GraphNews

In the realm of Artificial Intelligence (AI), ontology engineering plays a pivotal role in structuring knowledge and facilitating semantic…

In the case of interpersonal knowledge graphs, everyone in the community can contribute to the disambiguation and enrichment of the community’s online presence, and at the same time help with findability, accessibility, interoperability and reuse (the FAIR principles). And that applies not only to someone else finding your path to research discovery, but you being able to retrace your own steps whenever you need to.

Current anti-money laundering (AML) systems, predominantly rule-based, exhibit notable shortcomings in efficiently and precisely detecting instances of money laundering. As a result, there has been a recent surge toward exploring alternative approaches, particularly those utilizing machine learning. Since criminals often collaborate in their money laundering endeavors, accounting for diverse types of customer relations and links becomes crucial. In line with this, the present paper introduces a graph neural network (GNN) approach to identify money laundering activities within a large heterogeneous network constructed from real-world bank transactions and business role data belonging to DNB, Norway's largest bank. Specifically, we extend the homogeneous GNN method known as the Message Passing Neural Network (MPNN) to operate effectively on a heterogeneous graph. As part of this procedure, we propose a novel method for aggregating messages across different edges of the graph. Our findings highlight the importance of using an appropriate GNN architecture when combining information in heterogeneous graphs. The performance results of our model demonstrate great potential in enhancing the quality of electronic surveillance systems employed by banks to detect instances of money laundering. To the best of our knowledge, this is the first published work applying GNN on a large real-world heterogeneous network for anti-money laundering purposes.

What Mark said. Three things have been perennially true ever since we started Neo4j and kicked off the graph database category: 1) Every year or so there's a…

Redis Inc. is phasing out RedisGraph. This blog post explains the motivation behind this decision and the implications for existing customers and community members.

Knowledge graphs – a graphic representation of entities in a linked network − have the potential to solve many data integration challenges that pose a significant barrier to the use of AI.

While still a bit of an outlier, graph-oriented databases continue to find a role in the modern data stack -- thanks largely to AI.

We have seen recently a surge in vector databases in this era of generative AI. The idea behind vector databases is to index the data with vectors that relate… | 30 comments on LinkedIn

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...