An Exercise in Kidney Factomics: From Article Titles to RDF Knowledge Base

abduction action activation/inhibition ageing animal cognition artificial life aspect attention autism automatism belief cate gorization causality, mental causation cognitive development cognitive psychiatry cognitivism communication competence, performance complexity computational analysis concept connectionism consciousness constrai nt constructivism context and situation control counterfactual creativity desire d ifferentiation discourse distributed intelligence domain specificity dualism, monism emergence emotion epiphenomenalism epistemic epistemology experience explanation expressiveness ex ternalism, internalism foetal cognition frame problem function functional neuroimaging functionalism fuzzy goal-directed behaviour grammar holism identity individualism infa nt cognition information inheritance intentionality inte raction interpretation introduction (of Dictionary) introspection knowledge acquisition knowledge base language language of thought learning lexicon localization of function logic logicism, psychologism meaning memory mental imagery metacognition mind modality model modularity morphology naturalization neural Darwinism neural network neuropsychology normativity number object or al pattern recognition perception phenomenalism physicalism prag matics problem solving propositional attitude propositional format psychophysics qualia qualitative physics reading realism reasoning and rationality reductionism relevance representational r obotics schizophrenia semantic case, thematic relation semantic network semantics semiotics sense sense, reference sign social cognition space subdoxastic supervenience symbol syn tax text theory of mind time and tense truth Turing machine type, token validation will writing

Italian has five epistemic forms that can be considered as grammaticalized, at least to an extent: the epistemic future, the indicative and the conditional forms of dovere (MUST); the indicative and the conditional forms of potere (CAN). This article studies the semantic oppositions between such forms, with a twofold objective: to describe the internal structure of the epistemic domain in Italian and to characterize it typologically. The Italian epistemic domain appears to be structured around three axes: the gradation of certainty, the opposition between preminently deontic and preminently epistemic meanings, the opposition between epistemic/evidential and genuinely epistemic meanings. The structure of Italian epistemic domain is characterized according to four typological parameters relevant for the semantic classification of epistemic systems. This typological characterization highlights the peculiar complexity of the relationship between epistemicity and evidentiality in Italian, and suggests interesting differences between Italian and other European languages

How should problem-solving representations for complex knowledge domains be designed? Traditional approaches typically address the problem of semantic complexity by designing systems that offer multiple and often heterogonous forms of representation. The REEP approach advocates structure preserving integration of the different classes and perspectives of a domain within a single representation. This work reports on a novel representational system for nurse rostering that was designed under the REEP approach. An empirical evaluation suggests the kinds of knowledge support provided by the representation and demonstrates that participants prefer fully integrated over selective views of information even though the former increases visual complexity. This knowledge support is explained in terms of more abstract domain independent cognitive benefits that we present as reasons for adopting the REEP approach.

We derive an adaptive solver for random elliptic boundary value problems, using techniques from adaptive wavelet methods. Substituting wavelets by polynomials of the random parameters leads to a modular solver for the parameter dependence, which combines with any discretization on the spatial domain. We show optimality properties of this solver, and present numerical computations. Introduction Stochastic Galerkin methods have emerged in the past decade as an efficient solution procedure for boundary value problems depending on random data, see [DBO01, XK02, BTZ04, WK05, MK05, FST05, WK06, TS07, BS09, BAS10]. These methods approximate the random solution by a Galerkin projection onto a finite dimensional space of random fields. This requires the solution of a single coupled system of deterministic equations for the coefficients of the Galerkin projection with respect to a predefined set of basis functions on the parameter domain. A major remaining obstacle is the construction of suitable spaces in which to compute approximate solutions. These should be adapted to the stochastic structure of the equation. Simple tensor product constructions are infeasible due to the high dimensionality of the parameter domain in case of input random fields with low regularity. Parallel to but independently from the development of stochastic Galerkin methods, a new class of adaptive methods has emerged, which are set not in the continuous framework of a boundary value problem, but rather on the level of coefficients with respect to a hierarchic Riesz basis, such as a wavelet basis. Due to the norm equivalences constitutive of Riesz bases, errors and residuals in appropriate sequence spaces are equivalent to those in physically meaningful function spaces. This permits adaptive wavelet methods to be applied directly to a large class of equations, provided that a suitable Riesz basis is available. For symmetric elliptic problems, the error of the Galerkin projection onto the span of a set of coefficients can be estimated using a sufficiently accurate approximation of the ∗Research supported in part by the Swiss National Science Foundation grant No. 200021-120290/1.

We study the specification of access control policy in large-scale distributed systems. We present Cassandra, a language and system for expressing policy, and the results of a substantial case study, a security policy for a national electronic health record system, based on the requirements for the ongoing UK National Health Service procurement exercise. Cassandra policies are expressed in a language based on Datalog with constraints. The expressiveness of the language (and its computational complexity) can be tuned by choosing an appropriate constraint domain. Cassandra is role-based; it supports credential-based access control (e.g. between administrative domains); and rules can refer to remote policies (for automatic credential retrieval and trust negotiation). Moreover, the policy language is small, and it has a formal semantics for query evaluation and for the access control engine. For the case study we choose a constraint domain C/sub 0/ that is sufficiently expressive to encode many policy idioms. The case study turns out to require many subtle variants of these; it is important to express this variety smoothly, rather than add them as ad hoc features. By ensuring only a constraint compact fragment of C/sub 0/ is used, we guarantee a finite and computable fixed-point model. We use a top-down evaluation algorithm, for efficiency and to guarantee termination. The case study (with some 310 rules and 58 roles) demonstrates that this language is expressive enough for a real-world application; preliminary results suggest that the performance should be acceptable.

Quickly accessing the contents of a video is challenging for users, particularly for unstructured video, which contains no intentional shot boundaries, no chapters, and no apparent edited format. We approach this problem in the domain of lecture videos though the use of machine learning, to gather semantic information about the videos; and through user interface design, to enable users to fully utilize this new information. First, we use machine learning techniques to gather the semantic information. We develop a system for rapid automatic semantic tagging using a heuristic-based feature selection algorithm called Sort-Merge, by using large initial heterogeneous low-level feature sets (cardinality greater than 1K). We explore applying Sort-Merge to heterogeneous feature sets though two methods: early fusion and late fusion. Each takes different approaches to handling the different kinds of features in the heterogeneous set. We determine the most predictive feature sets for key-frame filters such as “has text”, “has computer source code”, or “has instructor motion”. Specifically we explore the usefulness of Harr Wavelets, Fast Fourier Transforms, Color Coherence Vectors, Line Detectors, Ink Features and Pan/Tilt/Zoom detectors. For evaluation, we introduce a “keeper” heuristic for feature sets, which provides a method of performance comparison against a baseline. Second, we create a user interface to allow the user to make use of the semantic tags we gathered though our computer vision and machine learning process. The interface is integrated into an existing video browser, which detected shot-like boundaries and presented a multitimeline view. The content within shot-like boundaries is represented by frames to which our new interface applies the generated semantic tags. Specifically, we make accessible the semantic concepts of 'text', 'code', 'presenter', and 'person motion.' The tags are detected in the simulated shots using the filters generated with our machine learning approach and are displayed to users using a user-customizable multi-timeline view. We also generate tags based on ASRgenerated transcripts that have been limited to the words provided in the index of the course text book. Each of these occurrences is aligned with the simulated shots. Each spoken word becomes a tag analogous to the visual concepts. A full Boolean algebra over the tags is provided to enable new composite tags such as 'text or code, but no presenter.' Finally, we quantify the effectiveness of our features and our browser through user studies, both observational and task driven. We find that users that use the full suite of tools performed a search task in 60% of the time of users without access to tags. We find that when users are asked to perform search tasks they follow a nearly fixed pattern of accesses, alternating between the use of tags and Keyframes, or between the use of Word Bubbles and the media player. Based on user behavior and feedback, we redesigned the interface to group spatially interface components that are used together, removed un-used components, and redesigned the display of Word Bubbles to match that of the Visual Tags. We found that users strongly preferred the Keyframe tool, as well as both kinds of tags. Users also either found the algebra very useful or not useful at all.

In this paper, we consider the role of semantics in the spoken dialogue translation systems. We begin by looking at some of the key properties of an existing spoken dialogue system, namely the sundial system which provides flight and train information over the telephone, and how these properties affect the design methodology and functionality of spoken translation systems. These properties include the effects of speech processing, designing the system to meet the needs of users, and an analysis model which clearly separates the linguistic, conceptual, pragmatic and task levels. In this model many task functionalities are dependent upon, and sometimes realizable by, the semantic and pragmatic analysis components. Central to this approach, is the use of underspecified semantic representations which are further specified as and when required by domain and/or task analysis. This model can be applied in the development of spoken translation systems with two important effects: monolingual semantic and pragmatic analysis can be carried out by processes independent of, but correlated with, the (translation) function of the system; and the main functions of the transfer processes is to further specifying the representations for the target language and to deal with mismatches between source and target language representations. We illustrate this approach with semantic analyses of German utterances required for translation in the verbmobil spoken dialogue translation system.

Real-world use of RDF requires the ability to transparently represent and explain metadata associated with RDF triples. For example, when RDF triples are extracted automatically by information extraction programs, there is a need to represent where the triples came from, what their temporal validity is, and how certain we are that the triple is correct. Today, there is no theoretically clean and practically scalable mechanism that spans these different needs - reification is the only solution propose to date, and its implementations have been ugly. In this paper, we present Annotated RDF (or aRDF for short) in which RDF triples are annotated by members of a partially ordered set (with bottom element) that can be selected in any way desired by the user. We present a formal declarative semantics (model theory) for annotated RDF and develop algorithms to check consistency of aRDF theories and to answer queries to aRDF theories. We show that annotated RDF supports users who need to think about the uncertainty, temporal aspects, and provenance of the RDF triples in an RDF database. We develop a prototype aRDF implementation and show that our algorithms work efficiently even on real world data sets containing over 10 million triples.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented. RECENT ADVANCES in natural language processing [NLP] have generated considerable interest within the Artificial Intelligence [AI] and Cognitive Science communities. Within NLP, researchers are trying to produce intelligent computer systems that can read, understand, and respond to various human-oriented texts. Terrorism stories, airline flight schedules, and how to fill ice cube trays are all domains that have been used for NLP programs. In order to understand these texts and others, some way of representing information is needed. A complete understanding of human-oriented prose requires the ability to combine the meanings of many readings in an intelligent manner. Learning through the process of generalization is one such mechanism. The integration of representation and generalization in the domain of NLP is the subject of this article. Physical object understanding is an area in which a variety of representation schemes and generalization methods have been used. In past years, researchers have devised various representation systems for objects that range from very simple

In recent years, several large-scale knowledge bases (KBs) have been constructed, such as YAGO, DBpedia, and Google Knowledge Graph. Although automatic extractio techniques that extract facts and rules from the Web is necessary for constructing such large-scale KBs, incorporation of noisy, unreliable knowledge cannot be unavoidable. Thus, Google Knowledge Vault assigns extracted knowledge with confidence scores based on consistency with the existing KBs. In this paper, we propose a new approach for associating confidence scores with knowledge based on a large amount of raw data for domains, where there is no existing KB. We first construct knowledge in a specific domain as a semantic network, and then design a probabilistic network, that corresponds to the semantic network. To associate the confidence scores with the semantic network, we train the probabilistic network with a large amount of open data, provided by the Osaka central wholesale market in Japan. We also confirm the validity of the confidence scores with the accuracy of reasoning on the probabilistic network. A semantic network associated with confidence scores, that is, a weighted labeled graph is advantageous not only for reducing the noisy, unreliable knowledge with low confidence, but also for making retrieval results ranking on the KB. In the future, probabilistic reasoning on semantic networks may also be possible.

An increasing number of recent information retrieval systems make use of ontologies to help the users clarify their information needs and come up with semantic representations of documents A particular concern here is the integration of these semantic approaches with traditional search technology The research presented in this paper examines how ontologies can be efficiently applied to large-scale search systems for the web We describe how these systems can be enriched with adapted ontologies to provide both an in-depth understanding of the user's needs as well as an easy integration with standard vector-space retrieval systems The ontology concepts are adapted to the domain terminology by computing a feature vector for each concept Later, the feature vectors are used to enrich a provided query The whole retrieval system is under development as part of a larger Semantic Web standardization project for the Norwegian oil & gas sector.

Type has come to the fore as one of the primary organizing elements in the design of input forms suitable for the generation of high quality moving image metadata. A lack of semantic precision in both the definition and in the conceptual complexity of DC.Type's encoding scheme has prompted a re-evaluation of its usefulness as an element to be populated for interchange and discovery. In order to introduce precision to this element, a distinction is made between subject-based descriptors (genres), object based descriptors (forms), and manifestations or format-based descriptors (formats). A DCT2 vocabulary is proposed for DC.Type as a point of discussion for facilitating the deployment of domain specific encoding schemes and for filling gaps in the current list of terms.

We introduce the concept of perceptually meaningful image editing and present two techniques for manipulating the apparent depth of objects in an image. The user loads an image, selects an object and specifies whether the object should appear closer or further away. The system automatically determines target values for the object and/or background that achieve the desired depth change. These depth editing operations, based on techniques used by traditional artists, manipulate either the luminance or color temperature of different regions of the image. By performing blending in the gradient domain and reconstruction with a Poisson solver, the appearance of false edges is minimized. The results of a preliminary user study, designed to evaluate the effectiveness of these techniques, are also presented.

This article aims to present the new trends for university libraries in the domain of new technologies, investigation, information literacy in order to add value, competencies and empowerment to library professionals. Focus areas like semantic web, digital curation, information literacy, bibliometrics, altmetrics and open science are some of relevant new opportunities for librarians to develop and learn, if they want to be on this profession in the future and to help their users to rich better results. The integration of the IHMT Library in CGIC – Centro de Gestao de Informacao e Conhecimento will be an unique opportunity to change and transform old services into a new renovated Centre offering new services, tailored for user centered needs and based on new top library trends.

An innovative electromagnetic model for the radar echoes received by a microwave altimeter is proposed: it is based on fractal models employed both to describe the natural surface and to evaluate the surface scattering behaviour. The scattering surface is modelled by means of the fractional Brownian model (fBm) process, whose realizations are obtained by using the band-limited Weierstrass-Mandelbrot (WM) function. The scattering process is evaluated by means of the Physical Optics solution specifically derived for fractal surfaces. Rationale to apply this solution to the evaluation (in time domain) of the received echoes is provided. Dependence of the return pulse shapes on surface topography at different roughness scales is highlighted: some relevant meaningful examples are presented. Simulation results are provided to show the potential of the proposed technique that could be the fundamental element to generate an atlas of expected returns from a given radar altimeter.