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Temporal multi-modal knowledge graph generation for link prediction

NEURAL NETWORKS 2025年 185卷 107108页

作者： Li, Yuandi Ji, Hui Yu, Fei Cheng, Lechao Che, Nan Jiangsu Univ Zhenjiang 212013 Peoples R China Liaoning Univ Technol Jinzhou 121001 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Harbin Univ Sci & Technol Harbin 150006 Peoples R China

Temporal Multi-Modal knowledge graphs (TMMKGs) can be regarded as a synthesis of Temporal knowledge graphs (TKGs) and Multi-Modal knowledge graphs (MMKGs), combining the characteristics of both. TMMKGs can effectively model dynamic real-world phenomena, particularly in scenarios involving multiple heterogeneous information sources and time series characteristics, such as e-commerce websites, scene recording data, and intelligent transportation systems. We propose a Temporal Multi-Modal knowledge graph generation (TMMKGG) method that can automatically construct TMMKGs, aiming to reduce construction costs. To support this, we construct a dynamic Visual-Audio-Language Multimodal (VALM) dataset, which is particularly suitable for extracting structured knowledge in response to temporal multimodal perception data. TMMKGG explores temporal dynamics and cross-modal integration, enabling multimodal data processing for dynamic knowledge graph generation and utilizing alignment strategies to enhance scene perception. To validate the effectiveness of TMMKGG, we compare it with state-of-the-art dynamic graph generation methods using the VALM dataset. Furthermore, TMMKG exhibits a significant disparity in the ratio of newly introduced entities to their associated newly introduced edges compared to TKGs. Based on this phenomenon, we introduce a Temporal Multi-Modal Link Prediction (TMMLP) method, which outperforms existing state-of-the-art techniques.

关键词： Multimodal knowledge graph Temporal knowledge graphs knowledge graph generation Link prediction

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Text2KGBench: A Benchmark for Ontology-Driven knowledge graph generation from Text 1

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22nd International Semantic Web Conference (ISWC)

作者： Mihindukulasooriya, Nandana Tiwari, Sanju Enguix, Carlos F. Lata, Kusum IBM Res Europe Dublin Ireland Univ Autonoma Tamaulipas Victoria Mexico Sharda Univ Greater Noida India

ISBN: (数字)9783031472435

ISBN: (纸本)9783031472428;9783031472435

The recent advances in large language models (LLM) and foundation models with emergent capabilities have been shown to improve the performance of many NLP tasks. LLMs and knowledge graphs (KG) can complement each other such that LLMs can be used for KG construction or completion while existing KGs can be used for different tasks such as making LLM outputs explainable or fact-checking in Neuro-Symbolic manner. In this paper, we present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences. We provide two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences. We define seven evaluation metrics to measure fact extraction performance, ontology conformance, and hallucinations by LLMs. Furthermore, we provide results for two baseline models, Vicuna-13B and Alpaca-LoRA-13B using automatic prompt generation from test cases. The baseline results show that there is room for improvement using both Semantic Web and Natural Language Processing techniques. Resource Type: Evaluation Benchmark Source Repo: https://***/cenguix/Text2KGBench

关键词： Benchmark Relation Extraction knowledge graph knowledge graph generation Large Language Models

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AI Supported knowledge graph Design and generation

AI Supported Knowledge Graph Design and Generation

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21st International Conference on The Semantic Web (ESWC)

作者： Ratta, Marco Open Univ Walton Hall Milton Keynes Bucks England

ISBN: (纸本)9783031789540;9783031789557

knowledge graphs (KG) have risen to be a powerful mechanism to represent data. Despite this most data sources are generally still represented via heterogeneous non-graph data structures. Converting these into KGs necessitates considerable effort from experts, proving this to be a time consuming process. While tools have been developed to aid KG builders, a gap still exists in terms of technologies that support the automation of designing KG building pipelines. Addressing this gap motivates this research. The aim is to first understand the problem at the knowledge level and, inspired by the recent release of generative tools such as GPT-Engineer, to put forward a conversational agent aimed at assisting the user in building their pipelines. We report on the preliminary findings that we have so far reached during the first year of research in deriving the requirements for building KG generating pipelines from the literature.

关键词： knowledge graph generation knowledge graph Design Artificial Intelligence knowledge graph Refinement

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Helio: A framework for implementing the life cycle of knowledge graphs

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SEMANTIC WEB 2024年第1期15卷 223-249页

作者： Cimmino, Andrea Garcia-Castro, Raul Univ Politecn Madrid Ontol Engn Grp Madrid Spain

Building and publishing knowledge graphs (KG) as Linked Data, either on the Web or in private companies, has become a relevant and crucial process in many domains. This process requires that users perform a wide number of tasks conforming to the life cycle of a KG, and these tasks usually involve different unrelated research topics, such as RDF materialisation or link discovery. There is already a large corpus of tools and methods designed to perform these tasks;however, the lack of one tool that gathers them all leads practitioners to develop ad-hoc pipelines that are not generic and, thus, non-reusable. As a result, building and publishing a KG is becoming a complex and resource-consuming process. In this paper, a generic framework called Helio is presented. The framework aims to cover a set of requirements elicited from the KG life cycle and provide a tool capable of performing the different tasks required to build and publish KGs. As a result, Helio aims at providing users with the means for reducing the effort required to perform this process and, also, Helio aims to prevent the development of ad-hoc pipelines. Furthermore, the Helio framework has been applied in many different contexts, from European projects to research work.

关键词： knowledge graph generation knowledge graph publication linked data

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IICONgraph: Improved Iconographic and Iconological Statements in knowledge graphs 21st

IICONGRAPH: Improved Iconographic and Iconological Statement...

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21st International Conference on The Semantic Web (ESWC)

作者： Sartini, Bruno Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9783031606342;9783031606359

Iconography and iconology are fundamental domains when it comes to understanding artifacts of cultural heritage (CH). Iconography deals with the study and interpretation of visual elements depicted in artifacts and their symbolism, while iconology delves deeper, exploring the underlying cultural and historical meanings. Despite the advances in representing CH with Linked Open Data (LOD), recent studies show persistent gaps in the representation of iconographic and iconological statements in current knowledge graphs (KGs). To address them, this paper presents IICONgraph, a KG that was created by refining and extending the iconographic and iconological statements of ArCo (the Italian KG of CH) and Wikidata. The development of IICONgraph was also driven by a series of requirements emerging from research case studies expressed in competency questions (CQs) that were unattainable in the non-reengineered versions of the KGs. The evaluation results demonstrate that IICONgraph not only outperforms ArCo and Wikidata through domain-specific assessments from the literature but also serves as a robust platform for answering the formulated CQs. IICONgraph is released and documented in accordance with the FAIR principles to guarantee the resource's reusability. The algorithms used to create it and assess the CQs have also been made available to ensure transparency and reproducibility. While future work focuses on ingesting more data into the KG, and on implementing it as a backbone of LLM-based question answering systems, the current version of IICONgraph still emerges as a valuable asset, contributing to the evolving landscape of CH representation within KGs, the Semantic Web, and beyond.

关键词： knowledge graph Iconography Iconology Symbolism Cultural Heritage Digital Humanities knowledge graph generation

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Towards a methodology for the semi-automatic generation of scientific knowledge graphs from XML documents 18

Towards a methodology for the semi-automatic generation of s...

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18th International Workshop on Ontology Matching, OM 2023

作者： Hannah, George Payne, Terry R. Tamma, Valentina Mitchell, Andrew Piercy, Ellen Konev, Boris University of Liverpool Foundation Building Brownlow Hill LiverpoolL69 7ZX United Kingdom Materials Innovation Factory University of Liverpool 51 Oxford Street LiverpoolL7 3NY United Kingdom

Robots used in analytical laboratories, such as those at Unilever, generate vast amounts of log data. This log data is typically stored in semi-structured formats (e.g. XML) according to some standard schema, e.g. the Analytical Information Markup Language (AnIML). Representing this data in a structured format such as a knowledge graph would allow for a more consistent data interpretation, as the relationships between concepts would be formalised in an ontology;consequently making the process of complex data analysis simpler for the scientists involved. We propose a semi-automatic pipeline that exploits the inherent structure of XML schemata, as well as previously represented domain knowledge, to create a knowledge graph that represents log data with its relevant metadata. We utilise ontology alignment techniques to identify related concepts in different ontologies, and therefore provide additional context when predicting the property linking two classes while building the graph. © 2023 Copyright for this paper by its authors.

关键词： knowledge graph generation Ontology alignment XML

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RMLStreamer-SISO: An RDF Stream Generator from Streaming Heterogeneous Data 21st

RMLStreamer-SISO: An RDF Stream Generator from Streaming Het...

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21st International Semantic Web Conference (ISWC)

作者： Oo, Sitt Min Haesendonck, Gerald De Meester, Ben Dimou, Anastasia Univ Ghent Dept Elect & Informat Syst IDLab IMEC Ghent Belgium KULeuven Dept Comp Sci Leuven Belgium AI Flanders Make Lommel Belgium

ISBN: (纸本)9783031194320;9783031194337

Stream-reasoning query languages such as CQELS and C-SPARQL enable query answering over RDF streams. Unfortunately, there currently is a lack of efficient RDF stream generators to feed RDF stream reasoners. State-of-the-art RDF stream generators are limited with regard to the velocity and volume of streaming data they can handle. To efficiently generate RDF streams in a scalable way, we extended the RMLStreamer to also generate RDF streams from dynamic heterogeneous data streams. This paper introduces a scalable solution that relies on a dynamic window approach to generate RDF streams with low latency and high throughput from multiple heterogeneous data streams. Our evaluation shows that our solution outperforms the state-of-the-art by achieving millisecond latency (compared to seconds that state-of-the-art solutions need), constant memory usage for all workloads, and sustainable throughput of around 70,000 records/s (compared to 10,000 records/s that state-of-the-art solutions take). This opens up the access to numerous data streams for integration with the semantic web.

关键词： RML Stream processing Window joins knowledge graph generation

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PooRaa-Agri KG: An agricultural knowledge graph-based simplified multilingual query system

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EXPERT SYSTEMS 2023年第10期40卷 e13434-e13434页

作者： Sivakumar, Nethraa Srinivasan, Pooja Kannan, Mrinalini Vijayalakshmi, P. Nagarajan, T. Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Commun & Engn Chennai Tamil Nadu India Shiv Nadar Univ Dept Comp Sci & Engn Chennai Tamil Nadu India

The current work proposes PooRaa-Agri KG, an agricultural knowledge graph-based simplified multilingual query system that works in real time to provide concise answers for agriculture-based queries. The proposed approach accommodates real-time and low-resource queries in English and Hindi with a novel multi-stage solution consisting of data pre-processing, sentence simplification, triplet extraction, knowledge graph generation, sentence reconstruction, query-to-reconstructed sentence matching, and machine translation as its sub-modules. In this work, a novel combination of rule-based sentence simplification and triplet extraction is carried out resulting in a triplet similarity score of 86.56% for the extracted triplets. This method is superior to the existing triplet extraction method whose triplet similarity score was found to be 60.65%. Further, the proposed work makes use of heuristic rules to reconstruct sentences which when evaluated by human evaluators for meaningfulness and grammar resulted in a score of 3.09/4 and 2.95/4 respectively. To complete end-to-end communication in the proposed system, a similarity-based query answer system is proposed in this work.

关键词： knowledge graph generation multilingual query system sentence segmentation triplet extraction

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Intent Classification by the Use of Automatically Generated knowledge graphs

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INFORMATION 2023年第5期14卷 288页

作者： Arcan, Mihael Manjunath, Sampritha Robin, Cecile Verma, Ghanshyam Pillai, Devishree Sarkar, Simon Dutta, Sourav Assem, Haytham McCrae, John P. Buitelaar, Paul Univ Galway Data Sci Inst Insight SFI Res Ctr Data Analyt Galway H91AEX4 Ireland Huawei Res Dublin D02 R156 Ireland Amazon Alexa AI Cambridge CB1 2GA England

Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers' goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs for targeted data sets to capture domain-specific knowledge and leverage embeddings trained on these knowledge graphs for the intent classification task. As existing knowledge graphs might not be suitable for a targeted domain of interest, our automatic generation of knowledge graphs can extract the semantic information of any domain, which can be incorporated within the classification process. We compare our results with state-of-the-art pre-trained sentence embeddings and our evaluation of three data sets shows improvement in the intent classification task in terms of precision.

关键词： intent classification term extraction named entity extraction relation extraction knowledge graph generation

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Ontology Learning Applications of knowledge Base Construction for Microelectronic Systems Information

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INFORMATION 2023年第3期14卷 176页

作者： Wawrzik, Frank Rafique, Khushnood Adil Rahman, Farin Grimm, Christoph TU Kaiserslautern WG Design Cyber Phys Syst D-67663 Kaiserslautern Germany

knowledge base construction (KBC) using AI has been one of the key goals of this highly popular technology since its emergence, as it helps to comprehend everything, including relations, around us. The construction of knowledge bases can summarize a piece of text in a machine-processable and understandable way. This can prove to be valuable and assistive to knowledge engineers. In this paper, we present the application of natural language processing in the construction of knowledge bases. We demonstrate how a trained bidirectional long short-term memory or bi-LSTM neural network model can be used to construct knowledge bases in accordance with the exact ISO26262 definitions as defined in the GENIAL! Basic Ontology. We provide the system with an electronic text document from the microelectronics domain and the system attempts to create a knowledge base from the available information in textual format. This information is then expressed in the form of graphs when queried by the user. This method of information retrieval presents the user with a much more technical and comprehensive understanding of an expert piece of text. This is achieved by applying the process of named entity recognition (NER) for knowledge extraction. This paper provides a result report of the current status of our knowledge construction process and knowledge base content, as well as describes our challenges and experiences.

关键词： ontology learning ISO26262 natural language processing named entity recognition ontology knowledge graph generation classification lessons learned POS tagging

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