KGGen: Extracting Knowledge Graphs from Plain Text with Language Models (2502.09956v1)

Abstract: Recent interest in building foundation models for KGs has highlighted a fundamental challenge: knowledge-graph data is relatively scarce. The best-known KGs are primarily human-labeled, created by pattern-matching, or extracted using early NLP techniques. While human-generated KGs are in short supply, automatically extracted KGs are of questionable quality. We present a solution to this data scarcity problem in the form of a text-to-KG generator (KGGen), a package that uses LLMs to create high-quality graphs from plaintext. Unlike other KG extractors, KGGen clusters related entities to reduce sparsity in extracted KGs. KGGen is available as a Python library (\texttt{pip install kg-gen}), making it accessible to everyone. Along with KGGen, we release the first benchmark, Measure of of Information in Nodes and Edges (MINE), that tests an extractor's ability to produce a useful KG from plain text. We benchmark our new tool against existing extractors and demonstrate far superior performance.

An Expert Overview of KGGen: Automating Knowledge Graph Extraction from Text

In the field of data science and artificial intelligence, knowledge graphs (KGs) have emerged as fundamental structures for information retrieval, representation, and reasoning. Despite their capability to model complex domains through structured triples, the scarcity of domain-specific KGs hinders their broader applicability. The paper "KGGen: Extracting Knowledge Graphs from Plain Text with LLMs" presents the KGGen tool, devised to enhance the creation and utilization of KGs by leveraging the power of LLMs.

Core Contributions

The paper introduces KGGen, a Python library that aims to address the paucity of detailed and high-quality KGs by automatically generating them from plain text. This innovation contrasts with traditional methods that either rely on human curation or simplistic pattern-matching techniques, both limited by scalability and data richness.

KgGen utilizes LLMs (LMs) coupled with a novel clustering algorithm. Unlike existing methods that produce sparse graphs with highly specific, often redundant entities, KGGen advocates for a more interconnected and semantically dense representation. By clustering related entities—normalizing differences such as tense or capitalization—KGGen produces KGs that facilitate effective downstream tasks such as graph embeddings and retrieval-augmented generation (RAG) systems.

Methodology

The KGGen architecture comprises three main modules:

1. Entity and Relation Extraction (generate): This stage involves parsing the input text using a LLM, such as GPT-4, and extracting relevant entities and their interrelations. The process involves initial identification followed by relation extraction to ensure consistency and accuracy.
2. Aggregation (aggregate): This step reduces redundancies by merging identified entities and relations across multiple text sources into a cohesive graph structure, implementing normalization to lower complexity while enhancing usability.
3. Entity and Edge Clustering (cluster): Leveraging iterative LLM-based clustering, KGGen resolves repeated and synonymous entities/relations into unique nodes, fostering a dense KG aligned with real-world semantics.

Benchmarking: MINE

The authors introduce the Measure of Information in Nodes and Edges (MINE) benchmark, a pioneering effort to evaluate the effectiveness of text-to-KG conversion. MINE underscores the tool's capability by comparing KGGen with existing frameworks like OpenIE and GraphRAG. KGGen exhibits an 18% performance improvement, highlighting its utility in producing meaningful, comprehensive KGs from diverse texts.

Implications and Future Directions

Practically, KGGen expands the capacity to derive structured information from text corpora, potentially benefiting domains with limited graphical data. This automation could democratize access to KGs, allowing smaller organizations to leverage cutting-edge AI solutions previously limited by data availability.

The enhancement of KG connectivity directly benefits embedding learning algorithms like TransE, which require rich relational contexts. The paper reveals that denser KGs improve link prediction and reasoning utilities, which are critical for advancing RAG systems in AI applications.

Theoretically, KGGen's development prompts a re-examination of semantic clustering within natural language processing. By aligning more closely with human cognizance and relationship perception, KGGen sets a new bar for automated knowledge representation.

Future work could focus on refining clustering mechanisms to further reduce over/under-clustering issues and expanding benchmarks to include larger corpora that reflect real-world applications of KGs.

In conclusion, the KGGen tool and accompanying MINE benchmark present a significant contribution to automated KG extraction from freeform text. This paper showcases the movement towards more effective utilization of LLMs in structuring knowledge while addressing core challenges in AI-driven data applications.