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Abstract. We focus on the factuality property during the extraction of an OpenIE corpus named OpenFact, which contains more than 12 million high-quality knowledge triplets. We break down the factuality property into two important aspects—expressiveness and groundedness—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main arguments of each triplet to contain linked Wikidata1 entities. A human evaluation suggests that the OpenFact triplets are much more accurate and contain denser information compared to OPIEC-Linked (Gashteovski et al., 2019), one recent high-quality OpenIE corpus grounded to Wikidata. Further experiments on knowledge base completion and knowledge base question answering show the effectiveness of OpenFact over OPIEC-Linked as supplementary knowledge to Wikidata as the major KG.

3 metaphors knowldge graphs to empower us, non-technical people, to be more active in the building of the Web of People.

NebulaGraph v3.5.0 is released, which supports full table scan without index and greatly improves FIND PATH performance.

“📈 More Graph DBs in @LangChainAI Graphs can store structured information in a way embeddings can't capture, and we're excited to support even more of them in LangChain: HugeGraph and SPARQL Not only can you query data, but you can also update graph data (!!!) 🧵”

Knowledge graphs are graph-structured collections of facts. And facts are statements that define and describe subject entities in terms of predicates and their…

📢 📢 📢 Amazing milestone! 📢 📢 📢 Neosemantics (n10s) reaches the first million all-time downloads 🤯 Let's keep building Knowledge Graphs together! 💪… | 21 comments on LinkedIn

The growing availability of large collections of digitized artworks has disclosed new opportunities to develop intelligent systems for the automatic a…

Leveraging knowledge graphs for LLMs using LangChain

Modelling data with JSON-LD, Turtle, SHACL. Contribute to mathiasrichter/shapiro development by creating an account on GitHub.

This blog post describes how to get semi-automatically interesting insights of an arts knowledge graph using Knime and Neo4j.

Home page for Industry Knowledge Graph™

brief description of our FOIS2023 paper entitled “a method to improve alignments between domain and foundational ontologies”, focusing on BFO-aligned ontologies

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BioCypher is a framework to simplify the creation of robust biomedical knowledge graphs.

RelationalAI announced today the release of an AI co-processor built for Snowflake, the popular cloud data warehouse provider.

Arrived to SIGMOD in Seattle and it’s an amazing honor that our joint academic/industry work on Property Graph Schema received the Best Industry Paper award.… | 14 comments on LinkedIn

Knowledge graphs are becoming an increasing area of interest in respect to explainability and interpretability of AI. To consolidate my own research and… | 30 comments on LinkedIn

“Link prediction on knowledge graphs is a loosing game, IMHO. Without injecting any new info, you'll only find links similar to those you already had. That's why this work is interesting: injecting external knowledge into link prediction is the only way to find truly new links.”

With the widespread use of large language models (LLMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LLMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. Similar to Multimodal-CoT, we modeled GoT reasoning as a two-stage framework, generating rationales first and then producing the final answer. Specifically, we employ an additional graph-of-thoughts encoder for GoT representation learning and fuse the GoT representation with the original input representation through a gated fusion mechanism. We implement a GoT reasoning model on the T5 pre-trained model and evaluate its performance on a text-only reasoning task (GSM8K) and a multimodal reasoning task (ScienceQA). Our model achieves significant improvement over the strong CoT baseline with 3.41% and 5.08% on the GSM8K test set with T5-base and T5-large architectures, respectively. Additionally, our model boosts accuracy from 84.91% to 91.54% using the T5-base model and from 91.68% to 92.77% using the T5-large model over the state-of-the-art Multimodal-CoT on the ScienceQA test set. Experiments have shown that GoT achieves comparable results to Multimodal-CoT(large) with over 700M parameters, despite having fewer than 250M backbone model parameters, demonstrating the effectiveness of GoT.

The K in “neuro-symbolic” stands for “knowledge” * Frank van Harmelen, Learning & Reasoning Group Vrije Universiteit Amsterdam Creative Commons License CC BY 3...

As organizations adopt AI and natural language processing, knowledge graphs used in graph databases open new opportunities.

Dynamic rewiring and delayed message passing mechanisms offer a tradeoff between standard MPNNs and graph Transformers

Prompting an LLM with an ontology to drive Knowledge Graph extraction from unstructured documents

How we delimit and develop the concept of facts is the key to a deeper, more detailed understanding of knowledge graphs because facts are crucial defining…

This new paper is a wonderful story on how generative AI can be used to help curriculum designers build a Knowledge Space dependency graph: Exploring the MIT… | 11 comments on LinkedIn

Aerospike is expanding its database capabilities with its Aerospike Graph database, which brings graph data model capabilities.

Now apart from #SPARQL (https://lnkd.in/ey_6S_6B) there is also a #SHACL wiki https://lnkd.in/ek9PHN9z It is a first release with much to fix, improve and…

One of my co-workers asked the other day if we could use an LLM to track the relationships between characters in his favorite book series…

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolving by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs; 2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering; and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions.