Question: Why do they think their assumption is reasonable?
Evidence: Natural language generation (NLG) has been one of the key topics of research in natural language processing, which was highlighted by the huge body of work on NLG surveyed in BIBREF0, BIBREF1. With the advances of several devices capable of understanding spoken language and conducting conversation with human (e.g., Google Home, Amazon Echo) and the shrinking gap created by the digital devices, it is not difficult to foresee that the market and application areas of NLG systems will continue to grow, especially in applications whose users are non-experts. In such application, a user often asks for certain information and waits for the answer and a NLG module would return the answer in spoken language instead of text such as in question-answering systems or recommendation systems. The NLG system in these two applications uses templates to generate the answers in natural language for the users. A more advanced NLG system in this direction is described in BIBREF2, which works with ontologies annotated using the Attempto language and can generate a natural language description for workflows created by the systems built in the Phylotastic project. The applications targeted by these systems are significantly different from NLG systems, whose main purpose is to generate high-quality natural language description of objects or reports, such as those reported in the recent AAAI conference BIBREF3, BIBREF4, BIBREF5.
The present paper is motivated by the need to generate natural language description of computational results to non-expert users such as those developed in the Phylotastic project. In this project, the users are experts in evolutionary biology but are none experts in ontologies and web services. When a user places a request, he/she will receive a workflow consisting of web services, whose inputs and outputs are specified by instances of classes in the ontologies working with web services, as well as the ordering and relationships between the services. To assist the user in understanding the workflow, a natural language description of the workflow is generated. In order to accomplish the task, the NLG system in the Phylotastic project proposes to annotate elements of the ontologies using Attempto, a simple subset of English with precisely defined syntax and semantics.
In this paper, we propose a system that addresses the limitation of the system discussed in the Phylotastic project BIBREF2. Specifically, we assume that the annotations given in an ontology are natural language sentences. This is a reasonable assumption given that the developers of an ontology are usually those who have intimate knowledge about entities described in the ontology and often have some sort of comments about classes, objects, and instances of the ontology. We then show that the system is very flexible and can be used for the same purpose with new ontologies.
The rest of the paper is organized as follows. Section SECREF2 briefly reviews the basics of Grammatical Framework (GF)BIBREF6. Section SECREF3 describes the main modules of the system. Section SECREF4 includes two use cases of the system using an available ontologies against in the context of reasoning about ontologies. Specifically, it compares with the system used in the Phylotastic project and an ontology about people. This section also contains a use case that highlights the versatility of the proposed system by addressing a challenge to create an abstract Wikipedia BIBREF7. Related works are discussed in Section SECREF5. Section SECREF6 concludes the paper.
This is a reasonable assumption given that the developers of an ontology are usually those who have intimate knowledge about entities described in the ontology and often have some sort of comments about classes, objects, and instances of the ontology.
Answer: The developers of an ontology are usually those who have intimate knowledge about entities described in the ontology and often have some sort of comments about classes, objects, and instances of the ontology.