Bridge Rules Research Articles

Mining bridging rules between conceptual clusters

Bridging rules take the antecedent and action from different conceptual clusters. They are distinguished from association rules (frequent itemsets) because (1) they can be generated by the infrequent itemsets that are pruned in association rule mining, and (2) they are measured by their importance including the distance between two conceptual clusters, whereas frequent itemsets are measured only by their support. In this paper, we first design two algorithms for mining bridging rules between clusters, and then propose two non-linear metrics to measure their interestingness. We evaluate these algorithms experimentally and demonstrate that our approach is promising.

Exploring the ncRNA–ncRNA patterns based on bridging rules

ncRNAs play an important role in the regulation of gene expression. However, many of their functions have not yet been fully discovered. There are complicated relationships between ncRNAs in different categories. Finding these relationships can contribute to identify ncRNAs’ functions and properties. We extend the association rule to represent the relationship between two ncRNAs. Based on this rule, we can speculate the ncRNA’s function when it interacts with other ncRNAs. We propose two measures to explore the relationships between ncRNAs in different categories. Entropy theory is to calculate how close two ncRNAs are. Association rule is to represent the interactions between ncRNAs. We use three datasets from miRBase and RNAdb. Two from miRBase are designed for finding relationships between miRNAs; the other from RNAdb is designed for relationships among miRNA, snoRNA and piRNA. We evaluate our measures from both biological significance and performance perspectives. All the cross-species patterns regarding miRNA that we found are proven correct using miRNAMap 2.0. In addition, we find novel cross-genomes patterns such as (hsa-mir-190b → hsa-mir-153-2). According to the patterns we find, we can (1) explore one ncRNA’s function from another with known function and (2) speculate the functions of both of them based on the relationship even we do no understand either of them. Our methods’ merits also include: (1) they are suitable for any ncRNA datasets and (2) they are not sensitive to the parameters.

AUGMENTING SUBSUMPTION PROPAGATION IN DISTRIBUTED DESCRIPTION LOGICS

Distributed description logics (DDL) enable reasoning with multiple ontologies interconnected by directional semantic mapping, called bridge rules. Bridge rules map concepts of a source ontology into concepts of a target ontology. Concept subsumptions of the source ontology can be propagated according to a propagation pattern expressed by means of bridge rules into concept subsumptions of the target ontology. In the basic formulation of DDL, such a propagation is mostly limited to cases when pairs of ontologies are directly linked by means of bridge rules. However, when more than two ontologies are involved, one would expect that subsumption propagates along chains of ontologies linked by bridge rules, but the semantics of DDL is too weak to support this behavior. In a recent study, an adjusted semantics for DDL that supports subsumption propagation through chains of bridge rules has been introduced. This study makes use of a so-called compositional consistency requirement that has been employed before in package-based description logics. While the results concerning subsumption propagation under the adjusted semantics are encouraging, there are important drawbacks. In this article we take a wider perspective, and propose a study of several different alternative extensions of the DDL semantics. For each of them we study the formal properties, and we select the one that, according to our analysis, constitutes a good compromise, and for this case we provide a sound and complete tableaux decision procedure.

Comparing formal theories of context in AI

The problem of context has a long tradition in different areas of artificial intelligence (AI). However, formalizing context has been widely discussed only since the late 80s, when J. McCarthy argued that formalizing context was a crucial step toward the solution of the problem of generality. Since then, two main formalizations have been proposed in AI: Propositional Logic of Context (PLC) and Local Models Semantics/ MultiContext Systems (LMS/MCS). In this paper, we propose the first in depth comparison between these two formalizations, both from a technical and a conceptual point of view. The main technical result of this paper is the formal proof of the following facts: (i) PLC can be embedded into a particular class of MCS, called MPLC; (ii) MCS/LMS cannot be embedded in PLC using only lifting axioms to encode bridge rules, and (iii) under some important restrictions (including the hypothesis that each context has finite and homogeneous propositional languages), MCS/LMS can be embedded in PLC with generic axioms. The last part of the paper contains a comparison of the epistemological adequacy of PLC and MCS/LMS for the representation of the most important issues about contexts.

Time to remember.

The metabolic computations performed by the spatiotemporal waves of intracellular signal transduction cascades in neurons are just beginning to unravel. Given the rapid pace of molecular and cellular biology, there is little doubt that in the coming years, exciting facts will be learned about these intracellular networks. However, a quick glance at Table 1Table 1 pinpoints a most critical and difficult problem facing brain research: the gap between the current success of neuroscience in disclosing types and tokens of molecular devices and processes used for maintenance and long-term plasticity on the one hand, and the still enigmatic codes and mechanisms of ongoing representations and computations unique to the brain, on the other. Closing the gap requires not only novel concepts, methodologies, and technologies but also an intimate dialogue between molecular and system neuroscientists, which may eventually yield some interlevel bridge rules (Nagel 1979xNagel, E. See all ReferencesNagel 1979). For example, dendritic Ca2+ transients may end up as terms in such rules (Table 1Table 1). How successful the bridging attempts will be, only time will tell.

Descriptive dynamic logic and its application to reflective architectures

The aim of this paper is to propose dynamic logic as a common logical framework to describe and identify the most relevant formal characteristics of multi-language logical architectures (MLA) in order to investigate the expressive power of the knowledge bases that can be built upon them. In general, a MLA allows to build knowledge bases as a set of units with initial local theories written in possible different logical languages. Each unit is also usually allowed to have its own intra-unit deductive system. Moreover, the whole knowledge base is equipped with an additional set of deductive rules, called bridge rules, to control the information flow among the different units of the knowledge base. The set of bridge rules act as an inter-unit deductive system. The reasoning dynamics of a knowledge base on top of a MLA can therefore be described by how the local theories of the units evolve during execution.

A multicontext architecture for formalizing complex reasoning

We propose multicontext systems (MC systems) as a formal framework for the specification of complex reasoning. MC systems provide the ability to structure the specification of “global” reasoning in terms of “local” reasoning subpatterns. Each subpattern is modeled as a deduction in a context, formally defined as an axiomatic formal system. the global reasoning pattern is modeled as a concatenation of contextual deductions via bridge rules, i.e., inference rules that infer a fact in one context from facts asserted in other contexts. Besides the formal framework, in this article we propose a three-layer architecture designed to specify and automatize complex reasoning. At the first level we have object-level contexts (called s-contexts) for domain specifications. Problem-solving principles and, more in general, meta-level knowledge about the application domain is specified in a distinct context, called Problem-Solving Context (PSC). On top of s-contexts and PSC, we have a further context, called MT, where it is possible to specify strategies to control multicontext reasoning spanning through s-contexts and PSC. We show how GETFOL can be used as a computer tool for the implementation of MC systems and for the automatization of multicontext deductions. © 1995 John Wiley & Sons, Inc.