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Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 4 facts

claimMulti-modal Named Entity Recognition (MNER) aims to leverage images associated with text to improve the identification of named entities.

claimKnowledge graph construction requires scalable methods for the acquisition, transformation, and integration of diverse input data, including structured, semi-structured, and multimodal unstructured data such as textual documents, web data, images, and videos.

referenceZhu et al. focus on the creation of multi-modal knowledge graphs, specifically by combining symbolic knowledge in a knowledge graph with corresponding images.

referenceA survey by Zhu et al. [13] provides a detailed discussion of methods for multi-modal knowledge extraction, which involves creating knowledge graphs from data sources beyond text, such as images.

Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv Nov 7, 2024 2 facts

claimImages and text are the most common input data types used in current neuro-symbolic methods.

claimMost neuro-symbolic AI research currently utilizes unimodal and non-heterogeneous representation spaces, focusing on single data types such as text, images, or structured data.

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv Oct 23, 2025 1 fact

claimMultimodal Knowledge Graph construction aims to integrate heterogeneous modalities, including text, images, audio, and video, into unified, structured representations to enable richer reasoning and cross-modal alignment.

Cybersecurity Trends and Predictions 2025 From Industry Insiders itprotoday.com ITPro Today 1 fact

claimMMS-based cyber attacks function by embedding malicious links within messages containing images or video content to impersonate legitimate businesses or services, luring users into divulging sensitive data.

Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Springer Apr 3, 2023 1 fact

claimThe construction of multi-modal knowledge graphs is complicated and inefficient because it requires the exploration of entities across different modalities, such as texts and images.

The Mechanisms of Psychedelic Visionary Experiences - Frontiers frontiersin.org Frontiers Sep 27, 2017 1 fact

claimAn amodal representational and conceptual system underlies the meaning of both language and images, serving as a common system of semantic representation for the comprehension of events regardless of whether the input modality is language or images, according to Jouena et al. (2015).

In the age of Industrial AI and knowledge graphs, don't overlook the ... symphonyai.com SymphonyAI Aug 12, 2024 1 fact

claimExisting asset hierarchies are rigid, making it difficult to update the operational technology (OT) data landscape when sites add new equipment, replace old equipment, or add new monitoring sources like images and video.

Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv Feb 16, 2025 1 fact

claimNeural networks (NNs) are exemplary in handling unstructured forms of data, such as images, sounds, and textual data.

Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org medRxiv Nov 2, 2025 1 fact

procedureThe Differential Diagnosis Test used the prompt: “Based on the Presentation of Case, Lab Data and Images, what would be the possible diagnosis?”

LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org arXiv Mar 11, 2025 1 fact

perspectiveThe authors plan to enrich the knowledge graph with multimodal data, including images and audio, to provide more comprehensive visual and auditory signals.

Self, selfhood and understanding - infed.org infed.org infed.org 1 fact

claimAccess to meanings, metaphors, images, and stories varies between different groups, and the material and social conditions in which people live foster contrasting ideas of what is important and possible.

A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer Dec 9, 2025 1 fact

claimNeural components in learning for reasoning systems act as heuristic guides or translators that convert unstructured modalities, such as images and text, into symbolic formats, making them amenable to deductive reasoning engines.

Self-awareness, self-regulation, and self-transcendence (S-ART) frontiersin.org Frontiers in Human Neuroscience 1 fact

referenceChadwick et al. (2008) established the reliability and validity of the Southampton mindfulness questionnaire (SMQ) for responding mindfully to unpleasant thoughts and images.