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sergio_101<p>So, I've been learning <a href="https://social.sixdegreesofohio.com/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a> with <a href="https://social.sixdegreesofohio.com/tags/Neo4j" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neo4j</span></a> this week. I think it would be SUPER fun to recursivlely do an mheard on all the <a href="https://social.sixdegreesofohio.com/tags/PacketRadio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PacketRadio</span></a> nodes, to help establish paths around the country.</p>
Matthew Turland<p><a href="https://phpc.social/tags/Klarna" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Klarna</span></a>: "Yes, we did shut down <a href="https://phpc.social/tags/Salesforce" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Salesforce</span></a> a year ago, as we have many <a href="https://phpc.social/tags/SaaS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SaaS</span></a> providers."</p><p><a href="https://threadreaderapp.com/thread/1896698293759230429.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">threadreaderapp.com/thread/189</span><span class="invisible">6698293759230429.html</span></a></p><p><a href="https://phpc.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://phpc.social/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ArtificialIntelligence</span></a> <a href="https://phpc.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLM</span></a> <a href="https://phpc.social/tags/LargeLanguageModels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LargeLanguageModels</span></a> <a href="https://phpc.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a> <a href="https://phpc.social/tags/KnowledgeGraphs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>KnowledgeGraphs</span></a> <a href="https://phpc.social/tags/Neo4J" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neo4J</span></a> <a href="https://phpc.social/tags/Business" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Business</span></a> <a href="https://phpc.social/tags/Banking" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Banking</span></a></p>
Aneesh Sathe<p><strong>Domain Ontologies: Indispensable for Knowledge Graph&nbsp;Construction</strong></p><p>AI slop is all around and increasingly extraction of useful information will face difficulties as we start to feed more noise into the already noisy world of knowledge. We are in an era of unprecedented data abundance, yet this deluge of information often lacks the structure necessary to derive meaningful insights. <strong>Knowledge graphs (KGs), with their ability to represent entities and their relationships as interconnected nodes and edges, have emerged as a powerful tool for managing and leveraging complex data</strong>. However, the efficacy of a KG is critically dependent on the underlying structure provided by domain ontologies. These ontologies, which are formal, machine-readable conceptualizations of a specific field of knowledge, are not merely useful, but essential for the creation of robust and insightful KGs. Let’s explore the role that domain ontologies play in scaffolding KG construction, drawing on various fields such as AI, healthcare, and cultural heritage, to illuminate their importance.</p><a href="https://aneeshsathe.com/wp-content/uploads/2025/01/image-8.png" rel="nofollow noopener" target="_blank"></a><a href="https://en.wikipedia.org/wiki/Wassily_Kandinsky" rel="nofollow noopener" target="_blank">Vassily Kandinsky,</a> 1913 – Composition VII (1913)<br>According to Kandinsky, this is the most complex piece he ever painted.<p>At its core, an ontology is a <strong>formal representation of knowledge within a specific domain</strong>, providing a structured vocabulary and defining the semantic relationships between concepts. In the context of KGs, ontologies serve as the blueprint that defines the types of nodes (entities) and edges (relationships) that can exist within the graph. Without this foundational structure, a KG would be a mere collection of isolated data points with limited utility. The ontology ensures that the KG’s data is not only interconnected but also semantically interoperable. For example, in the biomedical domain, an ontology like the Chemical Entities of Biological Interest (ChEBI) provides a standardized way of representing molecules and their relationships, which is essential for building biomedical KGs. Similarly, in the cultural domain, an ontology provides a controlled vocabulary to define the entities, such as artworks, artists, and historical events, and their relationships, thus creating a consistent representation of cultural heritage information.</p><p>One of the primary reasons domain ontologies are crucial for KGs is their role in <strong>ensuring data consistency and interoperability</strong>. Ontologies provide unique identifiers and clear definitions for each concept, which helps in aligning data from different sources and avoiding ambiguities. Consider, for example, a healthcare KG that integrates data from various clinical trials, patient records, and research publications. Without a shared ontology, terms like “cancer” or “hypertension” may be interpreted differently across these data sets. The use of ontologies standardizes the representation of these concepts, thus allowing for effective integration and analysis. This not only enhances the accuracy of the KG but also makes the information more accessible and reusable. Furthermore, using ontologies that follow the FAIR (Findable, Accessible, Interoperable, Reusable) principles facilitates data integration, unification, and information sharing, essential for building robust KGs.</p><p>Moreover, ontologies <strong>facilitate the application of advanced AI methods to unlock new knowledge</strong>. They support both deductive reasoning to infer new knowledge and provide structured background knowledge for machine learning. In the context of drug discovery, for instance, a KG built on a biomedical ontology can help identify potential drug targets by connecting genes, proteins, and diseases through clearly defined relationships. This structured approach to data also enables the development of explainable AI models, which are critical in fields like medicine where the decision-making process must be transparent and interpretable. The ontology-grounded KGs can then be used to generate hypotheses that can be validated through manual review, in vitro experiments, or clinical studies, highlighting the utility of ontologies in translating complex data into actionable knowledge.</p><p>Despite their many advantages, domain ontologies are not without their challenges. One major hurdle is the <strong>lack of direct integration between data and ontologies</strong>, meaning that most ontologies are abstract knowledge models not designed to contain or integrate data. This necessitates the use of (semi-)automated approaches to integrate data with the ontological knowledge model, which can be complex and resource-intensive. Additionally, the existence of multiple ontologies within a domain can lead to semantic inconsistencies that impede the construction of holistic KGs. Integrating different ontologies with overlapping information may result in semantic irreconcilability, making it difficult to reuse the ontologies for the purpose of KG construction. Careful planning is therefore required when choosing or building an ontology.</p><p>As we move forward, the development of <strong>integrated, holistic solutions</strong> will be crucial to unlocking the full potential of domain ontologies in KG construction. This means creating methods for integrating multiple ontologies, ensuring data quality and credibility, and focusing on semantic expansion techniques to leverage existing resources. Furthermore, there needs to be a greater emphasis on creating ontologies with the explicit purpose of instantiating them, and storing data directly in graph databases. The integration of expert knowledge into KG learning systems, by using ontological rules, is crucial to ensure that KGs not only capture data, but also the logical patterns, inferences, and analytic approaches of a specific domain.</p><p>Domain ontologies will prove to be the key to building robust and useful KGs. They provide the necessary structure, consistency, and interpretability that enables AI systems to extract valuable insights from complex data. By understanding and addressing the challenges associated with ontology design and implementation, we can harness the power of KGs to solve complex problems across diverse domains, from healthcare and science to culture and beyond. The future of knowledge management lies not just in the accumulation of data but in the development of intelligent, ontologically-grounded systems that can bridge the gap between information and meaningful understanding. </p><p><strong>References </strong></p><ol><li>Al-Moslmi, T., El Alaoui, I., Tsokos, C.P., &amp; Janjua, N. (2021). Knowledge graph construction approaches: A survey of recent research works. <em>arXiv preprint</em>. <a href="https://arxiv.org/abs/2011.00235" rel="nofollow noopener" target="_blank">https://arxiv.org/abs/2011.00235</a></li><li>Chandak, P., Huang, K., &amp; Zitnik, M. (2023). PrimeKG: A multimodal knowledge graph for precision medicine. <em>Scientific Data</em>. <a href="https://www.nature.com/articles/s41597-023-01960-3" rel="nofollow noopener" target="_blank">https://www.nature.com/articles/s41597-023-01960-3</a></li><li>Gilbert, S., &amp; others. (2024). Augmented non-hallucinating large language models using ontologies and knowledge graphs in biomedicine. <em>npj Digital Medicine</em>. <a href="https://www.nature.com/articles/s41746-024-01081-0" rel="nofollow noopener" target="_blank">https://www.nature.com/articles/s41746-024-01081-0</a></li><li>Guzmán, A.L., et al. (2022). Applications of Ontologies and Knowledge Graphs in Cancer Research: A Systematic Review. <em>Cancers, 14</em>(8), 1906. <a href="https://www.mdpi.com/2072-6694/14/8/1906" rel="nofollow noopener" target="_blank">https://www.mdpi.com/2072-6694/14/8/1906</a></li><li>Hura, A., &amp; Janjua, N. (2024). Constructing domain-specific knowledge graphs from text: A case study on subprime mortgage crisis. <em>Semantic Web Journal</em>. <a href="https://www.semantic-web-journal.net/content/constructing-domain-specific-knowledge-graphs-text-case-study-subprime-mortgage-crisis" rel="nofollow noopener" target="_blank">https://www.semantic-web-journal.net/content/constructing-domain-specific-knowledge-graphs-text-case-study-subprime-mortgage-crisis</a></li><li>Kilicoglu, H., et al. (2024). Towards better understanding of biomedical knowledge graphs: A survey. <em>arXiv preprint</em>. <a href="https://arxiv.org/abs/2402.06098" rel="nofollow noopener" target="_blank">https://arxiv.org/abs/2402.06098</a></li><li>Noy, N.F., &amp; McGuinness, D.L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. <em>Semantic Scholar</em>. <a href="https://www.semanticscholar.org/paper/Ontology-Development-101%3A-A-Guide-to-Creating-Your-Noy/c15cf32df98969af5eaf85ae3098df6d2180b637" rel="nofollow noopener" target="_blank">https://www.semanticscholar.org/paper/Ontology-Development-101%3A-A-Guide-to-Creating-Your-Noy/c15cf32df98969af5eaf85ae3098df6d2180b637</a></li><li>Taneja, S.B., et al. (2023). NP-KG: A knowledge graph for pharmacokinetic natural product-drug interaction discovery. <em>Journal of Biomedical Informatics</em>. <a href="https://www.sciencedirect.com/science/article/pii/S153204642300062X" rel="nofollow noopener" target="_blank">https://www.sciencedirect.com/science/article/pii/S153204642300062X</a></li><li>Zhao, X., &amp; Han, Y. (2023). Architecture of Knowledge Graph Construction. <em>Semantic Scholar</em>. <a href="https://www.semanticscholar.org/paper/Architecture-of-Knowledge-Graph-Construction-Zhao-Han/dcd600619962d5c1f1cfa08a85d0be43a626b301" rel="nofollow noopener" target="_blank">https://www.semanticscholar.org/paper/Architecture-of-Knowledge-Graph-Construction-Zhao-Han/dcd600619962d5c1f1cfa08a85d0be43a626b301</a></li></ol><p><a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/ai-in-healthcare/" target="_blank">#AIInHealthcare</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/artificial-intelligence-2/" target="_blank">#ArtificialIntelligence</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/biomedical-ontologies/" target="_blank">#BiomedicalOntologies</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/cultural-heritage-data/" target="_blank">#CulturalHeritageData</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-integration/" target="_blank">#DataIntegration</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-interoperability/" target="_blank">#DataInteroperability</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/domain-ontologies/" target="_blank">#DomainOntologies</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/drug-discovery/" target="_blank">#DrugDiscovery</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/explainable-ai/" target="_blank">#ExplainableAI</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/fair-principles/" target="_blank">#FAIRPrinciples</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/graph-databases/" target="_blank">#GraphDatabases</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/knowledge-graphs/" target="_blank">#KnowledgeGraphs</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/knowledge-management/" target="_blank">#KnowledgeManagement</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/llms/" target="_blank">#LLMs</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/ontology/" target="_blank">#Ontology</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/ontology-design/" target="_blank">#OntologyDesign</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/ontology-development/" target="_blank">#OntologyDevelopment</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/ontology-driven-ai/" target="_blank">#OntologyDrivenAI</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/semantic-relationships/" target="_blank">#SemanticRelationships</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/semantic-web/" target="_blank">#SemanticWeb</a></p>
Berlin PyLadies<p>📣 REMINDER 📣 NODES 2024 is right around the corner! We can’t wait to dive into this year’s content on LLMs, AI, data science, knowledge graphs and applications. It’d be amazing if you could join us. Sign up here 👉 <a href="https://neo4j.registration.goldcast.io/events/03805ea9-fe3a-4cac-8c15-aa622666531a?utm_campaign=pyladies_berlin&amp;utm_source=partners" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neo4j.registration.goldcast.io</span><span class="invisible">/events/03805ea9-fe3a-4cac-8c15-aa622666531a?utm_campaign=pyladies_berlin&amp;utm_source=partners</span></a></p><p><a href="https://mastodon.social/tags/graphdatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdatabases</span></a> <a href="https://mastodon.social/tags/knowledgegraphs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>knowledgegraphs</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://mastodon.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://mastodon.social/tags/NODES2024" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NODES2024</span></a> <a href="https://mastodon.social/tags/GraphRAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphRAG</span></a></p>
Berlin PyLadies<p>We are excited to partner with <a href="https://mastodon.social/tags/NODES2024" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NODES2024</span></a>, <a href="https://mastodon.social/tags/Neo4j" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neo4j</span></a>'s ONLINE developer conference! On November 7, join us for 24 hours of free live talks. Don't miss out, sign up today! <a href="https://neo4j.registration.goldcast.io/events/03805ea9-fe3a-4cac-8c15-aa622666531a?utm_campaign=pyladies_berlin&amp;utm_source=partners" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neo4j.registration.goldcast.io</span><span class="invisible">/events/03805ea9-fe3a-4cac-8c15-aa622666531a?utm_campaign=pyladies_berlin&amp;utm_source=partners</span></a> <br><a href="https://mastodon.social/tags/graphdatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdatabases</span></a> <a href="https://mastodon.social/tags/graphs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphs</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://mastodon.social/tags/GraphRAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphRAG</span></a> <a href="https://mastodon.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://mastodon.social/tags/NODES2024" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NODES2024</span></a></p>
PyData Madrid<p>Y sigue Luis Salvador hablando de Neo4j y GenAI</p><p><a href="https://masto.ai/tags/neo4j" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neo4j</span></a> <a href="https://masto.ai/tags/graphdatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdatabases</span></a> <a href="https://masto.ai/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://masto.ai/tags/pydata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pydata</span></a> <a href="https://masto.ai/tags/pydatamadrid" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pydatamadrid</span></a></p>
IT News<p>How TigerGraph CoPilot enables graph-augmented AI - Data has the potential to provide transformative business insights across various indu... - <a href="https://www.infoworld.com/article/3715344/how-tigergraph-copilot-enables-graph-augmented-ai.html#tk.rss_all" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">infoworld.com/article/3715344/</span><span class="invisible">how-tigergraph-copilot-enables-graph-augmented-ai.html#tk.rss_all</span></a> <a href="https://schleuss.online/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>artificialintelligence</span></a> <a href="https://schleuss.online/tags/softwaredevelopment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>softwaredevelopment</span></a> <a href="https://schleuss.online/tags/graphdatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdatabases</span></a> <a href="https://schleuss.online/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> <a href="https://schleuss.online/tags/database" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>database</span></a></p>
IT News<p>Using Neo4J’s graph database for AI in Azure - Once you get past the chatbot hype, it’s clear that generative AI is a useful tool, pr... - <a href="https://www.infoworld.com/article/3715020/using-neo4js-graph-database-for-ai-in-azure.html#tk.rss_all" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">infoworld.com/article/3715020/</span><span class="invisible">using-neo4js-graph-database-for-ai-in-azure.html#tk.rss_all</span></a> <a href="https://schleuss.online/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>artificialintelligence</span></a> <a href="https://schleuss.online/tags/microsoftazure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>microsoftazure</span></a> <a href="https://schleuss.online/tags/graphdatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdatabases</span></a> <a href="https://schleuss.online/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a></p>
Anita Graser 🇪🇺🇺🇦🇬🇪<p><span class="h-card" translate="no"><a href="https://mapstodon.space/@pokateo" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>pokateo</span></a></span> I'm a senior scientist at the <a href="https://fosstodon.org/tags/AIT" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIT</span></a>, developing <a href="https://fosstodon.org/tags/SpatialDataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpatialDataScience</span></a> solutions using <a href="https://fosstodon.org/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenSource</span></a> tools</p><p>I'm the lead developer of <span class="h-card" translate="no"><a href="https://fosstodon.org/@movingpandas" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>movingpandas</span></a></span>, serve on the PSC of <span class="h-card" translate="no"><a href="https://fosstodon.org/@qgis" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>qgis</span></a></span>, and try to keep an eye on <a href="https://fosstodon.org/tags/MobilityDB" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MobilityDB</span></a> &amp; <a href="https://fosstodon.org/tags/MEOS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MEOS</span></a> developments</p><p>I love exploring new ways to work with spatial data. Recently started dabbling with <a href="https://fosstodon.org/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a></p><p><a href="https://fosstodon.org/tags/GISChat" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GISChat</span></a> <a href="https://fosstodon.org/tags/AppliedResearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AppliedResearch</span></a> <a href="https://fosstodon.org/tags/qgis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>qgis</span></a> <a href="https://fosstodon.org/tags/movingpandas" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>movingpandas</span></a></p>
Taylor Riggan<p>Automate expiry of graph components in your Amazon Neptune database using Time to Live (TTL) and the techniques discussed in this blog post series. </p><p><a href="https://aws.amazon.com/blogs/database/implement-time-to-live-in-amazon-neptune-part-1-property-graph/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">aws.amazon.com/blogs/database/</span><span class="invisible">implement-time-to-live-in-amazon-neptune-part-1-property-graph/</span></a></p><p><a href="https://awscommunity.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a> <a href="https://awscommunity.social/tags/graphdb" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdb</span></a> <a href="https://awscommunity.social/tags/AmazonNeptune" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AmazonNeptune</span></a></p>
gabi<p>Friends, now that people are aggregating together, which conferences should I keep an eye on for submissions? My content goes beyond databases fundamentals, language agnostic. I can cover automation, schema tracking, observability, etc.</p><p>Also Vector databases.</p><p><a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/PHP" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PHP</span></a> <a href="https://hachyderm.io/tags/Javascript" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Javascript</span></a> <a href="https://hachyderm.io/tags/SQL" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SQL</span></a> <a href="https://hachyderm.io/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a> <a href="https://hachyderm.io/tags/MySQL" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MySQL</span></a> <a href="https://hachyderm.io/tags/Postgres" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Postgres</span></a></p>
Bert<p>Der Vortrag von <span class="h-card" translate="no"><a href="https://chaos.social/@magomi" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>magomi</span></a></span> und mir vor einiger Zeit zu <a href="https://chaos.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a> bei der SECO ist nun auf yt verfügbar.<br><a href="https://youtu.be/BFTPGB3S80Q" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/BFTPGB3S80Q</span><span class="invisible"></span></a></p>
IT News<p>How knowledge graphs improve generative AI - The initial surge of excitement and apprehension surrounding ChatGPT is waning. The pr... - <a href="https://www.infoworld.com/article/3707814/how-knowledge-graphs-improve-generative-ai.html#tk.rss_all" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">infoworld.com/article/3707814/</span><span class="invisible">how-knowledge-graphs-improve-generative-ai.html#tk.rss_all</span></a> <a href="https://schleuss.online/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>artificialintelligence</span></a> <a href="https://schleuss.online/tags/softwaredevelopment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>softwaredevelopment</span></a> <a href="https://schleuss.online/tags/graphdatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdatabases</span></a> <a href="https://schleuss.online/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> <a href="https://schleuss.online/tags/database" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>database</span></a></p>
Ben Lorica 罗瑞卡<p><a href="https://indieweb.social/tags/TheDataExchangePod" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TheDataExchangePod</span></a> 🎧 Emil Eifrem of Neo4j unlocks the secrets of <a href="https://indieweb.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a>, <a href="https://indieweb.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLMs</span></a>, <a href="https://indieweb.social/tags/VectorDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>VectorDatabases</span></a>, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of <a href="https://indieweb.social/tags/GraphDataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDataScience</span></a> &amp; retrieval-augmented LLMs </p><p><a href="https://indieweb.social/tags/nlproc" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlproc</span></a> <a href="https://indieweb.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://indieweb.social/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> <br>🔗 <a href="https://thedataexchange.media/the-future-of-graph-databases/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thedataexchange.media/the-futu</span><span class="invisible">re-of-graph-databases/</span></a></p>
Ben Lorica 罗瑞卡<p><a href="https://indieweb.social/tags/TheDataExchangePod" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TheDataExchangePod</span></a> 🎧 Emil Eifrem of Neo4j unlocks the secrets of <a href="https://indieweb.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a>, <a href="https://indieweb.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLMs</span></a>, <a href="https://indieweb.social/tags/VectorDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>VectorDatabases</span></a>, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of <a href="https://indieweb.social/tags/GraphDataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDataScience</span></a> &amp; retrieval-augmented LLMs </p><p><a href="https://indieweb.social/tags/nlproc" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlproc</span></a> <a href="https://indieweb.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://indieweb.social/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> <br>🔗 <a href="https://thedataexchange.media/the-future-of-graph-databases/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thedataexchange.media/the-futu</span><span class="invisible">re-of-graph-databases/</span></a></p>
Ben Lorica 罗瑞卡<p><a href="https://indieweb.social/tags/TheDataExchangePod" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TheDataExchangePod</span></a> 🎧 Emil Eifrem of Neo4j unlocks the secrets of <a href="https://indieweb.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a>, <a href="https://indieweb.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLMs</span></a>, <a href="https://indieweb.social/tags/VectorDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>VectorDatabases</span></a>, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of <a href="https://indieweb.social/tags/GraphDataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDataScience</span></a> &amp; retrieval-augmented LLMs </p><p><a href="https://indieweb.social/tags/nlproc" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlproc</span></a> <a href="https://indieweb.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://indieweb.social/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> <br>🔗 <a href="https://thedataexchange.media/the-future-of-graph-databases/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thedataexchange.media/the-futu</span><span class="invisible">re-of-graph-databases/</span></a></p>
peteo<p>I'm finding that looking at <a href="https://mastodon.nz/tags/meetup" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>meetup</span></a> groups is a great reminder how quickly technologies are moving</p><p><a href="https://mastodon.nz/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a> - never really happened<br><a href="https://mastodon.nz/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BigData</span></a> - so 10 years ago<br><a href="https://mastodon.nz/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NLP</span></a> - who doesn't know what a <a href="https://mastodon.nz/tags/llama" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>llama</span></a> is nowdays?<br><a href="https://mastodon.nz/tags/Crypto" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Crypto</span></a> - well, that didn't turn out so well</p>
Ben Lorica 罗瑞卡<p><a href="https://indieweb.social/tags/TheDataExchangePod" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TheDataExchangePod</span></a> 🎧 Emil Eifrem of Neo4j unlocks the secrets of <a href="https://indieweb.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a>, <a href="https://indieweb.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLMs</span></a>, <a href="https://indieweb.social/tags/VectorDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>VectorDatabases</span></a>, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of <a href="https://indieweb.social/tags/GraphDataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDataScience</span></a> &amp; retrieval-augmented LLMs </p><p><a href="https://indieweb.social/tags/nlproc" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlproc</span></a> <a href="https://indieweb.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://indieweb.social/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> <br>🔗 <a href="https://thedataexchange.media/the-future-of-graph-databases/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thedataexchange.media/the-futu</span><span class="invisible">re-of-graph-databases/</span></a></p>
Ben Lorica 罗瑞卡<p><a href="https://indieweb.social/tags/TheDataExchangePod" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TheDataExchangePod</span></a> 🎧 Emil Eifrem of Neo4j unlocks the secrets of <a href="https://indieweb.social/tags/GraphDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDatabases</span></a>, <a href="https://indieweb.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLMs</span></a>, <a href="https://indieweb.social/tags/VectorDatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>VectorDatabases</span></a>, and more. We examine the interplay of these elements with knowledge graphs and applications of graph neural networks. We explore the rise of new database companies and delve into the world of <a href="https://indieweb.social/tags/GraphDataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphDataScience</span></a> &amp; retrieval-augmented LLMs <br><a href="https://indieweb.social/tags/nlproc" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nlproc</span></a> <a href="https://indieweb.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://indieweb.social/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> </p><p>🔗 <a href="https://thedataexchange.media/the-future-of-graph-databases/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">thedataexchange.media/the-futu</span><span class="invisible">re-of-graph-databases/</span></a></p>
Sevoris<p>My current technological obsession in the near-term are O(1)-cost edge traversal <a href="https://mastodon.social/tags/graphdatabases" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>graphdatabases</span></a> as backing technology for note-taking with increasing structured data and logic. A normal B-tree index apparently isn‘t so expensive when you‘re doing analysis across the whole database, but when most of the data structure isn‘t of interest to a query per definition, things look a little different. And a good note-taking system doesn‘t shrink. It grows for years and decades, absorbing information.</p>