Writing clinical practice guidelines in controlled natural language

Clinicians could benefit from decision support systems incorporating the knowledge contained in clinical practice guidelines. However, the unstructured form of these guidelines makes them unsuitable for formal representation. To address this challenge we translated a complete set of pediatric guideline recommendations into Attempto Controlled English (ACE). One experienced pediatrician, one physician and a knowledge engineer assessed that a suitably extended version of ACE can accurately and naturally represent the clinical concepts and the proposed actions of the guidelines. Currently, we are developing a systematic and replicable approach to authoring guideline recommendations in ACE

A model for verbalising relations with roles in multiple languages

Natural language renderings of ontologies facilitate communication with domain experts. While for ontologies with terms in English this is fairly straightforward, it is problematic for grammatically richer languages due to conjugation of verbs, an article that may be dependent on the preposition, or a preposition that modifies the noun. There is no systematic way to deal with such `complex' names of OWL object properties, or their verbalisation with existing language models for annotating ontologies. The modifications occur only when the object performs some {\em role} in a relation, so we propose a conceptual model that can handle this. This requires reconciling the standard view with relational expressions to a positionalist view, which is included in the model and in the formalisation of the mapping between the two. This eases verbalisation and it allows for a more precise representation of the knowledge, yet is still compatible with existing technologies. We have implemented it as a Prot\'eg\'e plugin and validated its adequacy with several languages that need it, such as German and isiZulu

A comparison of three controlled natural languages for OWL 1.1

Abstract. At OWLED2007 a task force was formed to work towards

Terminological resources for text mining over biomedical scientific literature

Objective: We present a combined terminological resource for text mining over biomedical literature. The purpose of the resource is to allow the detection of mentions of specific biological entities in scientific publications, and their grounding to widely accepted identifiers. This is an essential process, useful in itself, and necessary as an intermediate step for almost every type of complex text mining application. Methods: We discuss some of the properties of the terminology for this domain, in particular the degree of ambiguity, which constitutes a peculiar problem for text mining applications. Without a correct recognition and disambiguation of the domain entities no reliable results can be produced. Results: We also discuss an application that makes use of the resulting terminological knowledge base. We annotate an existing corpus of sentences about protein interactions. The annotation consists of a normalization step that matches the terms in our resource with their actual representation in the corpus, and a disambiguation step that resolves the ambiguity of matched terms. Conclusion: In this paper we present a large terminological resource, compiled through the aggregation of a number of different manually curated sources. We discuss the lexical properties of such resources, specifically the degree of ambiguity of the terms, and we inspect the causes of such ambiguity, in particular for protein names. This information is of vital importance for the implementation of an efficient term normalization and grounding algorithm

Controlled Natural Language for Clinical Practice Guidelines

Clinicians would benefit from decision support systems incorporating the knowledge of clinical practice guidelines. However, the unstructured form of the guidelines makes them unsuitable for formal representation. To remedy this shortcoming we translated a set of pediatric guidelines into Attempto Controlled English (ACE). An experienced pediatrician and a knowledge engineer assessed that ACE can accurately represent the clinical concepts and the proposed actions of the guidelines. Currently, we are developing a systematic and replicable approach to authoring guideline recommendations in ACE