Distributed Concept Vectors for Cognitive QA Systems
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Solution Overview
Problem
Traditional cognitive question answering systems are unable to effectively identify and process concepts beyond mere sequences of words, limiting their ability to capture semantic and syntactic properties, and fail to process concept attributes in relation to other attributes, resulting in limited capabilities for natural language processing, analogy identification, and machine translation.
Innovation Solution
A system and method that extracts and processes concept vectors from annotated text, graph representations, and user navigation behavior to improve the quality of answers by generating distributed representations of concepts, enabling better concept interaction and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional vector representation methods (word2vec, matrix formulations) are used to represent words, then syntactic and semantic properties of words are captured, but concepts that are more than mere sequences of words cannot be identified or processed
Solution Approach 1:
The patent segments the concept representation task into multiple components: extracting concept sequences from text, representing individual concepts as vectors, and combining these to form distributed concept representations. This segmentation allows the system to handle complex concepts by breaking them down into manageable vector operations while preserving the hierarchical structure of concept relationships.
Solution Approach 2:
The patent transitions from traditional one-dimensional word vectors to multi-dimensional concept vectors that capture not only semantic meaning but also hierarchical relationships, attributes, and contextual information. By adding dimensional layers to the vector representation, the system can encode complex concept structures including parent-child relationships, attribute-value pairs, and contextual associations that go beyond simple word sequences.
2Productivity
If existing vector representation techniques are applied to concepts, then limited NLP parsing and machine translation capabilities are achieved, but the ability to process concept attributes in relation to other attributes is lost
Solution Approach 1:
The patent merges multiple sources of conceptual information including text annotations, graph representations, and user navigation behavior into a unified concept vector representation. This combination integrates diverse data types and relationship structures, allowing the system to preserve and process concept attribute relationships while enhancing overall NLP processing capability through multi-modal information fusion.
Solution Approach 2:
The patent creates composite concept representations by combining vector embeddings from different sources and relationship types. Similar to composite materials in physics, the concept vectors are constructed by integrating multiple information layers (semantic vectors, relational vectors, contextual vectors) to form a robust representation that captures both individual concept properties and their inter-attribute relationships, enabling advanced NLP tasks.
3Measurement precision
If brute force learning by Neural Networks or log-linear classifiers is used, then vector representations are produced, but the mechanism cannot identify concepts beyond word sequences
Solution Approach 1:
The patent introduces concept sequences and concept graphs as intermediary structures between raw text and final vector representations. These intermediaries serve as bridges that capture hierarchical concept relationships and attribute structures, allowing the learning mechanisms to process and identify complex concepts beyond simple word sequences while maintaining measurement precision through structured representation layers.
Data Source
AI summary
An approach is provided for automatically generating and processing concept vectors by extracting concept sequences from one or more content sources and generating a first concept vector for a first concept by supplying the concept sequences as inputs to a vector learning component, such that the first concept vector comprises information interrelating the first concept to other concepts in the concept sequences which is inferred from the concept sequences.