The corpus reported in this paper was developed for the evaluation of a domain-specific Text to Knowledge Mapping (TKM) prototype. The TKM prototype operates on the basis of both a combinatory categorical grammar (CCG) linguistic model and a knowledge model that consists of three layers: ontology, qualitative and quantitative layers. In the course of this evaluation it was necessary to populate...

Rapid development of robots and autonomous vehicles requires semantic information about the surrounding scene to decide upon the correct action or to be able to complete particular tasks. Scene understanding provides the necessary semantic interpretation by semantic scene graphs. For this task, so-called support relationships which describe the contextual relations between parts of the scene su...

This study is part of a project to develop an automatic method to build ontologies, especially in a medical domain, from a document collection. An earlier study had investigated an approach to inferring semantic relations between medical concepts using the UMLS (Unified Medical Language System) semantic net. The study found that semantic relations between concepts could be inferred 68% of the t...

Inclusive Social Network Services (ISN) represent an opportunity for ordinary people to access information and knowledge through the Web. Such systems should provide access for all, creating situations where users' diversity is respected and the access difficulties are minimized. The use of search mechanisms has been the main way to find information in the Web. However, such mechanisms are stil...

Semantic matching determines the mappings between the nodes of two graphs (e.g., ontologies) by computing logical relations (e.g., subsumption) holding among the nodes that correspond semantically to each other. We present an approach to deal with the lack of background knowledge in matching tasks by using semantic matching iteratively. Unlike previous approaches, where the missing axioms are m...

We propose Neural Reasoner , a framework for neural network-based reasoning over natural language sentences. Given a question, Neural Reasoner can infer over multiple supporting facts and find an answer to the question in specific forms. Neural Reasoner has 1) a specific interaction-pooling mechanism, allowing it to examine multiple facts, and 2) a deep architecture, allowing it to model the co...

This paper presents sGRAPH – a domain ontology-driven semantic graph auto extraction system used to discover knowledge from text publications in traditional Chinese medicine. The traditional Chinese medicine language system (TCMLs), composed of an ontology schema and a knowledge base containing 153,692 words and 304,114 relations, is used as the domain ontology. The sGRAPH comprises two compone...

A Semantic Link Network (SLN) consists of nodes (entities, features, concepts, schemas or communities) and semantic links between nodes. This paper proposes an autonomous Semantic Link Network formalism to support intelligent applications on large-scale network. The formalism integrates the SLN logical reasoning with the SLN analogical reasoning and the SLN inductive reasoning as well as existi...

Identifying relevant studies from the entire scientific literature is an important task in biomedical research. Past efforts have incorporated semantically recognized biological entities and medical ontologies into biomedical literature search. However, semantic relations are largely overlooked by biomedical search engines. In this work, we aim to discover synonymous biomedical semantic relatio...

This paper uses human verb associations as the basis for an investigation of verb properties, focusing on semantic verb relations and prominent nominal features. First, the lexical semantic taxonymy GermaNet is checked on the types of classic semantic relations in our data; verbverb pairs not covered by GermaNet can help to detect missing links in the taxonomy, and provide a useful basis for de...