Een theorie voor het bestuderen van information retrieval modellen

In dit artikel wordt een theoretisch raamwerk voor het bestuderen van information retrieval (IR) modellen gepresenteerd. Deze studie richt zich met name op de wijze waarop modellen besluiten dat een informatie item omtrent een ander informatie item is. Het raamwerk vindt zijn oorsprong in de Situation Theory. Zogenaamde infons en profons stellen elementaire informatie-dragers voor. Deze kunnen bewerkt worden door middel van fusie operatoren. Middels deze operatoren kunnen relaties tussen informatie-dragers worden vastgelegd. Een verzameling infons vormt een zogenaamde situatie waarmee informatie voorkomend in objecten, zoals documenten, gemodelleerd kan worden. Een willekeurig information retrieval model kan afgebeeld worden in dit raamwerk. Afhankelijk van het soort model zijn hiervoor speciale functies gedefinieerd . Binnen het theoretisch raamwerk definiëren wij een verzameling postulaten, die gebruikt kunnen worden om de omtrentheid relaties geassocieerd met information retrieval modellen, te beschrijven. Aan de hand van deze postulaten zijn wij in staat kwalitatieve uitspraken te doen over de verschillende omtrentheid-relaties die door de verschillende information retrieval modellen genduceerd worden. Ook is het mogelijk kwalitatieve uitspraken te doen over kwantitatieve grootheden als recall en precision. Aan de hand van het boolse retrieval model tonen wij de toepasbaarheid van ons theoretische raamwerk in de praktijk van de information retrieval

Application of aboutness to functional benchmarking in information retrieval

Experimental approaches are widely employed to benchmark the performance of an information retrieval (IR) system. Measurements in terms of recall and precision are computed as performance indicators. Although they are good at assessing the retrieval effectiveness of an IR system, they fail to explore deeper aspects such as its underlying functionality and explain why the system shows such performance. Recently, inductive (i.e., theoretical) evaluation of IR systems has been proposed to circumvent the controversies of the experimental methods. Several studies have adopted the inductive approach, but they mostly focus on theoretical modeling of IR properties by using some metalogic. In this article, we propose to use inductive evaluation for functional benchmarking of IR models as a complement of the traditional experiment-based performance benchmarking. We define a functional benchmark suite in two stages: the evaluation criteria based on the notion of "aboutness," and the formal evaluation methodology using the criteria. The proposed benchmark has been successfully applied to evaluate various well-known classical and logic-based IR models. The functional benchmarking results allow us to compare and analyze the functionality of the different IR models

Using ontological contexts to assess the relevance of statements in ontology evolution

Ontology evolution tools often propose new ontological changes in the form of statements. While different methods exist to check the quality of such statements to be added to the ontology (e.g., in terms of consistency and impact), their relevance is usually left to the user to assess. Relevance in this context is a notion of how well the statement fits in the target ontology. We present an approach to automatically assess such relevance. It is acknowledged in cognitive science and other research areas that a piece of information flowing between two entities is relevant if there is an agreement on the context used between the entities. In our approach, we derive the context of a statement from online ontologies in which it is used, and study how this context matches with the target ontology. We identify relevance patterns that give an indication of rele- vance when the statement context and the target ontology fulfill specific conditions. We validate our approach through an experiment in three dif- ferent domains, and show how our pattern-based technique outperforms a naive overlap-based approach

Quantum Structure in Cognition: Why and How Concepts are Entangled

One of us has recently elaborated a theory for modelling concepts that uses the state context property (SCoP) formalism, i.e. a generalization of the quantum formalism. This formalism incorporates context into the mathematical structure used to represent a concept, and thereby models how context influences the typicality of a single exemplar and the applicability of a single property of a concept, which provides a solution of the 'Pet-Fish problem' and other difficulties occurring in concept theory. Then, a quantum model has been worked out which reproduces the membership weights of several exemplars of concepts and their combinations. We show in this paper that a further relevant effect appears in a natural way whenever two or more concepts combine, namely, 'entanglement'. The presence of entanglement is explicitly revealed by considering a specific example with two concepts, constructing some Bell's inequalities for this example, testing them in a real experiment with test subjects, and finally proving that Bell's inequalities are violated in this case. We show that the intrinsic and unavoidable character of entanglement can be explained in terms of the weights of the exemplars of the combined concept with respect to the weights of the exemplars of the component concepts.Comment: 10 page

Epistemic Entanglement due to Non-Generating Partitions of Classical Dynamical Systems

Quantum entanglement relies on the fact that pure quantum states are dispersive and often inseparable. Since pure classical states are dispersion-free they are always separable and cannot be entangled. However, entanglement is possible for epistemic, dispersive classical states. We show how such epistemic entanglement arises for epistemic states of classical dynamical systems based on phase space partitions that are not generating. We compute epistemically entangled states for two coupled harmonic oscillators.Comment: 13 pages, no figures; International Journal of Theoretical Physics, 201

Quantum Experimental Data in Psychology and Economics

We prove a theorem which shows that a collection of experimental data of probabilistic weights related to decisions with respect to situations and their disjunction cannot be modeled within a classical probabilistic weight structure in case the experimental data contain the effect referred to as the 'disjunction effect' in psychology. We identify different experimental situations in psychology, more specifically in concept theory and in decision theory, and in economics (namely situations where Savage's Sure-Thing Principle is violated) where the disjunction effect appears and we point out the common nature of the effect. We analyze how our theorem constitutes a no-go theorem for classical probabilistic weight structures for common experimental data when the disjunction effect is affecting the values of these data. We put forward a simple geometric criterion that reveals the non classicality of the considered probabilistic weights and we illustrate our geometrical criterion by means of experimentally measured membership weights of items with respect to pairs of concepts and their disjunctions. The violation of the classical probabilistic weight structure is very analogous to the violation of the well-known Bell inequalities studied in quantum mechanics. The no-go theorem we prove in the present article with respect to the collection of experimental data we consider has a status analogous to the well known no-go theorems for hidden variable theories in quantum mechanics with respect to experimental data obtained in quantum laboratories. For this reason our analysis puts forward a strong argument in favor of the validity of using a quantum formalism for modeling the considered psychological experimental data as considered in this paper.Comment: 15 pages, 4 figure

Stratified information disclosure: a synthesis between hypermedia and information retrieval

Contains fulltext : mmubn000001_158864190.pdf (publisher's version ) (Open Access)Promotores : E. Falkenberg en T. van der Weide159 p

Informational inference via information flow

Human judgments about information would seem to have an inferential character. The article presents an informational inference mechanism realized via computations of information flow through a high dimensional conceptual space. The conceptual space is realized via the Hyperspace Analogue to Language Algorithm (HAL), which produces vector representations of concepts compatible with those used in human information processing. We show how inference at the symbolic level can be implemented by employing Barwise and Seligman's (1996) theory of information flow. The real valued state spaces advocated by them are realized by HAL vectors to represent the information "state" of a word in the context of a collection of words. Examples of information flow are given to illustrate how it can be used to drive informational inference

Inferring query models by computing information flow

The language modelling approach lo information retrieval can also be used lo compute query models. A query model can be envisaged as an expansion of an initial query. The more prominent query models in the literature have a probabilistic basis. This paper introduces an alternative, non-probabilistic approach to query modelling whereby the strength of information flow is computed between a query Q and a term w. Information flow is a reflection of how strongly w is informationally contained within the query Q. The information flow model is based on Hyperspace Analogue to Language (HAL) vector representations, which reflects the lexical co-occurrence information of terms. Research from cognitive science has demonstrated the cognitive compatibility of HAL representations with human processing. Query models computed from TREC queries by HAL-based information flow are compared experimentally with two probabilistic query language models. Experimental results are provided showing the HAL-based information flow model be superior to query models computed via Markov chains, and seems to be as effective as a probabilistically motivated relevance model