech Technical University in Prague grant No.SGS20/209/OHK3/3T/18.LOBSS,RK and KB are partially funded by funding from the Horizon2020 projects FAIRsFAIR grant No.831558.
文摘While the FAIR Principles do not specify a technical solution for'FAIRness',it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources.The FAIR Data Point is a metadata repository that follows the DCAT(2)schema,and utilizes the Linked Data Platform to manage the hierarchical metadata layers as LDP Containers.There has been a recent flurry of development activity around the FAIR Data Point that has significantly improved its power and ease-of-use.Here we describe five specific tools—an installer,a loader,two Webbased interfaces,and an indexer-aimed at maximizing the uptake and utility of the FAIR Data Point.
文摘Privacy preservation has recently received considerable attention for location-based mobile services. A lot of location cloaking approaches focus on identity and location protection, but few algorithms pay attention to prevent sensitive information disclosure using query semantics. In terms of personalized privacy requirements, all queries in a cloaking set, from some user＇s point of view, are sensitive. These users regard the privacy is breached. This attack is called as the sensitivity homogeneity attack. We show that none of the existing location cloaking approaches can effectively resolve this problem over road networks. We propose a （K, L, P）-anonymity model and a personalized privacy protection cloaking algorithm over road networks, aiming at protecting the identity, location and sensitive information for each user. The main idea of our method is first to partition users into different groups as anonymity requirements. Then, unsafe groups are adjusted by inserting relaxed conservative users considering sensitivity requirements. Finally, segments covered by each group are published to protect location information. The efficiency and effectiveness of the method are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against sensitivity homogeneity attacks is small.
