GO-WORDS: An Entropic Approach to Semantic Decomposition of Gene Ontology Terms

The Gene Ontology (GO) has a large and growing number of terms that constitute its vocabulary. An entropy-based approach is presented to automate the characterization of the compositional semantics of GO terms. The motivation is to extend the machine-readability of GO and to offer insights for the continued maintenance and growth of GO. A proto-type implementation illustrates the benefits of the approach

Automating class definitions from OWL to English

Text definitions for entities within bio-ontologies are a cor-nerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag be-tween specification of the entities in the ontology and the development of the text-based definitions. As well as these text definitions, there can also be logical descriptions and definitions of an ontology's entities. The goal of natural lan-guage generation (NLG) from ontologies is to take the logi-cal description of entities and generate fluent natural lan-guage. We should be able to use NLG to automatically pro-vide text-based definitions from an ontology that has logical descriptions of its entities and thus avoid the bottleneck of authoring these definitions by hand. In this paper we present some early work in using NLG to provide such text definitions for the Experimental factor Ontology (EFO). We present our results, discuss issues in generating text definitions, and highlight some future work

Crowdsourcing Question-Answer Meaning Representations

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A detailed qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, QA-SRL, and AMR) along with many previously under-resourced ones, including implicit arguments and relations. The QAMR data and annotation code is made publicly available to enable future work on how best to model these complex phenomena.Comment: 8 pages, 6 figures, 2 table

Chomskyan Arguments Against Truth-Conditional Semantics Based on Variability and Co-predication

In this paper I try to show that semantics can explain word-to-world relations and that sentences can have meanings that determine truth-conditions. Critics like Chomsky typically maintain that only speakers denote, i.e., only speakers, by using words in one way or another, represent entities or events in the world. However, according to their view, individual acts of denotations are not explained just by virtue of speakers’ semantic knowledge. Against this view, I will hold that, in the typical cases considered, semantic knowledge can account for the denotational uses of words of individual speakers

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN