Identification of Design Principles

This report identifies those design principles for a (possibly new) query and transformation language for the Web supporting inference that are considered essential. Based upon these design principles an initial strawman is selected. Scenarios for querying the Semantic Web illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of the query language to be designed and implemented by the REWERSE working group I4

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Pacific Symposium on Biocomputing 8:565-576(2003) FUNCTIONAL DISCRIMINATION OF GENE EXPRESSION PATTERNS IN TERMS OF THE GENE ONTOLOGY

The ever-growing amount of experimental data in molecular biology and genetics requires its automated analysis, by employing sophisticated knowledge discovery tools. We use an Inductive Logic Programming (ILP) learner to induce functional discrimination rules between genes studied using microarrays and found to be differentially expressed in three recently discovered subtypes of adenocarcinoma of the lung. The discrimination rules involve functional annotations from the Proteome HumanPSD database in terms of the Gene Ontology, whose hierarchical structure is essential for this task. While most of the lower levels of gene expression data (pre)processing have been automated, our work can be seen as a step toward automating the higher level functional analysis of the data. We view our application not just as a prototypical example of applying more sophisticated machine learning techniques to the functional analysis of genes, but also as an incentive for developing increasingly more sophisticated functional annotations and ontologies, that can be automatically processed by such learning algorithms. 1 Introduction an

A Unified Architecture for Knowledge Representation based on Description Logics

. This paper presents a unified architecture for knowledge representation based on description (terminological) logics. The novelty of our approach consists in trying to use description logics not only for representing the domain knowledge, but also for describing beliefs, epistemic operators and actions of intelligent agents in a unitary framework. For this purpose, we have chosen a decidable terminological language, called ALC ? , whose expressivity is high enough to be able to represent actions and epistemic operators corresponding to the majority of modal logics of knowledge and belief. Additionally, we describe practical inference algorithms for the language ALC ? which lies at the heart of our RegAL 2 knowledge representation system. The algorithms are sound and complete and can be used directly for deciding the validity and satisfiability of formulas in the propositional dynamic logic (PDL) by taking advantage of the correspondence between PDL and certain terminological lo..

Planning in Description Logics: Deduction versus Satisfiability Testing

Description Logics (DLs) are formalisms for taxonomic reasoning about structured knowledge. Adding the transitive closure of roles to DLs also enables them to represent and reason about actions and plans. The present paper explores several essentially different encodings of planning in Description Logics. We argue that DLs represent an ideal framework for analysing and comparing these approaches. Thus, we have identified two essentially different deductive encodings (a "causal" and a "symmetric" one), as well as a satisfiability-based approach. While the causal encoding is more appropriate for reasoning about precondition-triggered causal events, the symmetric encoding is more amenable to reasoning about possible outcomes of courses of actions without actually executing them (while allowing both progression and regression). In the deductive approaches, the existence of a plan corresponds to an inconsistency proof rather than to a model of some formula. Viewing planning as satisfiabili..

Reifying Concepts in Description Logics

Practical applications of description logics (DLs) in knowledge-based systems have forced us to introduce the following features which are absent from existing DLs: ffl allowing a concept to be regarded at the same time as an individual (the instance of some other meta-level concept) ffl allowing an individual to represent a collection (set) of other individuals. The first extension, called concept reification, is more general and thus can cover the second one too. We argue that the absence of these features from existing DLs is an important reason for the lack of a unified approach to description logics and object-oriented databases. We also show that concept reification cannot be dealt with by the standard DL semantics and propose a slightly modified semantics that takes care of the inherent higher-order features of reification in a first-order setting. A sound and complete inference algorithm for checking consistency in reified ALCO2 knowledge bases is subsequently put forward. ..