A location conversion method for roads through deep learning-based semantic matching and simplified qualitative direction knowledge representation

2021 ◽  
Vol 104 ◽  
pp. 104400
Author(s):  
Ruozhen Cheng ◽  
Jing Chen
Keyword(s):  
Deep Learning ◽  
Knowledge Representation ◽  
Semantic Matching ◽  
Conversion Method

scholarly journals An AI Using Construction Grammar to Understand Text

2021 ◽  
Vol 15 (2) ◽  
pp. 34-48
Author(s):  
Denis Kiselev
Keyword(s):  
Deep Learning ◽  
Knowledge Representation ◽  
Natural Language ◽  
State Of The Art ◽  
Deep Understanding ◽  
Knowledge Structure ◽  
Construction Grammar ◽  
Anaphora Resolution

This paper describes an AI that uses construction grammar (CG)—a means of knowledge representation for deep understanding of text. The proposed improvements aim at more versatility of the text form and meaning knowledge structure, as well as for intelligent choosing among possible parses. Along with the improvements, computational CG techniques that form the implementation basis are explained. Evaluation experiments utilize a Winograd schema (WS)—a major test for AI—dataset and compare the implementation with state-of-the-art ones. Results have demonstrated that compared with such techniques as deep learning, the proposed CG approach has a higher potential for the task of anaphora resolution involving deep understanding of the natural language.

scholarly journals Introduction to the TPLP Special Issue on User-oriented Logic Programming and Reasoning Paradigms

2019 ◽  
Vol 19 (2) ◽  
pp. 109-113
Author(s):  
STEFAN ELLMAUTHALER ◽  
CLAUDIA SCHULZ
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Unstructured Data ◽  
Knowledge Representation And Reasoning ◽  
Special Issue

With the rise of machine learning, and more recently the overwhelming interest in deep learning, knowledge representation and reasoning (KRR) approaches struggle to maintain their position within the wider Artificial Intelligence (AI) community. Often considered as part of thegood old-fashioned AI(Haugeland 1985) – like a memory of glorious old days that have come to an end – many consider KRR as no longer applicable (on its own) to the problems faced by AI today (Blackwell 2015; Garneloet al.2016). What they see are logical languages with symbols incomprehensible by most, inference mechanisms that even experts have difficulties tracing and debugging, and the incapability to process unstructured data like text.

scholarly journals Technical, Hard and Explainable Question Answering (THE-QA)

2019 ◽  
Author(s):  
Shailaja Sampat
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Question Answering ◽  
State Of The Art ◽  
Future Research ◽  
Semantic Parsing ◽  
Learning Models ◽  
Knowledge Types ◽  
Language And Vision

The ability of an agent to rationally answer questions about a given task is the key measure of its intelligence. While we have obtained phenomenal performance over various language and vision tasks separately, 'Technical, Hard and Explainable Question Answering' (THE-QA) is a new challenging corpus which addresses them jointly. THE-QA is a question answering task involving diagram understanding and reading comprehension. We plan to establish benchmarks over this new corpus using deep learning models guided by knowledge representation methods. The proposed approach will envisage detailed semantic parsing of technical figures and text, which is robust against diverse formats. It will be aided by knowledge acquisition and reasoning module that categorizes different knowledge types, identify sources to acquire that knowledge and perform reasoning to answer the questions correctly. THE-QA data will present a strong challenge to the community for future research and will bridge the gap between state-of-the-art Artificial Intelligence (AI) and 'Human-level' AI.

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