QUESTION ANSWERING SYSTEM USING KNOWLEDGE GRAPH EMBEDDINGS

Abstract

A question-answering system utilizing knowledge graph embeddings is disclosed. The system integrates a knowledge graph representation with advanced embedding techniques to enable efficient and accurate natural language question answering. Knowledge graphs store entities and their relationships in a structured format, while embeddings translate this structured data into a high-dimensional vector space. The system processes user queries by mapping them into the same vector space as the knowledge graph embeddings, enabling semantic alignment. By leveraging machine learning models, the system retrieves and ranks relevant answers based on contextual similarity and relationship strength. The disclosed approach improves response accuracy, scalability, and adaptability to diverse query domains, offering applications in search engines, virtual assistants, and automated decision-making systems.

Specification

Description:FIELD OF THE INVENTION
[0001] This invention relates to computational question-answering systems and, more specifically, to methods and systems utilizing knowledge graph embeddings to provide precise, contextually relevant answers to user queries.

BACKGROUND OF THE INVENTION
[0002] Efficient question-answering systems are essential in fields like customer service, education, and data analytics. Traditional approaches rely on textual databases or predefined heuristics, which often fail to leverage complex relationships between concepts. Knowledge graphs (KGs), which encode entities and their relationships, can improve accuracy. However, existing methods using KGs suffer from computational inefficiencies and lack semantic depth. In the field of information retrieval and artificial intelligence, question-answering (QA) systems have become a crucial tool for addressing natural language queries efficiently and accurately.
[0003] These systems are widely employed in various applications, including virtual assistants, search engines, customer support, and decision-making tools. Despite significant advancements, traditional QA systems face several challenges when processing complex queries, handling ambiguity, and scaling to large datasets. Conventional QA systems often rely on keyword matching or rule-based techniques to retrieve information. These approaches struggle with understanding the semantic meaning of queries, leading to imprecise or irrelevant answers. Furthermore, they are limited in their ability to handle multi-hop reasoning, where a query requires combining information across multiple data points to generate a coherent response. To overcome these limitations, recent research has shifted towards knowledge-driven approaches.
[0004] Knowledge graphs have emerged as a powerful representation for structured data, enabling systems to encode entities (e.g., people, places, concepts) and their relationships in a graph format. Knowledge graphs offer an intuitive way to model interconnected information, making them an excellent resource for QA systems. However, utilizing knowledge graphs directly poses computational challenges, especially when dealing with large-scale datasets. The need to traverse a vast number of nodes and edges for every query can lead to inefficiencies and latency issues. To address these computational challenges, embedding techniques have been introduced.
[0005] Knowledge graph embeddings convert entities and relationships into high-dimensional vector representations. These vectors capture the semantic meaning and relational information of the graph elements in a compressed, continuous space. By using embeddings, QA systems can efficiently process queries using vector operations, such as similarity computations, rather than graph traversal. The system is designed to bridge the gap between unstructured natural language queries and structured knowledge graph data. By leveraging embeddings, the system transforms both the knowledge graph and user queries into a common vector space, enabling efficient and accurate semantic matching.
[0006] Existing QA systems typically fall into two categories: text-based and knowledge graph-based. Text-based systems rely on unstructured data, such as documents or webpages, to retrieve information. While these systems benefit from the vast amount of available textual data, they often lack the precision and reasoning capabilities needed for complex queries. Knowledge graph-based systems, on the other hand, use structured data to provide precise answers. Traditional approaches to such systems involve graph traversal algorithms, which are computationally expensive and do not scale well to large datasets. Moreover, these systems often require extensive manual curation of the knowledge graph, making them difficult to maintain and expand.
[0007] There is a pressing need for a QA system that effectively combines the structured knowledge of graphs with the semantic richness of embeddings. Such a system would enable accurate, efficient, and scalable answering of natural language queries while addressing the limitations of existing approaches. The disclosed invention represents a significant advancement in QA systems by integrating knowledge graph embeddings with NLP and machine learning techniques. By addressing the challenges of semantic alignment, contextual understanding, scalability, and reasoning, the system provides a robust solution for answering natural language queries efficiently and accurately. This innovation has the potential to revolutionize how users interact with structured knowledge, offering widespread benefits across multiple domains.

OBJECTS OF THE INVENTION
[0008] The principal object of this invention is to provide a system that accurately interprets natural language queries by leveraging knowledge graph embeddings to map query components into a meaningful vector space.
[0009] Another object of this invention is to enhance the precision and relevance of answers by using embeddings that capture semantic relationships and contextual nuances within a knowledge graph.
[0010] A further object of this invention is to enable the system to scale across large and complex datasets by representing entities and relationships in a compressed, computationally efficient form.
[0011] Another further object of this invention is to facilitate easy adaptation to different knowledge domains and datasets by employing machine learning models that generalize across varied contexts.
[0012] Yet another object of this invention is to reduce the computational overhead of query processing by utilizing precomputed embeddings and vector similarity operations.
[0013] Another further object of this invention is to allow for the dynamic incorporation of new information into the knowledge graph and its embeddings without requiring extensive retraining or reprocessing.
[0014] Another object of this invention is to provide an intuitive interface for end-users to pose natural language questions and receive accurate, concise, and contextually appropriate responses.
[0015] Yet another object of this invention is to improve system robustness by disambiguating queries using the contextual and semantic relationships embedded in the knowledge graph.
[0016] These and another objects and advantages will become more apparent when reference is made to the following description and accompanying drawings.

SUMMARY OF THE INVENTION
[0017] A solution to one or more drawbacks of the existing technology and additional advantages are provided through the present disclosure. Additional features and advantages are realized through the technicalities of the present disclosure. Other aspects of the disclosure are described in detail herein and are considered to be a part of the claimed disclosure.
[0018] In an aspect of the present invention Natural Language Processing (NLP) Module processes and encodes user queries into vectorized representations.
[0019] In an aspect of the present invention knowledge graph embedding module generates embeddings for entities and relations in a knowledge graph using techniques like TransE, RotatE, or graph neural networks.
[0020] In an aspect of the present invention mapping mechanism transforms query vectors into the embedding space of the knowledge graph.
[0021] In an aspect of the present invention answer selection module identifies the most relevant entities or relationships in the embedding space and translates them into human-readable responses.
[0022] In an aspect of the present invention learning framework is a machine learning model, trained using supervised or unsupervised techniques, optimizes the mapping between queries and KG embeddings.

BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Fig 1 illustrates the question answering system using knowledge graph embeddings;
[0024] The figure of the present subject matter depicts for illustration only. A person skilled in the art will easily recognize from the following description that the illustration herein may be employed without departing from the principles of the disclosure described herein.

DETAILED DESCRIPTION OF THE INVENTION
[0025] The present invention is of the best mode presently contemplated for carrying out the invention. This description is not to be taken in a limiting sense, but is made merely for the purpose of describing the general principles of the invention. The scope of the invention should be determined with reference to the claims.
[0026] Traditional QA systems face significant challenges in understanding complex natural language queries, resolving ambiguities, and scaling to large datasets. Many rely on keyword matching or rule-based techniques, which lack semantic understanding and reasoning capabilities. Knowledge graphs, which represent entities and their relationships in a structured format, have emerged as a solution for enabling precise and logical responses. However, querying knowledge graphs directly is computationally expensive, particularly for large-scale data. Embedding techniques address these challenges by transforming entities and relationships in the knowledge graph into high-dimensional vector representations. This allows semantic matching and reasoning to be performed using efficient vector computations rather than graph traversal. Despite their promise, existing embedding-based QA systems struggle with issues like semantic alignment, multi-hop reasoning, and adaptability to dynamic knowledge updates.
[0027] The query processing module interprets natural language queries and maps them into the same embedding space as the knowledge graph. The subcomponent Natural Language Understanding (NLU) breaks down the query into its components, including entities, intents, and contextual cues. The encoding transforms the processed query into a vector representation using pre-trained language models like BERT or GPT. The semantic alignment ensures that the query vector aligns with the embeddings of the knowledge graph by fine-tuning the language model on the graph's vocabulary and structure. Similarity Computation Module compares the query vector with the embeddings of entities and relationships in the knowledge graph. It computes semantic similarity using metrics such as cosine similarity, dot product, or learned distance functions. The output is a ranked list of candidate answers based on their relevance to the query.
[0028] The answer generation module retrieves the most relevant entities and relationships from the knowledge graph and constructs a response. Key features include Multi-Hop Reasoning that combines information from multiple nodes and edges in the graph to answer complex queries, contextual relevance applies additional weighting to entities and relationships that align with the query's context and explanation generation provides a rationale for the answer by highlighting the paths or nodes in the knowledge graph that contributed to the response. The update module supports dynamic integration of new knowledge into the graph. This involves a) Adding new entities, relationships, and attributes, and b) Recomputing embeddings for updated sections of the graph while preserving previously learned representations. The use of embeddings enables efficient similarity computation, avoiding the need for expensive graph traversal.
[0029] The system can handle large and complex knowledge graphs, making it suitable for enterprise-scale applications. By aligning query embeddings with knowledge graph embeddings, the system improves the accuracy and relevance of answers. The update module ensures the system remains up-to-date with new information, reducing the need for extensive manual maintenance. The system supports multi-hop reasoning, allowing it to handle complex queries that require connections across multiple nodes in the knowledge graph. Embedding-based similarity computations eliminate the need for costly graph traversal operations. Semantic alignment ensures precise responses to user queries. Dynamic updates and fine-tuning allow the system to adapt to new domains and datasets. Multi-hop reasoning capabilities enable the system to address complex queries effectively. The system performs well on large-scale knowledge graphs, making it suitable for industrial applications. Natural language input and explainable answers enhance user interaction and trust.
[0030] The invention has a wide range of applications like, enhancing search precision by leveraging structured knowledge, improving conversational AI systems like Siri, Alexa, and Google Assistant, supporting medical professionals with structured responses from medical knowledge bases, providing detailed and contextual answers in intelligent tutoring systems, automating and improving the accuracy of responses in chatbot and assisting businesses in retrieving relevant insights from structured data repositories. The QA system using knowledge graph embeddings is a transformative approach to answering natural language queries with precision and efficiency. By addressing the challenges of traditional systems such as semantic understanding, reasoning, and scalability it provides a robust solution applicable across various industries. Its integration of advanced NLP, machine learning, and graph embedding techniques ensures adaptability to diverse use cases, setting a new benchmark for intelligent information retrieval.
[0031] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will further be understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, references to "a method" or "an embodiment" throughout are not intended to mean the same method or same embodiment, unless the context clearly indicates otherwise.
[0032] It should it be understood that, in general, where the invention, or aspects of the invention, is/are referred to as comprising particular elements, features, etc., certain embodiments of the invention or aspects of the invention consist, or consist essentially of, such elements, features, etc. It is noted that the term "comprising" is intended to be open and permits the inclusion of additional elements or steps. Furthermore, it is to be understood that unless otherwise indicated or otherwise evident from the context and understanding of one of ordinary skill in the art, values that are expressed as ranges can assume any specific value or subrange within the stated ranges in different embodiments of the invention, to the tenth of the unit of the lower limit of the range, unless the context clearly dictates otherwise.
, Claims:We claim:
1. A computer-implemented question-answering system, comprising:

a knowledge graph comprising entities and relationships represented in a structured format;
an embedding module configured to generate vector representations of the entities and relationships within the knowledge graph;
a query processing module configured to transform a natural language query into a vector representation in the same embedding space as the knowledge graph;
a similarity computation module configured to compute semantic similarity between the query vector representation and the vector representations of entities and relationships in the knowledge graph; and
an answer generation module configured to retrieve and rank responses based on the computed similarity and to output a response to the natural language query.

2. The system claimed in claim 1, wherein the embedding module employs machine learning models, including graph neural networks, to generate embeddings that capture semantic relationships between entities.

3. The system claimed in claim 1, wherein the query processing module includes a natural language processing engine configured to tokenize, parse, and encode the query into vector representations.

4. The system claimed in claim 1, wherein the answer generation module provides ranked answers based on a relevance score computed using the similarity metric and contextual weighting.

5. The system claimed in claim 1, further comprising an update module configured to dynamically incorporate new entities and relationships into the knowledge graph and update the embeddings accordingly.

6. The system claimed in claim 1, wherein the knowledge graph embeddings are pre-trained using a knowledge graph completion task to enhance the accuracy of missing link prediction.

7. The system claimed in claim 1, wherein the answer generation module supports explanation generation by retrieving the reasoning paths or intermediate nodes used in forming the response.

8. A method for answering natural language questions using knowledge graph embeddings, comprising:

constructing a knowledge graph comprising entities and relationships;
generating vector representations of the entities and relationships in the knowledge graph;
receiving a natural language query and transforming the query into a vector representation;
computing semantic similarity between the query vector and knowledge graph embeddings; and
retrieving and presenting an answer based on the computed similarity.

9. The method claimed in claim 8, further comprising dynamically updating the knowledge graph and its embeddings with new information to maintain response relevance and accuracy.

10. The method claimed in claim 8, wherein the transformation of the query into a vector representation includes contextual embedding techniques utilizing pre-trained language models.

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