Knowledge Representations

A suite of standards known as the Semantic Web is transforming the Internet to a semantic graph rather than a graph of hypertext links. This paper will describe how various ideas and initiatives in artificial intelligence knowledge representation influenced its design. We begin with the seminal work by Alan Turing and Alonzo Church that led to the definition of Turing Machines, enabled digital computing, and provided the mathematical theory of computation which has been one of the determining factors for Artificial Intelligence knowledge representation. We then provide a brief history of artificial intelligence knowledge representation starting with groundbreaking researchers such as Newell and Simon, then to the first "AI boom" driven primarily by rule-based expert systems followed by major initiatives such as Cyc and the DARPA Knowledge Sharing Initiative. We will discuss how innovations from these initiatives affected standards that in turn led to the suite of standards known as the Semantic Web. We conclude with a brief overview of the most important issues currently facing those who wish to see widespread adoption of Semantic Web technology in industry.

Objective In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies. Materials and methods Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible. Discussion While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.

Scope & Topics
9 th International Conference on Artificial Intelligence & Applications (ARIA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[48]) are based on a purely con-nectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter Gärdenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by Gärdenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs.

Goal-Oriented Requirements Engineering (GORE) has grown into an important area of research in the past decades. Still, some of its corners remain dark, since different GORE languages do not provide a well-founded conceptualization of the domain and are not consensual. This may lead to ambiguous or weak understanding of GORE concepts. In this paper, we introduce the Goal-Oriented Requirements Ontology (GORO), a domain ontology founded on the Unified Foundational Ontol-ogy (UFO) that intends to represent the nature and relations of concepts surrounding the GORE domain. We use GORO to explore and clarify the semantics used, sometimes implicitly, by well-known GORE languages.

Kinship plays a fundamental role in human communities as a basic principle for organizing individuals into social groups. Representing kinship relationships in a formal and precise way is then a crucial task when modelling many knowledge domains, and it may constitute a relevant benchmark for the reasoning layer of the Semantic Web. In this paper we face the problem of representing some basic aspects of kinship relationships using the fragments of OWL2 corresponding to the description logics SROIQ and EL ++ . As a first step, we provide an intuitive but formal description of some kinship terms, using an expressive description logic. From this intuitive description, we derive a set of test cases, indicating the expected inferences that should be drawn from the candidate ontologies. Finally, three different ontologies (two for SROIQ and one for EL ++ ), whose coherence with the intuitive description of kinship terms is established, are compared by means of these test cases. 5 http://www.w3.org/standards/techs/rif#w3c_all 6 http://www.w3.org/TR/2009/REC-owl2-overview-20091027/

The purpose of this paper was to examine whether students' epistemic beliefs differed as a function of variations in procedural versus conceptual knowledge in statistics. Students completed Hofer's (Contem Edu Psychol 25:378-405, 2000) Discipline-Focused Epistemological Beliefs Questionnaire five times over the course of a semester. Differences were explored between students' initial beliefs about statistics knowledge and their specific beliefs about conceptual knowledge and procedural knowledge in statistics. Results revealed differences across these contexts; students' beliefs differed between procedural versus conceptual knowledge. Moreover, students' initial beliefs about statistics knowledge were more similar to their beliefs about conceptual knowledge rather than procedural knowledge. Finally, regression analyses revealed that students' beliefs about the justification of knowledge, attainability of truth and source of knowledge were significant predictors of examination performance, depending on the examination. These results have important theoretical, methodological and pedagogical implications.

Two approaches are taken here in an endeavor to discover natural definitions of knowledge and wisdom that are justifiable with respect to both theory and practice, using graph theory: (1) The metrics approach is to produce graphs that force an increase in various graph metrics, whereas (2) the dimensions approach is based on the observation that the graphical representation of aggelia in the DIKW hierarchy seems to increase in dimension with each step up the hierarchy. The dimensions method produces far more cogent definitions than the metrics method, so that is the set of definitions proposed, especially for use in artificial intelligence.

In recent years, there has been a growing interest in the application of foundational ontologies, i.e., formal ontological theories in the philosophical sense, to provide a theoretically sound foundation for improving the theory and practice of conceptual modeling and knowledge representation. This paper addresses one particular foundational theory of events termed UFO-B, which has been successfully employed as a reference model for addressing problems from complex media management, enterprise architecture, software engineering, and modeling of events in petroleum exploration. Despite that, there is still no formalization of UFO-B in a decidable knowledge representation language that could support reasoning about complex events and event relations. We address this gap by proposing a number of alternative translations from UFO-B's original axiomatization (in first-order logic and in the Alloy formal language) to the description logic SROIQ, which is the formal underpinning of OWL 2 DL. Additionally, to support practical applications, we translated these SROIQ theories to OWL 2 DL TBoxes, which were validated by showing that all the intended models of UFO-B (the logical models of the UFO-B specification in Alloy) that we generated are consistent with these UFO-B TBoxes.

The use of formal languages in the specification of upper ontologies helps establishing precise and unambiguous definitions for its concepts and relations. In this context, the use of formal languages with support of proof-assistant software systems has potential of aiding the specification author in various aspects. We describe a study case in which the Isabelle/HOL language and the Isabelle proof-assistant environment are used to formalize a simplified version of the UFO-A ontology of endurants, to verify and correct the original axiomatization, and to optimize the specification theory's axioms and signature.

This comparative study examines essays and audiovisual presentations made by students, using English as a foreign language, in a digital learning environment. The aim of the study is to describe and understand six Swedish upper secondary students’ digital written and audiovisual text production in English, with a specific focus on representation in different modes. The study uses a framework of multimodal social semiotics (Halliday, 1978; Kress, 2010; Kress & van Leeuwen, 1996/2006) and multimodal transcriptions as tools for processing and interpreting data. Analyses of the essays and audiovisual presentations involve form and content, and the study employs multimodal analysis to investigate affordances and constraints for making meaning and shaping knowledge, and visualizations of transduction of knowledge. Results show that students appear familiar with constructing written text, but have to create their own structures for audiovisual text, where the familiar logic for constructing written text is both a help and a hindrance. The medium of audiovisual text opens up for more engagement and emotions compared to written text. It has more means for structuring and creating cohesion than does written text, which becomes visible in how the students’ texts are transduced, and transformed, across essays and audiovisual presentations.

The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop and ...

Innovative research technologies in the neurosciences have remarkably improved the perception of brain structure and function. The use of several neurofeedback training techniques is broadly used for the memory and cognition augmentation as well as for several learning difficulties and AHDD rehabilitation. Author's objective is to review cognitive enhancement techniques with the use of brain imaging intervention methods as well to evaluate the effects of these methods in the educational process. The efficiency and limitations of neurofeedback training with the use of EEG brain imaging, HEG scanning, namely NIR and PIR method and fMRI scan including rt-fMRI brain scanning technique are also discussed. Moreover, technical and clinical details of several neurofeedback treatment approaches were also taken into consideration.

—In recent years, there has been a growth in the use of reference conceptual models, in general, and domain ontologies, in particular, to capture information about complex and critical domains. These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able to understand and reason using the models' content. In other words, it is important that conceptual models are cognitively tractable. However, it is unavoidable that when the information of the represented domain grows, so does the size and complexity of the artifacts and models that represent them. For this reason, more sophisticated techniques for complexity management in ontology-driven conceptual models, need to be developed. Some approaches are based on the notion of model modularization. In this paper, we follow the work on model modularization to present an approach for view extraction for the ontology-driven conceptual modeling language OntoUML. We provide a formal definition for ontological views over OntoUML conceptual models that completely leverages on the ontologically well-grounded real-world semantics of that language. Moreover, we present a plug-in tool, particularly developed for an OntoUML model-based editor that implements this formal view structure in terms of queries defined over the OntoUML metamodel embedded in that tool.

—Risk analysis is a complex and critical activity in various contexts, ranging from strategic planning to IT systems operation. Given its complexity, several Enterprise Architecture (EA) frameworks and modeling languages have been developed to help analysts in representing and analyzing risks. Yet, the notion of risk remains overloaded and conceptually unclear in most of them. In this paper, we investigate the real-world semantics underlying risk-related constructs in one of such approaches, namely ArchiMate's Risk and Security Overlay (RSO). We perform this investigation by means of ontological analysis to reveal semantic limitations in the overlay, such as ambiguity and missing constructs. Building on the results of this analysis, we propose a well-founded redesign of the risk modeling aspects of the RSO.

Using an eight-dimensional model for studying socio-technical systems, a multidisciplinary team of investigators identified barriers and facilitators to clinical decision support (CDS) implementation in a community setting, the Mid-Valley Independent Physicians Association in the Salem, Oregon area. The team used the Rapid Assessment Process, which included nine formal interviews with CDS stakeholders, and observation of 27 clinicians. The research team, which has studied 21 healthcare sites of various sizes over the past 12 years, believes this site is an excellent example of an organization which is using a commercially available electronic-health-record system with CDS well. The eight-dimensional model proved useful as an organizing structure for the evaluation.

EROSS (https://eris2020.inf.unibz.it/)

The KRDB Research Centre for Knowledge and Data at the Free University of Bozen-Bolzano, Italy, organizes the “ER Online Summer Seminars” (EROSS-2020), an interdisciplinary series of online seminars featuring world-class speakers from areas such as Ontology, Data Semantics, Artificial Intelligence, Software Engineering, Cognitive Sciences, and Logics, but with the ultimate goal of contributing to the theory and practice of the discipline of Conceptual Modelling.

EROSS is an event affiliated with the International Conference on Conceptual Modeling (ER), and it is organized in the context of the Bolzano Summer of Knowledge (BOSK-2020).

The talks will be hosted by Giancarlo Guizzardi and Enrico Franconi from the Conceptual and Cognitive Modeling Research Group – CORE, Knowledge Representation and Databases Center – KRDB, Free University of Bozen-Bolzano, Italy.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics subdisciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics subdisciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

The use of formal languages in the specification of upper ontologies helps establishing precise and unambiguous definitions for its concepts and relations. In this context, the use of formal languages with support of proof-assistant software systems has potential of aiding the specification author in various aspects. We describe a study case in which the Isabelle/HOL language and the Isabelle proof-assistant environment are used to formalize a simplified version of the UFO-A ontology of endurants, to verify and correct the original axiomatization, and to optimize the specification theory’s axioms and signature.

Advances in computing power and new computational techniques have changed the way researchers approach biology, medicine, and indeed all of science. In biomedicine, one of the most fruitful approaches has been to use software tools and knowledge resources known as ontologies-machineprocessable descriptions of scientific domains-that can promote the integration of disparate data sources. We have shown that such resources can enable data aggregation, improve search, and allow the detection of new associations that were previously not detectable. It is now possible to demonstrate computationally correlations among genes, diseases, treatments, and outcomes, to use these correlations to efficiently direct research into potentially fruitful areas, and to translate the insights from this research to the practice of medicine. Achieving these integrative analyses requires software systems that take advantage of the semantics of these areas and that can intelligently negotiate domains and knowledge sources, identifying commonality across systems that use different and conflicting vocabularies, while understanding apparent differences that may be concealed by the use of superficially similar terms. 1 An appropriate ontology provides the cornerstone of software for bridging systems, domains, and resources. 2 Ontologies are the foundation of all semantic technologies in e-science, and are a critical component of multi-disciplinary and translational research in biomedicine. The National Center for Biomedical Ontology (NCBO) has become a leading scientific organization for bringing semantic technology to biomedicine. With core performance sites at Stanford University, the Mayo Clinic, the University of Victoria, and the University at Buffalo, our team works to create and disseminate national infrastructure that supports the use of computer-stored knowledge in the form of ontologies.

The Arden Syntax is an HL7 standard language for representing medical knowledge as logic statements. Despite nearly 2 decades of availability, Arden Syntax has not been widely used. This has been attributed to the lack of a generally available compiler to implement the logic, to Arden's complex syntax, to the challenges of mapping local data to data references in the Medical Logic Modules (MLMs), or, more globally, to the general absence of decision support in healthcare computing. An XML representation (ArdenML) may partially address the technical challenges. MLMs created in ArdenML can be converted into executable files using standard transforms written in the Extensible Stylesheet Language Transformation (XSLT) language. As an example, we have demonstrated an approach to executing MLMs written in ArdenML using the Drools business rule management system. Extensions to ArdenML make it possible to generate a user interface through which an MLM developer can test for logical errors.

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.

Objective To develop an algorithm for the discovery of drug treatment patterns for endocrine breast cancer therapy within an electronic medical record and to test the hypothesis that information extracted using it is comparable to the information found by traditional methods. Materials The electronic medical charts of 1507 patients diagnosed with histologically confirmed primary invasive breast cancer. Methods The automatic drug treatment classification tool consisted of components for: (1) extraction of drug treatment-relevant information from clinical narratives using natural language processing (clinical Text Analysis and Knowledge Extraction System); (2) extraction of drug treatment data from an electronic prescribing system; (3) merging information to create a patient treatment timeline; and (4) final classification logic. Results Agreement between results from the algorithm and from a nurse abstractor is measured for categories: (0) no tamoxifen or aromatase inhibitor (AI) treatment; (1) tamoxifen only; (2) AI only; (3) tamoxifen before AI; (4) AI before tamoxifen; (5) multiple AIs and tamoxifen cycles in no specific order; and (6) no specific treatment dates. Specificity (all categories): 96.14%e100%; sensitivity (categories (0)e(4)): 90.27%e99.83%; sensitivity (categories (5)e(6)): 0e23.53%; positive predictive values: 80%e97.38%; negative predictive values: 96.91%e99.93%. Discussion Our approach illustrates a secondary use of the electronic medical record. The main challenge is event temporality. Conclusion We present an algorithm for automated treatment classification within an electronic medical record to combine information extracted through natural language processing with that extracted from structured databases. The algorithm has high specificity for all categories, high sensitivity for five categories, and low sensitivity for two categories.

Clinical research is the foundation for advancing the practice of medicine. However, the lack of seamless integration between clinical research and patient care workflow impedes recruitment efficiency, escalates research costs, and hence threatens the entire clinical research enterprise. Increased use of electronic health records (EHRs) holds promise for facilitating this integration but must surmount regulatory obstacles. Among the unintended consequences of current research oversight are barriers to accessing patient ...

The growing role of models across the life-cycle of enterprises, and their information and software systems, fuels the need for a more fundamental reflection on the foundations of modeling. Two of the core theories of the discipline of enterprise engineering (Factual Information (FI) theory and the Model Universe (MU) theory) aim at contributing to these foundations. The latest versions of the FI-and MU-theories have recently been published. Offering an analysis and criticism to them enables us to continue the important debate on the semiotic, ontological, and general philosophical foundations of domain modeling and enterprise modeling in particular. A core concept in the field of domain modeling is the conceptualization of the domain. In this paper, we specifically focus on the development of a deeper understanding of domain conceptualizations, while reflecting on the way this notion is positioned in the FI-and MU-theories.

The conduct of clinical and translational research regularly involves the use of a variety of heterogeneous and large-scale data resources. Scalable methods for the integrative analysis of such resources, particularly when attempting to leverage computable domain knowledge in order to generate actionable hypotheses in a highthroughput manner, remain an open area of research. In this report, we describe both a generalizable design pattern for such integrative knowledge-anchored hypothesis discovery operations and our experience in applying that design pattern in the experimental context of a set of driving research questions related to the publicly available Osteoarthritis Initiative data repository. We believe that this 'test bed' project and the lessons learned during its execution are both generalizable and representative of common clinical and translational research paradigms.

: A suite of standards known as the Semantic Web is transforming the Internet to a semantic graph rather than a graph of hypertext links. This paper will describe how various ideas and initiatives in artificial intelligence knowledge representation influenced its design. We begin with the seminal work by Alan Turing and Alonzo Church that led to the definition of Turing Machines, enabled digital computing, and provided the mathematical theory of computation which has been one of the determining factors for Artificial Intelligence knowledge representation. We then provide a brief history of artificial intelligence knowledge representation starting with groundbreaking researchers such as Newell and Simon, then to the first "AI boom" driven primarily by rule-based expert systems followed by major initiatives such as Cyc and the DARPA Knowledge Sharing Initiative. We will discuss how innovations from these initiatives affected standards that in turn led to the suite of standards known as the Semantic Web. We conclude with a brief overview of the most important issues currently facing those who wish to see widespread adoption of Semantic Web technology in industry.

Innovative research technologies in the neurosciences have remarkably improved the perception of brain structure and function. The use of several neurofeedback training techniques is broadly used for the memory and cognition augmentation as well as for several learning difficulties and AHDD rehabilitation. Author's objective is to review cognitive enhancement techniques with the use of brain imaging intervention methods as well to evaluate the effects of these methods in the educational process. The efficiency and limitations of neurofeedback training with the use of EEG brain imaging, HEG scanning, namely NIR and PIR method and fMRI scan including rt-fMRI brain scanning technique are also discussed. Moreover, technical and clinical details of several neurofeedback treatment approaches were also taken into consideration.

Goal-Oriented Requirements Engineering (GORE) has grown into an important area of research in the past decades. Still, some of its corners remain dark, since different GORE languages do not provide a well-founded conceptualization of the domain and are not consensual. This may lead to ambiguous or weak understanding of GORE concepts. In this paper, we introduce the Goal-Oriented Requirements Ontology (GORO), a domain ontology founded on the Unified Foundational Ontology (UFO) that intends to represent the nature and relations of concepts surrounding the GORE domain. We use GORO to explore and clarify the semantics used, sometimes implicitly, by well-known GORE languages.

Clinical research is the foundation for advancing the practice of medicine. However, the lack of seamless integration between clinical research and patient care workflow impedes recruitment efficiency, escalates research costs, and hence threatens the entire clinical research enterprise. Increased use of electronic health records (EHRs) holds promise for facilitating this integration but must surmount regulatory obstacles. Among the unintended consequences of current research oversight are barriers to accessing patient ...

The purpose of this paper was to examine whether students' epistemic beliefs differed as a function of variations in procedural versus conceptual knowledge in statistics. Students completed Hofer's (Contem Edu Psychol 25:378-405, 2000) Discipline-Focused Epistemological Beliefs Questionnaire five times over the course of a semester. Differences were explored between students' initial beliefs about statistics knowledge and their specific beliefs about conceptual knowledge and procedural knowledge in statistics. Results revealed differences across these contexts; students' beliefs differed between procedural versus conceptual knowledge. Moreover, students' initial beliefs about statistics knowledge were more similar to their beliefs about conceptual knowledge rather than procedural knowledge. Finally, regression analyses revealed that students' beliefs about the justification of knowledge, attainability of truth and source of knowledge were significant predictors of examination performance, depending on the examination. These results have important theoretical, methodological and pedagogical implications.

Usually, the problems in Artificial Intelligence may be many times related to Philosophy of Mind, and perhaps because this reason may be in essence very disputable. So, for instance, the famous question: Can a machine think? It was proposed by Alan Turing. And it may be the more decisive question, but for many people it would be a nonsense. So, two of the very fundamental and more confronted positions usually considered according this line include the Connectionism and the Computational Theory of Mind. We analyze here its content, with their past disputes, and current situation.

Objectives To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. Materials and Methods Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured.

The AMIA Public Health Informatics 2011 Conference brought together members of the public health and health informatics communities to revisit the national agenda developed at the AMIA Spring Congress in 2001, assess the progress that has been made in the past decade, and develop recommendations to further guide the field. Participants met in five discussion tracks: technical framework; research and evaluation; ethics; education, professional training, and workforce development; and sustainability. Participants identified 62 recommendations, which clustered into three key themes related to the need to (1) enhance communication and information sharing within the public health informatics community, (2) improve the consistency of public health informatics through common public health terminologies, rigorous evaluation methodologies, and competency-based training, and (3) promote effective coordination and leadership that will champion and drive the field forward. The agenda and recommendations from the meeting will be disseminated and discussed throughout the public health and informatics communities. Both communities stand to gain much by working together to use these recommendations to further advance the application of information technology to improve health.

This work aims to provide 3d interaction assistance in virtual environments depending on context. We designed and implemented a generic decision engine that can connect to our existing virtual reality applications through a set of tools. It uses an ontology and Conceptual Graphs (CGs) to represent knowledge, and First Order Logic to conduct semantic reasoning. Context information are gathered by virtual sensors in the application and interpreted by the engine. Multimodal assistance is provided by virtual actuators. Our first test scenario is about assistance to selection of objects or navigation towards objects: the engine automatically detects user's interests and manages adaptations depending on user's hand gestures, interactions history and type of task.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics subdisciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

Goal-Oriented Requirements Engineering (GORE) has grown into an important area of research in the past decades. Still, some of its corners remain dark, since different GORE languages do not provide a well-founded conceptualization of the domain and are not consensual. This may lead to ambiguous or weak understanding of GORE concepts. In this paper, we introduce the Goal-Oriented Requirements Ontology (GORO), a domain ontology founded on the Unified Foundational Ontology (UFO) that intends to represent the nature and relations of concepts surrounding the GORE domain. We use GORO to explore and clarify the semantics used, sometimes implicitly, by well-known GORE languages.

The AMIA Public Health Informatics 2011 Conference brought together members of the public health and health informatics communities to revisit the national agenda developed at the AMIA Spring Congress in 2001, assess the progress that has been made in the past decade, and develop recommendations to further guide the field. Participants met in five discussion tracks: technical framework; research and evaluation; ethics; education, professional training, and workforce development; and sustainability. Participants identified 62 recommendations, which clustered into three key themes related to the need to (1) enhance communication and information sharing within the public health informatics community, (2) improve the consistency of public health informatics through common public health terminologies, rigorous evaluation methodologies, and competency-based training, and (3) promote effective coordination and leadership that will champion and drive the field forward. The agenda and recommendations from the meeting will be disseminated and discussed throughout the public health and informatics communities. Both communities stand to gain much by working together to use these recommendations to further advance the application of information technology to improve health.

Objectives Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A ratelimiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. Materials and Methods Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. Results Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. Discussion These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. Conclusion Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.

The purpose of this paper was to examine whether students' epistemic beliefs differed as a function of variations in procedural versus conceptual knowledge in statistics. Students completed Hofer's (Contem Edu Psychol 25:378-405, 2000) Discipline-Focused Epistemological Beliefs Questionnaire five times over the course of a semester. Differences were explored between students' initial beliefs about statistics knowledge and their specific beliefs about conceptual knowledge and procedural knowledge in statistics. Results revealed differences across these contexts; students' beliefs differed between procedural versus conceptual knowledge. Moreover, students' initial beliefs about statistics knowledge were more similar to their beliefs about conceptual knowledge rather than procedural knowledge. Finally, regression analyses revealed that students' beliefs about the justification of knowledge, attainability of truth and source of knowledge were significant predictors of examination performance, depending on the examination. These results have important theoretical, methodological and pedagogical implications.

Objectives Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A ratelimiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. Materials and Methods Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models.

The AMIA Public Health Informatics 2011 Conference brought together members of the public health and health informatics communities to revisit the national agenda developed at the AMIA Spring Congress in 2001, assess the progress that has been made in the past decade, and develop recommendations to further guide the field. Participants met in five discussion tracks: technical framework; research and evaluation; ethics; education, professional training, and workforce development; and sustainability. Participants identified 62 recommendations, which clustered into three key themes related to the need to (1) enhance communication and information sharing within the public health informatics community, (2) improve the consistency of public health informatics through common public health terminologies, rigorous evaluation methodologies, and competency-based training, and (3) promote effective coordination and leadership that will champion and drive the field forward. The agenda and recommendations from the meeting will be disseminated and discussed throughout the public health and informatics communities. Both communities stand to gain much by working together to use these recommendations to further advance the application of information technology to improve health.

The Arkeotek project aims at building knowledge bases in the domain of the archaeology of techniques. These knowledge bases are made up of documents structured in data and interpretation rules, the latter being understood as inference operations performed to generate conclusions or interpretative hypotheses. Such structured documents are obtained through the logicist analysis, a 30-year old term given to an ensemble of research aiming at clarifying the mechanisms and foundations of the reasoning which organize our scientific constructs (GARDIN 2003). Nowadays, only logicism proposes an efficient methodology for extracting the reasonings contained in our scientific publications and therefore for building corpuses of inference rules. In this paper, we focus on the tools and resources designed for querying such corpuses: a domain ontology associated with a terminology, a semantic annotation tool as well a query tool. The originality of our approach is to support the corpus and domain knowledge evolution. The ultimate goal is to give the archaeologist the possibility to consult archaeological interpretations on specific subjects, as well as the foundations of these interpretations including data bases.