Database Creation for Information Processing Methods, Metrics, and Models (DCIPM3)

Information seeking during the COVID-19 pandemic: Application of the risk information seeking and processing model

Using national online panel survey data (N = 1,000) in South Korea, this study applied the Risk Information Seeking and Processing (RISP) model to examine how people are motivated to seek information about obesity. We added autonomous motivation and health consciousness to the RISP model to investigate their association with health information seeking behavior about obesity. We proposed that information seeking is influenced by information insufficiency, autonomous motivation that is associated with health consciousness, and negative affective response that is related to subjective norm and risk perception. Using structural equation modeling analysis, this study largely supported our hypotheses. The results revealed associations among the socio-psychological variables that predict information seeking behavior on obesity. We found that information insufficiency, autonomous motivation, and negative affective responses appeared to be strong predictors in seeking obesity-related information. These findings have practical implications for health communication efforts in an emerging obesity risk country.

This study applies the risk information seeking and processing (RISP) model to examine how information processing influences individuals’ support for climate change mitigation policy. Perceived issue salience, attitude toward climate information, and systematic processing are positively related to policy support, whereas heuristic processing is not a significant predictor. The direct and indirect relationships illustrated in the model help expand the utility of the risk information seeking and processing model to address policy support. From an applied perspective, this study suggests important pathways to encourage systematic processing of information related to climate change, which may lead to increased public support for mitigation policies.

Resource Description Framework (RDF) is a semantic web specification that aims to support conceptual modeling of information about resources in the form of a triples of facts. In this paper, we note that, although RDF provides mechanisms to encode meta-information (such as source, trust, or certainty) about facts recorded in the knowledge base, existing RDF query languages and RDF stores fail to support key primitives needed in a large class of knowledge applications which associate utilities or costs on the available knowledge statements. To address this shortcoming, we propose a novel R2DF framework for utility ranked resource descriptions. We first propose a simple ranked RDF (R2DF) specification to enhance RDF triples with an application specific weight (e.g. cost). We then propose a SPARankQL query language specification, which includes a set of novel primitives on top of the SPARQL language to express top-k queries using traditional query patterns as well as novel flexible path predicates. An extended query processor engine, AR2Q, leverages novel index structures to support efficient ranked path search and includes query optimization strategies based on two key metrics: (a) proximity and (b) sub-result inter-arrival time. Experiments show that the use of these two metrics has significant impacts on the performance of top-k queries over R2DF graphs: in particular, the proximity measure helps reduce the number of path matches that need to be considered, whereas the inter-arrival measure reduces the overall execution time significantly especially when used along with proximity. The proposed strategies help obtain query plans close to optimal.

The ontology based framework is developed for representing image domain. The textual features of images are extracted and annotated as the part of the ontology. The ontology is represented in Web Ontology Language (OWL) format which is based on Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). Internally, the RDF statements represent an RDF graph which provides the way to represent the image data in a semantic manner. Various tools and languages are used to retrieve the semantically relevant textual data from ontology model. The SPARQL query language is more popular methods to retrieve the textual data stored in the ontology. The text or keyword based search is not adequate for retrieving images. The end users are not able to convey the visual features of an image in SPARQL query form. Moreover, the SPARQL query provides more accurate results by traversing through RDF graph. The relevant images cannot be retrieved by one to one mapping. So the relevancy can be provided by some kind of onto mapping. The relevancy is achieved by applying a decision tree algorithm. This study proposes methods to retrieve the images from ontology and compare the image retrieval performance by using SPARQL query language, decision tree algorithm and Lire which is an open source image search engine. The SPARQL query language is used to retrieving the semantically relevant images using keyword based annotation and the decision tree algorithms are used in retrieving the relevant images using visual features of an image. Lastly, the image retrieval efficiency is compared and graph is plotted to indicate the efficiency of the system.

Chapter 22 - Self-Regulation Interventions with a Focus on Learning Strategies

Extending the social information processing perspective: New links to attitudes, behaviors, and perceptions

The Internet of Things is a vast and growing virtual universe that includes medical devices, automobiles, industrial systems and a variable number of consumer electronics devices. With some 100 billion devices expected to be connected to the Internet by 2025, providing interoperability among the things on the IoT is one of the most fundamental requirements to support tracking, object addressing and discovery as well as information representation, storage, and exchange. Applying semantic techniques to IoT can support Interoperability, effective data access, integration resource discovery reasoning and processing of data knowledge extraction. In this paper, we present an overview of semantic technologies such as Semantic Web of Things (SWoT), Ontologies, Resource Description Framework (RDF), RDF Schema, Web Ontology Language (OWL), SPARQL Protocol And RDF Query Language (SPARQL), Semantic annotation and reasoning widely deployed in IoT.

The implementation of the European integrated marine policy poses many scientific challenges. Among them, the knowledge and understanding of interactions between anthropogenic pressures and ecological components is an important issue, particularly to help define Good Environmental Status, environmental targets and monitoring programs of the Marine Strategy Framework Directive (2008, MSFD). Assessment of cumulative effects of different pressures is a particularly complex issue requiring modeling tools and methods, as well as accurate data sets on human activities, anthropogenic pressures and ecological components. The results of these assessments are also uncertain and highly dependent on the calculation methods and assumptions, as well as on the data sets used. Within this context, we developed a technical and methodological approach to map the risk of cumulative effects of different pressures on benthic habitats. These developments were initiated as part of the implementation of the MSFD in France to contribute to the diagnosis of the marine environment. Here we provide a demonstrator to illustrate the feasibility for mapping the risk of cumulative effects of different pressures on benthic habitats, as well as the confidence index and the variability associated with this analysis. The method is based on a spatial analysis using a mapping of benthic habitats and their sensitivity to pressures, as well as the distribution and intensity of human activities and associated pressures. We collected and prepared relatively accurate and consistent data sets to describe human activities and benthic habitats. Data sets are embedded into a grid that facilitates the management and analysis of the data and exploitation of the results. The demonstrator consists of a relational database using the Spatial Query Language (SQL) language as well as data analysis scripts using the R language. The first demonstrator operations validated the main methodological and technical choices and helped to identify future developments needed to facilitate the appropriation and integration of these approaches in the implementation of public policies for the management of the marine environment.\n

What nucleic acids cannot do: a model for information processing in protein molecules

Modern information technology makes it possible to redesign the ways people work. In the future, machines can carry out intelligence-requiring tasks, which previously were done by people. It is thus good to develop methodologies for designing intelligent systems. An example of such methods is cognitive mimetics, i.e. imitating human information processing. Today, machines cannot by themselves navigate in archipelagos. However, the fact that people can take care of ship steering and navigation means that there is an information process, which makes it possible to navigate ships. This information process takes place inside the minds of navigating people. If we are able to explicate the information processing in the navigator’s mind, the knowledge of it can be used in designing intelligent machines. Replicating physical objects and industrial processes by means of digital computers is called digital twinning. Digital twins (DTs), which are digital replicas of physical systems and processes, have recently become tools for working with complex industrial processes. A crucial question for DTs is should human actions be added to them? As the answer is positive, such models of human information processing can be called human digital twins (HDTs). The knowledge of human tacit and explicit information processes can be represented by human digital twins. Models can be used in the search for a deeper understanding of human intelligent information processes. Human digital twins can thus be used as methodological tools in cognitive mimetics. In our present study, we modeled paper machine operators’ thinking. Specifically, we developed an ideal-exception-correction (IEC) model for paper operators’ control logic. The model illustrates how research and HDT-modeling can be used for explicating the subconscious or tacit information processing of people for the design of intelligent systems. In this article a model for design processes using cognitive modelling will be suggested. The concepts of cognitive mimetics and human digital twins enable us to outline a model for using the long tradition of simulating human thinking as a tool in designing intelligent systems.

This chapter accompanies the lecture on SPARQL with entailment regimes at the 7th Reasoning Web Summer School in Galway, Ireland, 2011. SPARQL is a query language and protocol for data specified in the Resource Description Format (RDF). The basic evaluation mechanism for SPARQL queries is based on subgraph matching. The query criteria are given in the form of RDF triples possibly with variables in place of the subject, object, or predicate of a triple, called basic graph patterns. Each instantiation of the variables that yields a subgraph of the queried RDF graph constitutes a solution. The query language further contains capabilities for querying for optional basic graph patterns, alternative graph patterns etc. We first introduce the main features of SPARQL as a query language. In order to define the semantics of a query, we show how a query can be translated to an abstract query, which can then be evaluated according to SPARQL's query evaluation mechanism. Apart from the features of SPARQL 1.0, we also briefly introduce the new features of SPARQL 1.1, which is currently being developed by the Data Access Working Group of the World Wide Web Consortium. In the second part of these notes, we introduce SPARQL's extension point for basic graph pattern matching. We illustrate how this extension point can be used to define a semantics for basic graph pattern evaluation based on more elaborate semantics such as RDF Schema (RDFS) entailment or OWL entailment. This allows for solutions to a query that implicitly follow from an RDF graph, but which are not necessarily explicitly present. We illustrate what constitutes an extension point and how problems that arise from using a semantic entailment relation can be addressed. We first introduce SPARQL in combination with the RDFS entailment relation and then move on to the more expressive Web Ontology Language OWL. We cover OWL's Direct Semantics, which is based on Description Logics, and the RDF-Based Semantics, which is an extension of the RDFS semantics. For the RDF-Based Semantics we mainly focus on the OWL 2 RL profile, which allows for an efficient implementation using rule engines. We assume that readers have a basic knowledge of RDF and Turtle, which we use in examples. For the OWL parts, we assume some background in OWL or Description Logics (see lecture notes Foundations of Description Logics). The examples for the OWL part are given in Turtle, OWL's functional-style syntax and Description Logics syntax. Although the inferences that are relevant for the example queries are explained, a basic idea about OWL's modeling constructs and their semantics are certainly helpful.

Bovine tuberculosis (bTB) is an infectious, zoonotic disease caused by Mycobacterium bovis that can spread between domestic and wild animals, as well as to humans. The disease is characterized by the progressive development of lesions that compromise the victim's lungs and lymph system. The disease was first identified in northwest Minnesota in both cattle and white-tailed deer (Odocoileus virginianus) in 2005. Due to its risks to human and animal health, bTB has numerous implications related to population management, policy outcomes, stakeholder relations, and economic impacts. When dealing with complicated risks, like bTB, individuals often seek out and process information as a method to learn about, and cope, with the risk. We developed a questionnaire that adapted components of the Risk Information Seeking and Processing (RISP) model and surveyed northwest Minnesota deer hunters. Our objectives were to better understand how stakeholders perceive and act on information regarding disease management in wildlife and to understand the utility of the RISP model for such management contexts. We drew a random proportional sample of licensed deer hunters (n = 2100) from the area affected by bTB and conducted a multi-contact mail survey. We found that 43% of the variability in the information-seeking behaviors of respondents was explained by demographics, hunting importance, personal risk perceptions, attitudes, and subjective norms. However, these results are largely attributable to the factors in the RISP model encompassed by components of the Theory of Planned Behavior (i.e., attitudes, subjective norms, perceived behavioral control, and behavioral intentions). This information can help managers contextualize individuals' perceived risks to better frame communication efforts to address stakeholder concerns and develop best practices for disease communication. While the state of Minnesota is currently considered free of bTB, future outbreaks remain possible in Minnesota and elsewhere. Understanding the key factors in the processes through which deer hunters seek out information pertaining to the disease can help managers collect the data necessary to aid decisions about desired future management outcomes. In addition, testing RISP model performance in applied research improves its future use across a broad spectrum of topics throughout veterinary disease management.

In the last few decades, the number of people living in fire-prone ecosystems has increased, placing more people and private property at risk to future fire events. Substantial research has demonstrated consistent public support for the use of prescribed fires in fuel-reduction efforts; however, continuing public concern regarding smoke emissions and negative air quality impacts exists. To date, limited research has specifically examined public attitudes toward smoke emissions. In this study, we use a mail-back or internet survey to assess citizen information seeking behaviors regarding smoke emissions in four communities in high fire risk areas. Path analysis was used to apply the risk information seeking and processing (RISP) model to examine factors that motivate people to seek information relevant to smoke emissions. We find that residents were concerned about smoke emissions and believed that they needed more information. Residents’ intentions to seek information were influenced by information (in)sufficiency, the number of sources used, and smoke acceptability, among other factors. Findings suggest that currently available information resources on smoke may not be sufficient to meet residents’ information needs, particularly for those most motivated to learn more about emissions.

Berners-Lee’s vision of the Semantic Web describes the idea of providing machine readable and processable information using key technologies such as ontologies and automated reasoning in order to create intelligent agents.The prospective amount of machine readable information available in the future will be large. Thus, heterogeneity and scalability will be central issues, rendering exhaustive searches and central storage of data infeasible. This paper presents a scalable peer-to-peer based approach to distributed querying of Semantic Web information that allows ordering of entries in result sets and limiting the size of result sets which is necessary to prevent results with millions of matches. The system relies on the graph-based W3C standard Resource Description Framework (RDF) for knowledge description. Thereby, it enables queries on large, distributed RDF graphs.KeywordsResource Description FrameworkDistribute Hash TableResource Description Framework DataResource Description Framework GraphResource Description Framework TripleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.