Concept Vector Analysis for Temporal Trend Detection
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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, lacking mechanisms to handle concept attributes and relationships, which limits their capabilities in natural language processing, analogy identification, and machine translation.
Innovation Solution
A system and method for processing inquiries by extracting concept sequences from annotated text, graph representations, and user navigation behavior to compute distributed concept vectors, enabling improved representation and visualization of concepts and their relationships, and identifying trends and disruptive concepts by comparing concept graph states over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional vector-based methods (word2vec, matrix formulations) are used to represent words, then distributed representation of words is achieved, but the system cannot identify and process concepts that are more than mere sequences of words or handle concept attributes and relationships
Solution Approach 1:
The patent transitions from word-level vector representations to concept-level vector representations by introducing a new dimension of abstraction. Concepts are extracted as structured entities with attributes and relationships, moving beyond simple word sequences to multi-dimensional concept models that capture semantic meaning, attributes, and inter-concept relationships.
Solution Approach 2:
The patent segments the knowledge base corpus into discrete concept entities with defined attributes and relationships. By breaking down text into structured concept components rather than treating it as continuous word sequences, the system can process and analyze individual concept attributes and their relationships independently.
2Measurement precision
If concept sequences are extracted from annotated text and graph representations to compute distributed concept vectors, then the system can identify concepts and their relationships, but the complexity of processing and visualizing concept graphs increases
Solution Approach 1:
The patent introduces concept vectors as intermediary representations that bridge the gap between complex concept graphs and actionable insights. These vectors serve as compressed, mathematically tractable representations of concept relationships, enabling efficient computation and analysis without directly manipulating the full complexity of the concept graph structure.
Solution Approach 2:
The patent transforms concept relationships from complex graph structures into vector space parameters. By representing concepts and their relationships as vectors with specific parameters (dimensions, magnitudes, angles), the system converts structural complexity into algebraic operations that are computationally efficient and easier to process.
3Loss of information
If the system compares two states of concept graph over time to identify changes in relationship strengths, then trends and disruptive concepts can be detected, but the computational time and resources required increase
Solution Approach 1:
The patent creates vector representations (copies) of concept graphs at different time points, enabling efficient comparison without repeatedly analyzing the full original graph structures. These vector copies capture the essential state of concept relationships at each time point, allowing rapid temporal analysis and trend detection.
Data Source
AI summary
A method and apparatus are provided for automatically generating and processing first and second concept vector sets extracted, respectively, from a first set of concept sequences and from a second, temporally separated, concept sequences by performing a natural language processing (NLP) analysis of the first concept vector set and second concept vector set to detect changes in the corpus over time by identifying changes for one or more concepts included in the first and/or second set of concept sequences.