Knowledge graph augmented advanced learning models for commonsense reasoning
نویسندگان
چکیده
منابع مشابه
Knowledge Representation for Commonsense Reasoning with Text
The reader of a text actively constructs a rich picture of the objects, events, and situation described. The text is a vague, insufficient, and ambiguous indicator of the world that the writer intends to depict. The reader draws upon world knowledge to disambiguate and clarify the text, selecting the most plausible interpretation from among the (infinitely) many possible ones. In principle, any...
متن کاملAn Incremental Learning Model for Commonsense Reasoning
A self-organizing incremental learning model that attempts to combine inductive learning with prior knowledge and default reasoning is described. The inductive learning scheme accounts for useful generalizations and dynamic priority allocation, and effectively supplements prior knowledge. New rules may be created and existing rules modified, thus allowing the system to evolve over time. By comb...
متن کاملCommonsense reasoning, commonsense knowledge, and the SP theory of intelligence
This paper describes how the SP theory of intelligence, outlined in an Appendix, may throw light on aspects of commonsense reasoning (CSR) and commonsense knowledge (CSK) (together shortened to CSRK), as discussed in another paper by Ernest Davis and Gary Marcus (DM). The SP system has the generality needed for CSRK: Turing equivalence; the generality of information compression as the foundatio...
متن کاملA Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases
Tasks such as question answering and semantic search are dependent on the ability of querying & reasoning over large-scale commonsense knowledge bases (KBs). However, dealing with commonsense data demands coping with problems such as the increase in schema complexity, semantic inconsistency, incompleteness and scalability. This paper proposes a selective graph navigation mechanism based on a di...
متن کاملDeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2021
ISSN: 1757-899X
DOI: 10.1088/1757-899x/1022/1/012038