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KGC24 takeaways
KGC24 takeaways
Had a great time at The Knowledge Graph Conference last week! Here are my takeaways: Not surprisingly, there was a ton of presentations and talk about GenAI…
·linkedin.com·
KGC24 takeaways
Knowledge Graphs Add Layers of Value, part 1 | LinkedIn
Knowledge Graphs Add Layers of Value, part 1 | LinkedIn
I see a growing consensus: more and more people are catching on to the value of knowledge graphs. I hear fewer and fewer questions this year about the basic concepts of knowledge graphs as fancy databases.
Knowledge Graphs Add Layers of Value, part 1
·linkedin.com·
Knowledge Graphs Add Layers of Value, part 1 | LinkedIn
KGC 2024 review
KGC 2024 review
500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.
KnowledgeGraph
·linkedin.com·
KGC 2024 review
Decoding Kanji Relationships
Decoding Kanji Relationships
What are the concepts that have the most influence in language? For anyone that missed this fun language model + network science community talk, here's the…
·linkedin.com·
Decoding Kanji Relationships
Knowledge Graph Semantic Web Conference
Knowledge Graph Semantic Web Conference
🌐 Announcement: Join Us at the Knowledge Graph Semantic Web Conference! 🌐 Dear colleagues and enthusiasts of the Semantic Web, We are thrilled to extend a…
Knowledge Graph Semantic Web Conference
·linkedin.com·
Knowledge Graph Semantic Web Conference
Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy
Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy
Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy. All the books have… | 146 comments on LinkedIn
Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy
·linkedin.com·
Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy
30 Emerging Technologies That Will Guide Your Business Decisions
30 Emerging Technologies That Will Guide Your Business Decisions
Use this year’s Gartner Emerging Tech Impact Radar to: ☑️Enhance your competitive edge in the smart world ☑️Prioritize prevalent and impactful GenAI use cases that already deliver real value to users ☑️Balance stimulating growth and mitigating risk ☑️Identify relevant emerging technologies that support your strategic product roadmap Explore all 30 technologies and trends: www.gartner.com/en/articles/30-emerging-technologies-that-will-guide-your-business-decisions
·gartner.com·
30 Emerging Technologies That Will Guide Your Business Decisions
GQL in code | LinkedIn
GQL in code | LinkedIn
Lots of gratifying announcements about the GQL standard: Neo4j, TigerGraph, JTC 1, AWS/Neo4j, Memgraph, Stefan the editor, The Register ..
·linkedin.com·
GQL in code | LinkedIn
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to an absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-grounded dialogue generation methods tackle this problem by leveraging facts from Knowledge Graphs (KGs); however, they do not guarantee that the model utilizes a relevant piece of knowledge from the KG. To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG. Specifically, our SURGE framework first retrieves the relevant subgraph from the KG, and then enforces consistency across facts by perturbing their word embeddings conditioned by the retrieved subgraph. Then, we utilize contrastive learning to ensure that the generated texts have high similarity to the retrieved subgraphs. We validate our SURGE framework on OpendialKG and KOMODIS datasets, showing that it generates high-quality dialogues that faithfully reflect the knowledge from KG.
·arxiv.org·
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Graph-based metadata filtering for improving vector search in RAG applications
Graph-based metadata filtering for improving vector search in RAG applications
Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Editor's Note: the following is a guest blog post from Tomaz Bratanic, who focuses on Graph ML and GenAI research at Neo4j. Neo4j is a graph database and analytics company which helps organizations find hidden relationships and patterns
·blog.langchain.dev·
Graph-based metadata filtering for improving vector search in RAG applications