A Conceptual Graph and RDF(S) Approach for Representing and Querying Document Content

Abstract. This article describes a first synthesis of a Conceptual Graph and RDF(S) approach for representing and querying document contents. The framework of this work is the escrire project [1], the main goal of which is to compare three knowledge representation formalisms (KR): conceptual graphs (CG), descriptions logics (DL), and object-oriented representation languages (OOR) for querying about document contents by relying on ontology-based annotations on document content. This comparison relies on an expressive XML-based pivot language to define the ontology and to represent annotations and queries; it consists of evaluating the capacity of the three KR formalisms for expressing the features of the pivot language. Each feature of the pivot language is translated into each KR formalism, which is than used to draw inferences and to answer queries. Our team was responsible on the CG part. The motivation of this paper is to give a first synthesis of the translation process from the pivot language to RDF(S) and CG, to underline the main problems encountered during this translation

Sealife: A Semantic Grid Browser for the Life Sciences Applied to the Study of Infectious Diseases

The objective of Sealife is the conception and realisation of a semantic Grid browser for the life sciences, which will link the existing Web to the currently emerging eScience infrastructure. The SeaLife Browser will allow users to automatically link a host of Web servers and Web/Grid services to the Web content he/she is visiting. This will be accomplished using eScience's growing number of Web/Grid Services and its XML-based standards and ontologies. The browser will identify terms in the pages being browsed through the background knowledge held in ontologies. Through the use of Semantic Hyperlinks, which link identified ontology terms to servers and services, the SeaLife Browser will offer a new dimension of context-based information integration. In this paper, we give an overview over the different components of the browser and their interplay. This SeaLife Browser will be demonstrated within three application scenarios in evidence-based medicine, literature & patent mining, and molecular biology, all relating to the study of infectious diseases. The three applications vertically integrate the molecule/cell, the tissue/organ and the patient/population level by covering the analysis of high-throughput screening data for endocytosis (the molecular entry pathway into the cell), the expression of proteins in the spatial context of tissue and organs, and a high-level library on infectious diseases designed for clinicians and their patients. For more information see http://www.biote.ctu-dresden.de/sealife

Utilizing Deep Learning and RDF to Predict Heart Transplantation Survival

In this paper, we describe the conversion of three different heart transplantation data sets to a Resource Description Framework (RDF) representation and how it can be utilized to train deep learning models. These models were used to predict the outcome of patients both pre- and post-transplant and to calculate their survival time. The International Society for Heart & Lung Transplantation (ISHLT) maintains a registry of heart transplantations that it gathers from grafts performed worldwide. The American organization United Network for Organ Sharing (UNOS) and the Scandinavian Scandiatransplant are contributors to this registry, although they use different data models. We designed a unified graph representation covering these three data sets and we converted the databases into RDF triples. We used the resulting triplestore as input to several machine learning models trained to predict different aspects of heart transplantation patients. Recipient and donor properties are essential to predict the outcome of heart transplantation patients. In contrast with the manual techniques we used to extract data from the tabulated files, the RDF triplestore together with SPARQL, enables us to experiment quickly and automatically with different combinations of features sets, to predict the survival, and simulate the effectiveness of organ allocation policies