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Artykuły w czasopismach na temat „Knowledge Graphs (KG)”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 25 lipca 2025

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1

Khan, Arijit. "Knowledge Graphs Querying." ACM SIGMOD Record 52, no. 2 (2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.

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Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning rece

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Teern, Anna, Markus Kelanti, Tero Päivärinta, and Mika Karaila. "Design Objectives for Evolvable Knowledge Graphs." Complex Systems Informatics and Modeling Quarterly, no. 36 (October 31, 2023): 1–15. http://dx.doi.org/10.7250/csimq.2023-36.01.

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Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting

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Chen, Xuelu, Muhao Chen, Weijia Shi, Yizhou Sun, and Carlo Zaniolo. "Embedding Uncertain Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3363–70. http://dx.doi.org/10.1609/aaai.v33i01.33013363.

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Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by

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Li, Tongxin, Weiping Wang, Xiaobo Li, Tao Wang, Xin Zhou, and Meigen Huang. "Embedding Uncertain Temporal Knowledge Graphs." Mathematics 11, no. 3 (2023): 775. http://dx.doi.org/10.3390/math11030775.

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Knowledge graph (KG) embedding for predicting missing relation facts in incomplete knowledge graphs (KGs) has been widely explored. In addition to the benchmark triple structural information such as head entities, tail entities, and the relations between them, there is a large amount of uncertain and temporal information, which is difficult to be exploited in KG embeddings, and there are some embedding models specifically for uncertain KGs and temporal KGs. However, these models either only utilize uncertain information or only temporal information, without integrating both kinds of informatio

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Bellomarini, Luigi, Marco Benedetti, Andrea Gentili, Davide Magnanimi, and Emanuel Sallinger. "KG-Roar: Interactive Datalog-Based Reasoning on Virtual Knowledge Graphs." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4014–17. http://dx.doi.org/10.14778/3611540.3611609.

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Logic-based Knowledge Graphs (KGs) are gaining momentum in academia and industry thanks to the rise of expressive and efficient languages for Knowledge Representation and Reasoning (KRR). These languages accurately express business rules, through which valuable new knowledge is derived. A versatile and scalable backend reasoner, like Vadalog, a state-of-the-art system for logic-based KGs---based on an extension of Datalog---executes the reasoning. In this demo, we present KG-Roar, a web-based interactive development and navigation environment for logical KGs. The system lets the user augment a

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Bizon, Chris, Steven Cox, James Balhoff, et al. "ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources." Journal of Chemical Information and Modeling 59, no. 12 (2019): 4968–73. http://dx.doi.org/10.1021/acs.jcim.9b00683.

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Gu, Qianqian, Ben Scott, and Vincent Smith. "Enhancing Botanical Knowledge Graphs with Machine Learning." Biodiversity Information Science and Standards 6 (August 23, 2022): e91384. https://doi.org/10.3897/biss.6.91384.

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Integrating sparse and incomplete biodiversity data into a global, coherent data space and generating machine-readable data infrastructures is a challenge in biodiversity informatics. In recent years, biodiversity data researchers have started proposing Knowledge Graphs (KGs) as one approach to connecting biodiversity data worldwide (Page 2019), representing the connections between the what, when, and where of objects in natural history collections. At the Natural History Museum (NHM) we have constructed a KG of botanical specimens and collectors, encoded into numerical representations, and us

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Orogat, Abdelghny, and Ahmed El-Roby. "Maestro: Automatic Generation of Comprehensive Benchmarks for Question Answering Over Knowledge Graphs." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–24. http://dx.doi.org/10.1145/3589322.

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Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried knowledge graph's vocabulary and structure, as well as the syntax of the structured query language used to express the user's information needs. To increase the user base of these KGs, a set of Question Answering (QA) systems that use natural language to query these knowledge graphs have been introduced. However, finding a benchmark that accurately evaluates the quality of a QA system is a difficult task due to (1) the h

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Niu, Guanglin, Yongfei Zhang, Bo Li, et al. "Rule-Guided Compositional Representation Learning on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2950–58. http://dx.doi.org/10.1609/aaai.v34i03.5687.

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Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability

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10

Cui, Yuanning, Yuxin Wang, Zequn Sun, et al. "Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4217–24. http://dx.doi.org/10.1609/aaai.v37i4.25539.

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Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG w

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11

Khan, Arijit, Tianxing Wu, and Xi Chen. "LLM+KG@VLDB 24 Workshop Summary." ACM SIGMOD Record 54, no. 2 (2025): 60–65. https://doi.org/10.1145/3749116.3749132.

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The unification of large language models (LLMs) and knowledge graphs (KGs) has emerged as a hot topic. At the LLM+KG'24 workshop, co-located with VLDB 2024 in Guangzhou, China, the key theme explored was important data management challenges and opportunities due to the effective interaction between LLMs and KGs. The report outlines major directions and approaches presented by various speakers during the workshop.

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Mohoney, Jason, Anil Pacaci, Shihabur Rahman Chowdhury, et al. "High-Throughput Vector Similarity Search in Knowledge Graphs." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–25. http://dx.doi.org/10.1145/3589777.

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There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems propose augmenting query processing with online vector similarity search. In this work, we explore vector similarity search in the context of Knowledge Graphs (KGs). Motivated by the tasks of finding related KG queries and entities for past KG query workloads, we focus on hybrid vector similarity search (hybrid queries for short) where part of the query corresponds to vector similarity search and part of the query co

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Hofer, Marvin, Daniel Obraczka, Alieh Saeedi, Hanna Köpcke, and Erhard Rahm. "Construction of Knowledge Graphs: Current State and Challenges." Information 15, no. 8 (2024): 509. http://dx.doi.org/10.3390/info15080509.

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With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources (e.g., text) and structured data sources (e.g., databases) are mostly well researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the mai

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14

Hsu, Chao-Chun, Zi-Yuan Chen, Chi-Yang Hsu, et al. "Knowledge-Enriched Visual Storytelling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7952–60. http://dx.doi.org/10.1609/aaai.v34i05.6303.

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Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on th

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Mavromatis, Costas, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, et al. "TempoQR: Temporal Question Reasoning over Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5825–33. http://dx.doi.org/10.1609/aaai.v36i5.20526.

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Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding

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Guo, Lingbing, Qingheng Zhang, Wei Hu, Zequn Sun, and Yuzhong Qu. "Learning to Complete Knowledge Graphs with Deep Sequential Models." Data Intelligence 1, no. 3 (2019): 289–308. http://dx.doi.org/10.1162/dint_a_00016.

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Knowledge graph (KG) completion aims at filling the missing facts in a KG, where a fact is typically represented as a triple in the form of ( head, relation, tail). Traditional KG completion methods compel two-thirds of a triple provided (e.g., head and relation) to predict the remaining one. In this paper, we propose a new method that extends multi-layer recurrent neural networks (RNNs) to model triples in a KG as sequences. It obtains state-of-the-art performance on the common entity prediction task, i.e., giving head (or tail) and relation to predict the tail (or the head), using two benchm

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Krinkin, Kirill, Alexander Ivanovich Vodyaho, Igor Kulikov, and Nataly Zhukova. "Deductive Synthesis of Networks Hierarchical Knowledge Graphs." International Journal of Embedded and Real-Time Communication Systems 12, no. 3 (2021): 32–48. http://dx.doi.org/10.4018/ijertcs.2021070103.

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The article focuses on developing of a deductive synthesis method for building telecommunications networks (TN) hierarchical knowledge graphs (KG). Synthesized KGs can be used to solve search, analytical, and recommendation (forecast) problems. TNs are complex heterogeneous objects. The synthesis of knowledge graphs of such objects requires much computational resources. The proposed method provides a low complexity of the synthesis of KG of TN by taking into account their hierarchical structure. The authors propose to do synthesis by direct downward multilevel inference and reverse multilevel

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Bu, *Chenyang, Xingchen Yu, Yan Hong, and Tingting Jiang. "Low-Quality Error Detection for Noisy Knowledge Graphs." Journal of Database Management 32, no. 4 (2021): 48–64. http://dx.doi.org/10.4018/jdm.2021100104.

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The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple <New_York, IsCapitalOf, America> satisfies the constraints that the head entity “New_York” is a city and the tail entity “Ame

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Kejriwal, Mayank. "Knowledge Graphs: A Practical Review of the Research Landscape." Information 13, no. 4 (2022): 161. http://dx.doi.org/10.3390/info13040161.

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Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph. However, another factor that is less discussed, but which has been equally instrumental in the success of KGs, is the cross-disci

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Yang, Xu, Ziyi Huan, Yisong Zhai, and Ting Lin. "Research of Personalized Recommendation Technology Based on Knowledge Graphs." Applied Sciences 11, no. 15 (2021): 7104. http://dx.doi.org/10.3390/app11157104.

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Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algor

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Dash, Sanjeeb, and Joao Goncalves. "Rule Induction in Knowledge Graphs Using Linear Programming." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4233–41. http://dx.doi.org/10.1609/aaai.v37i4.25541.

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We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. Our LP model chooses a set of rules of bounded complexity from a list of candidate first-order logic rules and assigns weights to them. The complexity bound is enforced via explicit constraints. We combine simple rule generation heuristics with our rule selection LP to obtain predictions with accuracy comparable to state-of-the-art codes, even while generating much more compact rule sets. F

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Debruyne, Christophe, Gary Munnelly, Lynn Kilgallon, Declan O’Sullivan, and Peter Crooks. "Creating a Knowledge Graph for Ireland’s Lost History: Knowledge Engineering and Curation in the Beyond 2022 Project." Journal on Computing and Cultural Heritage 15, no. 2 (2022): 1–25. http://dx.doi.org/10.1145/3474829.

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The Beyond 2022 project aims to create a virtual archive by digitally reconstructing and digitizing historical records lost in a catastrophic fire which consumed items in the Public Record Office of Ireland in 1922. The project is developing a knowledge graph (KG) to facilitate information retrieval and discovery over the reconstructed items. The project decided to adopt Semantic Web technologies to support its distributed KG and reasoning. In this article, we present our approach to KG generation and management. We elaborate on how we help historians contribute to the KG (via a suite of sprea

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Destandau, Marie, and Jean-Daniel Fekete. "The missing path: Analysing incompleteness in knowledge graphs." Information Visualization 20, no. 1 (2021): 66–82. http://dx.doi.org/10.1177/1473871621991539.

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Knowledge Graphs (KG) allow to merge and connect heterogeneous data despite their differences; they are incomplete by design. Yet, KG data producers need to ensure the best level of completeness, as far as possible. The difficulty is that they have no means to distinguish cases where incomplete entities could and should be fixed. We present a new visualization tool: The Missing Path, to support them in identifying coherent subsets of entities that can be repaired. It relies on a map, grouping entities according to their incomplete profile. The map is coordinated with histograms and stacked cha

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Xie, Luodi, Huimin Huang, and Qing Du. "A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs." Applied Sciences 12, no. 2 (2022): 715. http://dx.doi.org/10.3390/app12020715.

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Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional r

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Zhou, Ziwei. "Analysis of recommendation systems based on knowledge graphs." Applied and Computational Engineering 69, no. 1 (2024): 200–206. http://dx.doi.org/10.54254/2755-2721/69/20241529.

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Due to the rapid development of internet technology, recommendation systems have played a crucial role in improving user experience and enhancing user retention. Knowledge graphs (KG), a technique capable of capturing complex semantic relationships and contextual information, are gradually included in recommendation systems to augment their accuracy and intelligence. This paper reviews the application of knowledge graphs in recommendation systems, analyzing their unique advantages in handling user-item relationships. This paper comprehensively analyzes embedding methods based on tensor decompo

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Wang, Xiaxia, Tengteng Lin, Weiqing Luo, Gong Cheng, and Yuzhong Qu. "CKGSE: A Prototype Search Engine for Chinese Knowledge Graphs." Data Intelligence 4, no. 1 (2022): 41–65. http://dx.doi.org/10.1162/dint_a_00118.

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Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detaile

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Dr.S.Anusooya, S.M.Kamali, and Kandaneri Ramamoorthy Saravanan. "KGCD: Leveraging Knowledge Graphs for Intelligent Curriculum Design in Education." Recent Trends in Cloud Computing and Web Engineering 7, no. 1 (2024): 1–9. https://doi.org/10.5281/zenodo.13756522.

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<em>Curriculum design is a critical aspect of education, requiring careful consideration of content relevance, student progression, and pedagogical coherence. In recent years, the use of Knowledge Graphs (KG) has gained attention for their ability to represent complex relationships between concepts in a structured format. This paper introduces KGCD (Knowledge Graph-based Curriculum Design), a novel approach to intelligent curriculum design that leverages knowledge graphs to model subject matter interdependencies, skill progression, and student learning paths. By incorporating AI-driven insight

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Tong, Peihao, Qifan Zhang, and Junjie Yao. "Leveraging Domain Context for Question Answering Over Knowledge Graph." Data Science and Engineering 4, no. 4 (2019): 323–35. http://dx.doi.org/10.1007/s41019-019-00109-w.

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Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questi

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Cao, Keyan, and Chuang Zheng. "TBRm: A Time Representation Method for Industrial Knowledge Graph." Applied Sciences 12, no. 22 (2022): 11316. http://dx.doi.org/10.3390/app122211316.

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With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technolog

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Dehal, Ramandeep Singh, Mehak Sharma, and Enayat Rajabi. "Knowledge Graphs and Their Reciprocal Relationship with Large Language Models." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 38. https://doi.org/10.3390/make7020038.

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The reciprocal relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) highlights their synergistic potential in enhancing artificial intelligence (AI) applications. LLMs, with their natural language understanding and generative capabilities, support the automation of KG construction through entity recognition, relation extraction, and schema generation. Conversely, KGs serve as structured and interpretable data sources that improve the transparency, factual consistency and reliability of LLM-based applications, mitigating challenges such as hallucinations and lack of expl

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Tan, Fiona Anting, Debdeep Paul, Sahim Yamaura, Miura Koji, and See-Kiong Ng. "Constructing and Interpreting Causal Knowledge Graphs from News." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 52–59. http://dx.doi.org/10.1609/aaaiss.v1i1.27476.

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Many financial jobs rely on news to learn about causal events in the past and present, to make informed decisions and predictions about the future. With the ever-increasing amount of news available online, there is a need to automate the extraction of causal events from unstructured texts. In this work, we propose a methodology to construct causal knowledge graphs (KGs) from news using two steps: (1) Extraction of Causal Relations, and (2) Argument Clustering and Representation into KG. We aim to build graphs that emphasize on recall, precision and interpretability. For extraction, although ma

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Uyanhewage, Janadhi, Viraj Welgama, and Ruvan Weerasinghe. "Multi-Hop Question Answering over Knowledge Graphs." International Journal on Advances in ICT for Emerging Regions (ICTer) 17, no. 2 (2024): 76–84. http://dx.doi.org/10.4038/icter.v17i2.7281.

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Multi-Hop Question Answering over Knowledge Graphs (MHQA-KG) plays a pivotal role in various applications, including but not limited to Question Answering, Recommendation Systems, and Semantic Search. Nevertheless, current models for MHQA have limitations in their ability to grasp all the information included in the question, resulting a reduction in accuracy when producing answers. In order to mitigate this limitation, this paper proposes a novel Multi-Hop Question Answering over Knowledge Graphs approach. It mainly utilizes question and path embedding to answer multi-hop questions, significa

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Chen, Kai, Guohua Shen, Zhiqiu Huang, and Haijuan Wang. "Improved Entity Linking for Simple Question Answering Over Knowledge Graph." International Journal of Software Engineering and Knowledge Engineering 31, no. 01 (2021): 55–80. http://dx.doi.org/10.1142/s0218194021400039.

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Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocab

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Fernández-Álvarez, Daniel, Johannes Frey, Jose Emilio Labra Gayo, Daniel Gayo-Avello, and Sebastian Hellmann. "Approaches to measure class importance in Knowledge Graphs." PLOS ONE 16, no. 6 (2021): e0252862. http://dx.doi.org/10.1371/journal.pone.0252862.

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The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea w

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Kelley, Aidan, and Daniel Garijo. "A framework for creating knowledge graphs of scientific software metadata." Quantitative Science Studies 2, no. 4 (2021): 1423–46. http://dx.doi.org/10.1162/qss_a_00167.

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Abstract An increasing number of researchers rely on computational methods to generate or manipulate the results described in their scientific publications. Software created to this end—scientific software—is key to understanding, reproducing, and reusing existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, scientific software is usually challenging to find, set up, and compare to similar software due to its disconnected documentation (dispersed in manuals, readme files, websites, and code comments) and the lack of structured metadata to

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Singh, Priyank Kumar, Sami Ur Rehman, Darshan J, Shobha G, and Deepamala N. "Automated dynamic schema generation using knowledge graph." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1261. http://dx.doi.org/10.11591/ijai.v11.i4.pp1261-1269.

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<span>On the internet where the number of database developers is increasing with the availability of huge data to be stored and queried. Establishing relations between various schemas and helping the developers by filtering, prioritizing, and suggesting relevant schema is a requirement. Recommendation system plays an important role in searching through a large volume of dynamically generated schemas to provide database developers with personalized schemas and services. Although many methods are already available to solve problems using machine learning, they require more time and data to

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Priyank, Kumar Singh, Ur Rehman1 Sami, J. Darshan, G. Shobha, and N. Deepamala. "Automated dynamic schema generation using knowledge graph." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1261–69. https://doi.org/10.11591/ijai.v11.i4.pp1261-1269.

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On the internet where the number of database developers is increasing with the availability of huge data to be stored and queried. Establishing relations between various schemas and helping the developers by filtering, prioritizing, and suggesting relevant schema is a requirement. Recommendation system plays an important role in searching through a large volume of dynamically generated schemas to provide database developers with personalized schemas and services. Although many methods are already available to solve problems using machine learning, they require more time and data to learn. Thes

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Yu, Guangya, Qi Ye, and Tong Ruan. "Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label." Bioengineering 11, no. 3 (2024): 225. http://dx.doi.org/10.3390/bioengineering11030225.

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The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledg

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Hou, Kun, Jingyuan Li, Yingying Liu, Shiqi Sun, Haoliang Zhang, and Haiyang Jiang. "KG-EGV: A Framework for Question Answering with Integrated Knowledge Graphs and Large Language Models." Electronics 13, no. 23 (2024): 4835. https://doi.org/10.3390/electronics13234835.

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Despite the remarkable progress of large language models (LLMs) in understanding and generating unstructured text, their application in structured data domains and their multi-role capabilities remain underexplored. In particular, utilizing LLMs to perform complex reasoning tasks on knowledge graphs (KGs) is still an emerging area with limited research. To address this gap, we propose KG-EGV, a versatile framework leveraging LLMs to perform KG-based tasks. KG-EGV consists of four core steps: sentence segmentation, graph retrieval, EGV, and backward updating, each designed to segment sentences,

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Swapnil, S. Mahure. "Missing Link Prediction in Art Knowledge Graph using Representation Learning." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 5 (2024): 30–33. https://doi.org/10.35940/ijitee.J9264.13050424.

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<strong>Abstract:</strong> Knowledge graphs are an important evolving field in Artificial Intelligence domain which has multiple applications such as in question answering, important information retrieval, information recommendation, Natural language processing etc. Knowledge graph has one big limitation i.e. Incompleteness, it is due to because of real world data are dynamic and continues evolving. This incompleteness of Knowledge graph can be overcome or minimized by using representation learning models. There are several models which are classified on the base of translation distance, seman

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Lan, Ning, Shuqun Yang, Ling Yin, and Yongbin Gao. "Research on Knowledge Graphs with Concept Lattice Constraints." Symmetry 13, no. 12 (2021): 2363. http://dx.doi.org/10.3390/sym13122363.

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The application of knowledge graphs has been restricted in some domains, especially the industrial and academic domains. One of the reasons is that they require a high reliability of knowledge, which cannot be satisfied by the existing knowledge graph research. By comparison, traditional knowledge engineering has a high correctness, but low efficiency is an inevitable drawback. Therefore, it is meaningful to organically connect traditional knowledge engineering and knowledge graphs. Therefore, we propose a theory from Attribute Implications to Knowledge Graphs, named AIs-KG, which can construc

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Monka, Sebastian, Lavdim Halilaj, and Achim Rettinger. "A survey on visual transfer learning using knowledge graphs." Semantic Web 13, no. 3 (2022): 477–510. http://dx.doi.org/10.3233/sw-212959.

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The information perceived via visual observations of real-world phenomena is unstructured and complex. Computer vision (CV) is the field of research that attempts to make use of that information. Recent approaches of CV utilize deep learning (DL) methods as they perform quite well if training and testing domains follow the same underlying data distribution. However, it has been shown that minor variations in the images that occur when these methods are used in the real world can lead to unpredictable and catastrophic errors. Transfer learning is the area of machine learning that tries to preve

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Chen, Xuelu, Ziniu Hu, and Yizhou Sun. "Fuzzy Logic Based Logical Query Answering on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 3939–48. http://dx.doi.org/10.1609/aaai.v36i4.20310.

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Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and KG entities in the same space and conduct query answering via dense similarity search. However, most logical operators designed in previous studies do not satisfy the axiomatic system of classical logic, limiting their performance. Moreover, these logical operators are parameterized and thus require many complex FOL queries as training data, which are often arduous to collect or even inaccessible in most real-world

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Goel, Rishab, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. "Diachronic Embedding for Temporal Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3988–95. http://dx.doi.org/10.1609/aaai.v34i04.5815.

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Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones–a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embeddi

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Hao, Wu, Jiao Menglin, Tian Guohui, Ma Qing, and Liu Guoliang. "R-KG: A Novel Method for Implementing a Robot Intelligent Service." AI 1, no. 1 (2020): 117–40. http://dx.doi.org/10.3390/ai1010006.

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Aiming to solve the problem of environmental information being difficult to characterize when an intelligent service is used, knowledge graphs are used to express environmental information when performing intelligent services. Here, we specially design a kind of knowledge graph for environment expression referred to as a robot knowledge graph (R-KG). The main work of a R-KG is to integrate the diverse semantic information in the environment and pay attention to the relationship at the instance level. Also, through the efficient knowledge organization of a R-KG, robots can fully understand the

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Mirasdar, Sharayu, and Mangesh Bedekar. "Knowledge graphs for NLP: A comprehensive analysis." Scientific Temper 16, Spl-1 (2025): 141–48. https://doi.org/10.58414/scientifictemper.2025.16.spl-1.18.

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Comprehensive analysis done for this paper examines the blend of knowledge graphs (KGs) and natural language processing (NLP), emphasizing the collective potential of both techniques to improve understanding and processing of textual data amid its rapid growth. KGs provide structured semantic representations that facilitate deeper reasoning and contextual understanding, addressing the limitations inherent in traditional NLP approaches. By consolidating insights from over 79 research papers, the review in-depth explores the definitions, applications, and challenges related to the integration of

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Liu, Ruoqi, Lingfei Wu, and Ping Zhang. "KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8805–14. http://dx.doi.org/10.1609/aaai.v38i8.28727.

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Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE. Unlike previous approaches, KG-TREAT constructs dual-focus KGs and integrates a deep bi-level attention synergy method for in

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Wang, Meihong, Linling Qiu, and Xiaoli Wang. "A Survey on Knowledge Graph Embeddings for Link Prediction." Symmetry 13, no. 3 (2021): 485. http://dx.doi.org/10.3390/sym13030485.

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Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform

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Govindapillai, Sini, Soon Lay-Ki, and Haw Su-Cheng. "Domain-Independent True Fact Identification from Knowledge Graph." JOIV : International Journal on Informatics Visualization 9, no. 3 (2025): 893. https://doi.org/10.62527/joiv.9.3.3690.

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The trustworthiness of information in the Knowledge Graph (KG) is determined by the trustworthiness of information at the fact level. KGs are incomplete and noisy. Yet, most existing error detection approaches were applied to specific KGs. A large percentage of error detection approaches work well on DBpedia, particularly. However, we do not have a single KG containing all the information regarding the entity relations of a specific entity from any random class. The main objective of this research is to increase the trustworthiness of entity relations from KGs. In this paper, we propose a fram

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Yang, Zhenyu, Lei Wu, Peian Wen, and Peng Chen. "Visual Question Answering reasoning with external knowledge based on bimodal graph neural network." Electronic Research Archive 31, no. 4 (2023): 1948–65. http://dx.doi.org/10.3934/era.2023100.

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<abstract><p>Visual Question Answering (VQA) with external knowledge requires external knowledge and visual content to answer questions about images. The defect of existing VQA solutions is that they need to identify task-related information in the obtained pictures, questions, and knowledge graphs. It is necessary to properly fuse and embed the information between different modes identified, to reduce the noise and difficulty in cross-modality reasoning of VQA models. However, this process of rationally integrating information between different modes and joint reasoning to find re

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