Using human authored description logics ABoxes as concept models for natural language generation

Abstract

Papers written by researchers in medical and life sciences are a valuable source of information even for non-experts looking for knowledge related to rare diseases, but only if those non-experts can read English. If researchers create descriptors of their papers in the form of description logics (DL) ABoxes (assertion components) according to a DL ontology, then by using currently available software, computers can reason over the ABoxes to infer semantic consequences of the assertions in the descriptor. One open issue is how best to render information contained in the ABox for a particular user based on that user's knowledge requirements and background knowledge, including language preference. Natural language generation (NLG) is a method for rendering computer-interpretable statements, content models, in human-readable form, natural language text. ABoxes could be used as content models for NLG with particularly rich semantics.In particular, ABoxes could be used to generate expressions of expert knowledge in languages different from the original language that are more accurate and more tailor-fit to the user's cognitive state than existing methods for translating scientific papers. A method for generating natural language expressions from ABoxes in English and Japanese is presented and compared with a state-of-the-art expert translation software package.

Can SKOS Ontologies Improve the Accuracy of Measuring Semantic Similarity of Purchase Orders?

“…The other side of getting creators of knowledge resources to make computer-understandable descriptors is to develop applications in areas that directly benefit the knowledge creator, a concept termed "instant gratification" by McDowell et al (2003). One example is natural language generation, which can be used to generate accurate representations of the semantic graphs in any language that is handled by the generator (Kraines and Guo, 2009;Androutsopoulos et al 2007).…”

A System for Ontology-Based Sharing of Expert Knowledge in Sustainability Science

“…In summary, if computer-understandable semantic statements describing expert scientific knowledge can be provided that are highly accurate and semantically rich, we could leverage advanced inference and reasoning technologies from the fields of artificial intelligence and the Semantic Web to enable computers to provide a wide range of knowledge processing services. As one example, we suggest that it should be possible to translate semantic statements created in one natural language (such as Japanese) to another (such as English), or even from one domain of knowledge (such as chemical engineering) to another (such as macroeconomics), with essentially no loss of accuracy, simply by using a relatively straightforward mapping of content models that are expressed in terms of ontologies for different knowledge domains (Bateman, 1990;Bateman et al, 2005;Dymetman, 2002;Kruijff-Korbayova & Kruijff, 1999;Kraines & Guo, 2009).…”

“…The semantic statement shown in Figure 4 can be rendered in English using a simple natural language generation algorithm that we have developed for the EKOSS system as shown in Figure 5 (Kraines & Guo, 2009). We have also developed natural language generation algorithms for Japanese and Chinese.…”