Review of Biomedical Knowledge and Data Representation with Conceptual Graphs

ARAX: a graph-based modular reasoning tool for translational biomedicine

MOTIVATION

With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine-supporting that language-that federates semantically integrated knowledge-bases.

RESULTS

We introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface (API). ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the user's query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAX's application and utility in specific disease contexts, we present several use-case examples.

AVAILABILITY AND IMPLEMENTATION

The source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx.ai and a web API endpoint at arax.rtx.ai/api/arax/v1.3/ui/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Part-whole representation and reasoning in formal biomedical ontologies

OBJECTIVE

Biomedical ontologies are typically structured in a biaxial way, reflecting both a taxonomic (is-a) and a partonomic (part-of) hierarchy. Commonly used biomedical terminologies, which incorporate such distinctions excel in terms of broad coverage but lack a rigid formal foundation. The latter, however, is a prerequisite for automated reasoning. For the biomedical domain, it is not only crucial to cope with ontological dependencies between wholes and their parts but also with specific reasoning patterns which underlie the propagation of roles across partonomic hierarchies.

METHODS

We scale down part-whole reasoning to subsumption-based taxonomic reasoning within the formal framework of a parsimonious variant of description logics (viz. ALC).

RESULTS

We provide a formal basis for ontological engineering in the domain of biomedicine, as far as part-whole relationships are concerned, by addressing typical reasoning patterns encountered in this domain.

Designing and implementing health data and information providers

OBJECTIVES

To model and implement web portals providing access to certified and high-quality information in the domain of health.

MATERIAL AND METHODS

The Unified Medical Language System (UMLS) knowledge sources of the U.S. National Library of Medicine and principles of implementation resulting from the previous ARIANE project are described. The XML technology that allows files transformations by the means of XSLT is briefly presented.

RESULTS

The design and implementation of software modules that exploit knowledge sources, operate the translation of a user's query to selected information sources, and wrap obtained results are detailed. Querying documentary and factual medical databases are presented.

DISCUSSION

Current implementation and wrapping perspectives are discussed in terms of integration and interoperability of health information and data resources.

Developing the ontological foundations of a terminological system for end-stage diseases, organ failure, dialysis and transplantation

The Etablissement français des Greffes (EfG) is a state agency dealing with Public Health issues related to organ, tissue and cell transplantation in France. The evaluation of organ retrieval and transplantation activities, one of its missions, is supported by a national information system (EfG-IS). The EfG-IS is moving towards a new n-tier architecture comprising a terminology server for end-stage diseases, organ failure, dialysis and transplantation (EfG-TS). Following a preliminary audit of the existing coding system and in order to facilitate data recording, to improve the quality of information, to assume compatibility with terminological existing standards and to allow semantic interoperability with other local, national or international registries, a specific work has been conducted on the thesauri to integrate within the EfG-TS. In this paper focusing on the server's content rather than the container, we report first the functional and cognitive requirements that resulted from the preliminary audit. We then describe the methodological approach used to build the terminological server on "sound ontological foundations". We performed the semantic analysis of existing medical terms to set up disease description frame-like structures. These diseases description frames consist of a limited set of nosological discriminating slots such as etiology, semiology, pathology, evolution and associated diseases. Each relevant medical term is thus associated to a concept defined and inserted within a hierarchy according to disease description frame resulting from the semantic analysis. Last, because this terminological server is shared by various transplant and dialysis centers to record patient data at different time point, contextualization of terms appeared as one of the functional requirements. We will also point out various contexts for medical terms and how they have been taken into account.

Knowledge representation and retrieval using conceptual graphs and free text document self-organisation techniques

Hospitals generate and store a large amount of clinical data each year, a significant portion of which is in free text format. Conventional database storage and retrieval algorithms are incapable of effectively processing free text medical data. The rich information and knowledge buried in healthcare records are unavailable for clinical decision-making. We examined a number of techniques for structuring and processing free text documents to effective and efficient for information retrieval and knowledge discovery. One critical success criterion is that the complexity of the techniques must be polynomial both in space and time for them to be able to cope with very large databases. We used conceptual graphs (CG) to capture the structure and semantic information/knowledge contained within the free text medical documents. Ordering and self-organising techniques (lattice techniques and knowledge space) were used to improve organisation of concepts from standard medical nomenclatures and large sets of free text medical documents. Pair-wise union of CG was performed to identify the common generalisation structure and a lattice structure of these CG documents. A combination of all three techniques allowed us to organise a set of 9000 discharge summaries into a generalisation hierarchy that supported efficient and rich information/knowledge retrieval.

The basis for using the Internet to support the information needs of primary care

Synthesizing the state of the art from the published literature, this review assesses the basis for employing the Internet to support the information needs of primary care. The authors survey what has been published about the information needs of clinical practice, including primary care, and discuss currently available information resources potentially relevant to primary care. Potential methods of linking information needs with appropriate information resources are described in the context of previous classifications of clinical information needs. Also described is the role that existing terminology mapping systems, such as the National Library of Medicine's Unified Medical Language System, may play in representing and linking information needs to answers.