Deductive databases for computing certain and consistent answers from mediated data integration systems

We address the problem of retrieving certain and consistent answers to queries posed to a mediated data integration system under the local-as-view paradigm with open sources and conjunctive and disjunctive view definitions. For obtaining certain answers a query program is run under the cautious stable model semantics on top of a normal deductive database with choice operator that specifies the class of minimal legal instances of the integration system. This methodology works for all monotone Datalog queries. To compute answers to queries that are consistent with respect to given global integrity constraints, the specification of minimal legal instances is combined with another disjunctive deductive database that specifies the repairs of those legal instances. This allows to retrieve the answers to any Datalog^¬ query that are consistent with respect to global universal and referential integrity constraints.     

Logic-based XML data integration: a semi-materializing approach

We first describe the approach to XML data integration in the XPathLog/LoPiX project that uses a warehouse strategy. We show that the DOM model and the XML Query Data Model are not suitable for this task since the integrated database is not necessarily a tree, but a set of overlapping (original and integrated) trees. The problem is solved by using a node-labeled graph-based data model, called XTreeGraph, for the internal XML database that represents multiple, overlapping XML trees, or tree views.In the second part, we return to the standard XML data model—by still keeping the overlapping tree idea by “simulating” it: The data is internally represented by XML where the “overlayed” resulting tree is represented by XLink elements that refer to the original sources. By using a logical, transparent data model for XLinks as investigated in [May, in: 11th WWW Conference, 2002], all queries behave as stated against the XTreeGraph. The use of links for partial materialization also turns the approach from a warehouse approach into a mixed approach that combines the advantages of the warehouse approach and of the virtual approach. The approach is again illustrated by using XPathLog as data integration language.     

Rule-based schema matching for ontology-based mediators

Mediating heterogeneous data sources heavily relies on explicit domain knowledge expressed, for example, as ontologies and mapping rules. We discuss the use of logic representations for mapping schema elements onto concepts expressed in a simplified ontology for cultural assets. Starting with a logic representation of the ontology, criteria for a rule-based schema matching are exemplified. Special requirements are the handling of uncertain information and the processing of hierarchical XML structures representing instances.     

Question answering from structured knowledge sources

We present an implemented approach for domain-restricted question answering from structured knowledge sources, based on robust semantic analysis in a hybrid NLP system architecture. We perform question interpretation and answer extraction in an architecture that builds on a lexical-conceptual structure for question interpretation, which is interfaced with domain-specific concepts and properties in a structured knowledge base. Question interpretation involves a limited amount of domain-specific inferences, and accounts for higher-level quantificational questions. Question interpretation and answer extraction are modular components that interact in clearly defined ways. We derive so-called proto queries from the linguistic representations, which provide partial constraints for answer extraction from the underlying knowledge sources. The search queries we construct from proto queries effectively compute minimal spanning trees from the underlying knowledge sources. Our approach naturally extends to multilingual question answering, and has been developed as a prototype system for two application domains: the domain of Nobel prize winners, and the domain of Language Technology, on the basis of the large ontology underlying the information portal LT World.     

Neural-network models of learning and memory: leading questions and an emerging framework

Real-time neural-network models provide a conceptual framework for formulating questions about the nature of cognition, an architectural framework for mapping cognitive functions to brain regions, a semantic framework for defining terms, and a computational framework for testing hypotheses. This article considers key questions about how a physical system might simultaneously support one-trial learning and lifetime memories, in the context of neural models that test possible solutions to the problems posed. Model properties point to partial answers, and model limitations lead to new questions. Placing individual system components in the context of a unified real-time network allows analysis to move from the level of neural processes, including learning laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness.     

Retraction and Revocation in Agent Deliberation Dialogs

We present a generic denotational semantic framework for protocols for dialogs between rational and autonomous agents over action which allows for retraction and revocation of proposals for action. The semantic framework views participants in a deliberation dialog as jointly and incrementally manipulating the contents of shared spaces of action-intention tokens. The framework extends prior work by decoupling the identity of an agent who first articulates a proposal for action from the identity of any agent then empowered to retract or revoke the proposal, thereby permitting proposals, entreaties, commands, promises, etc., to be distinguished semantically.     

Conceptual Integration of Arithmetic Operations With Real‐World Knowledge: Evidence From Event‐Related Potentials

Research on language processing has shown that the disruption of conceptual integration gives rise to specific patterns of event‐related brain potentials (ERPs)—N400 and P600 effects. Here, we report similar ERP effects when adults performed cross‐domain conceptual integration of analogous semantic and mathematical relations. In a problem‐solving task, when participants generated labeled answers to semantically aligned and misaligned arithmetic problems (e.g., 6 roses + 2 tulips  = ? vs. 6 roses + 2 vases  = ?), the second object label in misaligned problems yielded an N400 effect for addition (but not division) problems. In a verification task, when participants judged arithmetically correct but semantically misaligned problem sentences to be “unacceptable,” the second object label in misaligned sentences elicited a P600 effect. Thus, depending on task constraints, misaligned problems can show either of two ERP signatures of conceptual disruption. These results show that well‐educated adults can integrate mathematical and semantic relations on the rapid timescale of within‐domain ERP effects by a process akin to analogical mapping.     

Change and continuity in early spatial development: claiming the “radical middle”

Debate over change and continuity in cognitive development has revolved around questions of qualitative transitions versus quantitative and incremental improvement. Piaget’s stage theory is the best-known example of a change/qualitative transition approach, while both nativism and empiricism have, for very different reasons, taken a continuity/quantitative increment stance. Recent proposals have, however, attempted to transcend this stark dichotomy, in a move that can be termed claiming the “radical middle.” This paper presents two examples of developmental analyses in spatial development. These two transitions are characterizable as either qualitative or quantitative, but are best thought of as both. A position of this kind allows for much more precise answers to practical questions about issues such as sensitive periods than would otherwise be possible.     

Semantics, conceptual spaces, and the meeting of minds

We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions.     

Neighbourhood Semantics for Quantified Relevant Logics

The Mares-Goldblatt semantics for quantified relevant logics have been developed for first-order extensions of R, and a range of other relevant logics and modal extensions thereof. All such work has taken place in the the ternary relation semantic framework, most famously developed by Sylvan (née Routley) and Meyer. In this paper, the Mares-Goldblatt technique for the interpretation of quantifiers is adapted to the more general neighbourhood semantic framework, developed by Sylvan, Meyer, and, more recently, Goble. This more algebraic semantics allows one to characterise a still wider range of logics, and provides the grist for some new results. To showcase this, we show, using some non-augmented models, that some quantified relevant logics are not conservatively extended by connectives the addition of which do conservatively extend the associated propositional logics, namely fusion and the dual implication. We close by proposing some further uses to which the neighbourhood Mares-Goldblatt semantics may be put.

     

Pragmatic reasoning schemas

We propose that people typically reason about realistic situations using neither content-free syntactic inference rules nor representations of specific experiences. Rather, people reason using knowledge structures that we term pragmatic reasoning schemas, which are generalized sets of rules defined in relation to classes of goals. Three experiments examined the impact of a “permission schema” on deductive reasoning. Experiment 1 demonstrated that by evoking the permission schema it is possible to facilitate performance in Wason's selection paradigm for subjects who have had no experience with the specific content of the problems. Experiment 2 showed that a selection problem worded in terms of an abstract permission elicited better performance than one worded in terms of a concrete but arbitrary situation, providing evidence for an abstract permission schema that is free of domain-specific content. Experiment 3 provided evidence that evocation of a permission schema affects not only tasks requiring procedural knowledge, but also a linguistic rephrasing task requiring declarative knowledge. In particular, statements in the form if p then q were rephrased into the form p only if q with greater frequency for permission than for arbitrary statements, and rephrasings of permission statements produced a pattern of introduction of modals (must, can) totally unlike that observed for arbitrary conditional statements. Other pragmatic schemas, such as “causal” and “evidence” schemas can account for both linguistic and reasoning phenomena that alternative hypotheses fail to explain.     

Towards a Functional Approach to Modular Ontologies Using Institutions

We propose a functional view of ontologies that emphasises their role in determining answers to queries, irrespective of the formalism in which they are written. A notion of framework is introduced that captures the situation of a global language into which both an ontology language and a query language can be translated, in an abstract way. We then generalise existing notions of robustness from the literature, and relate these to interpolation properties that support modularisation of ontologies.     

A probabilistic model of semantic plausibility in sentence processing

Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example, lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico-syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a wide-coverage model that both assigns thematic roles to verb–argument pairs and determines a preferred interpretation by evaluating the plausibility of the resulting ( verb , role , argument ) triples. The model is trained on a corpus of role-annotated language data. We also present a transparent integration of the semantic model with an incremental probabilistic parser. We demonstrate that both the semantic plausibility model and the combined syntax/semantics model predict judgment and reading time data from the experimental literature.     

The Observer XT: A tool for the integration and synchronization of multimodal signals

The Observer was originally developed as a manual event recorder for the collection, management, analysis, and presentation of observational data in animals. Because of the flexibility of later versions, it became clear that The Observer was suitable for almost any study involving collection of observational data in both animals and humans. Furthermore, the most recent version of The Observer (The Observer XT) allows the integration and synchronization of multimodal signals from various sources, such as observational, video, tracking, and physiological data. This article describes how The Observer XT was used to integrate and synchronize video, observational, tracking, and physiological data from an experiment carried out in 2001 at the Wageningen Institute of Animal Sciences of Wageningen University and Research Centre. The integration and synchronization of these multimodal signals in The Observer XT allows the user to draw a more complete picture of the phenomena under study.     

Semantic information and the network theory of account

The article addresses the problem of how semantic information can be upgraded to knowledge. The introductory section explains the technical terminology and the relevant background. Section 2 argues that, for semantic information to be upgraded to knowledge, it is necessary and sufficient to be embedded in a network of questions and answers that correctly accounts for it. Section 3 shows that an information flow network of type A fulfils such a requirement, by warranting that the erotetic deficit, characterising the target semantic information t by default, is correctly satisfied by the information flow of correct answers provided by an informational source s. Section 4 illustrates some of the major advantages of such a Network Theory of Account (NTA) and clears the ground of a few potential difficulties. Section 5 clarifies why NTA and an informational analysis of knowledge, according to which knowledge is accounted semantic information, is not subject to Gettier-type counterexamples. A concluding section briefly summarises the results obtained.     

Language and hand: The dimension of referential competence1

Kinesic and verbal performance data were collected for two groups of women college seniors chosen to represent extremes in a type of verbal ability termed here referential competence. This ability is conceptualized in terms of degree of integration of symbolic and ikonic representation systems, i.e., the strength of the symbol-referent links in semantic memory. A criterion measure of referential competence was developed using deviation of color naming from word-reading times on the Stroop Color-Word test. Referential competence was shown to be statistically as well as conceptually unrelated to both the standard verbal intelligence and fluency measures, and to cognitive style. During a five-minute videotaped monologue about a personal experience, persons high in referential competence produced verbal material which was specific, vivid, and objective; in contrast to the subjective and general material of the low referential group; and also produced approximately three times as many object-focused hand movements, linked to rhythm and intonation patterns of speech but largely without decipherable content. The results suggest that motor schema have a continuing intrinsic role in mature speech production, related to structure rather than content of language.     

Learning to learn causal models

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.