Journal articles: 'Knowledge Graphs'

1

Gutierrez, Claudio, and Juan F. Sequeda. "Knowledge graphs." Communications of the ACM 64, no. 3 (March 2021): 96–104. http://dx.doi.org/10.1145/3418294.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, et al. "Knowledge Graphs." Synthesis Lectures on Data, Semantics, and Knowledge 12, no. 2 (November 8, 2021): 1–257. http://dx.doi.org/10.2200/s01125ed1v01y202109dsk022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia D’amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, et al. "Knowledge Graphs." ACM Computing Surveys 54, no. 4 (July 2021): 1–37. http://dx.doi.org/10.1145/3447772.

Full text

Abstract:

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

APA, Harvard, Vancouver, ISO, and other styles

4

Weikum, Gerhard. "Knowledge graphs 2021." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3233–38. http://dx.doi.org/10.14778/3476311.3476393.

Full text

Abstract:

Providing machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing vision and challenge for AI. Over the last 15 years, huge knowledge bases, also known as knowledge graphs, have been automatically constructed from web data, and have become a key asset for search engines and other use cases. Machine knowledge can be harnessed to semantically interpret texts in news, social media and web tables, contributing to question answering, natural language processing and data analytics. This position paper reviews these advances and discusses lessons learned. It highlights the role of "DB thinking" in building and maintaining high-quality knowledge bases from web contents. Moreover, the paper identifies open challenges and new research opportunities. In particular, extracting quantitative measures of entities (e.g., height of buildings or energy efficiency of cars), from text and web tables, presents an opportunity to further enhance the scope and value of knowledge bases.

APA, Harvard, Vancouver, ISO, and other styles

5

Khan, Arijit. "Knowledge Graphs Querying." ACM SIGMOD Record 52, no. 2 (August 10, 2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.

Full text

Abstract:

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 recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.

APA, Harvard, Vancouver, ISO, and other styles

6

Liu, Wenqiang, Jun Liu, Mengmeng Wu, Samar Abbas, Wei Hu, Bifan Wei, and Qinghua Zheng. "Representation learning over multiple knowledge graphs for knowledge graphs alignment." Neurocomputing 320 (December 2018): 12–24. http://dx.doi.org/10.1016/j.neucom.2018.08.070.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Telnov, V. P., and Yu A. Korovin. "Programming of Knowledge Graphs, Reasoning on Graphs." PROGRAMMNAYA INGENERIA 10, no. 2 (February 26, 2019): 59–68. http://dx.doi.org/10.17587/prin.10.59-68.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Shah, Rita. "Reimagine Pharma Regulatory Operations using Knowledge Graphs." International Journal of Science and Research (IJSR) 13, no. 5 (May 5, 2024): 1372–73. http://dx.doi.org/10.21275/sr24517123345.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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 providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different confidence score modeling strategies. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results, and it consistently outperforms baselines on these tasks.

APA, Harvard, Vancouver, ISO, and other styles

10

Noy, Natasha, Yuqing Gao, Anshu Jain, Anant Narayanan, Alan Patterson, and Jamie Taylor. "Industry-scale knowledge graphs." Communications of the ACM 62, no. 8 (July 24, 2019): 36–43. http://dx.doi.org/10.1145/3331166.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Portmann, Edy, Patrick Kaltenrieder, and Witold Pedrycz. "Knowledge Representation through Graphs." Procedia Computer Science 62 (2015): 245–48. http://dx.doi.org/10.1016/j.procs.2015.08.446.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

MURALI MOHANA KRISHNA DANDU, Vishwasrao Salunkhe, Shashwat Agrawal, Prof.(Dr) Punit Goel, and Vikhyat Gupta. "Knowledge Graphs for Personalized Recommendations." Innovative Research Thoughts 9, no. 1 (March 30, 2023): 450–79. http://dx.doi.org/10.36676/irt.v9.i1.1497.

Full text

Abstract:

Knowledge graphs have emerged as a transformative tool in enhancing personalized recommendation systems. By integrating diverse datasets into a structured semantic network, knowledge graphs offer a holistic view of relationships and entities that can significantly improve the relevance and accuracy of recommendations. Unlike traditional recommendation algorithms that rely primarily on user behaviour and item similarity, knowledge graphs leverage contextual information and complex interconnections among entities to deliver more nuanced and context-aware suggestions. This abstract explores the pivotal role of knowledge graphs in advancing personalized recommendation systems, focusing on their ability to capture intricate relationships between users, items, and attributes. By mapping out these relationships, knowledge graphs facilitate a deeper understanding of user preferences and item characteristics, enabling the generation of more tailored and precise recommendations. Additionally, the incorporation of external knowledge sources into the graph can further enrich the recommendation process, leading to enhanced user satisfaction and engagement. The paper reviews various methodologies for integrating knowledge graphs into recommendation systems, including graph-based algorithms and machine learning techniques. It also examines real-world applications and case studies where knowledge graphs have demonstrated substantial improvements in recommendation quality. Ultimately, the utilization of knowledge graphs represents a significant leap forward in personalizing user experiences, offering a promising avenue for future research and development in the field of recommendation systems.

APA, Harvard, Vancouver, ISO, and other styles

13

Callahan, Tiffany J., Ignacio J. Tripodi, Harrison Pielke-Lombardo, and Lawrence E. Hunter. "Knowledge-Based Biomedical Data Science." Annual Review of Biomedical Data Science 3, no. 1 (July 20, 2020): 23–41. http://dx.doi.org/10.1146/annurev-biodatasci-010820-091627.

Full text

Abstract:

Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.

APA, Harvard, Vancouver, ISO, and other styles

14

Zhang, Xiang, Qingqing Yang, Jinru Ding, and Ziyue Wang. "Entity Profiling in Knowledge Graphs." IEEE Access 8 (2020): 27257–66. http://dx.doi.org/10.1109/access.2020.2971567.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Zheng, Weiguo, Jeffrey Xu Yu, Lei Zou, and Hong Cheng. "Question answering over knowledge graphs." Proceedings of the VLDB Endowment 11, no. 11 (July 2018): 1373–86. http://dx.doi.org/10.14778/3236187.3236192.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Popping, Roel. "Text Analysis for Knowledge Graphs." Quality & Quantity 41, no. 5 (August 25, 2006): 691–709. http://dx.doi.org/10.1007/s11135-006-9020-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Shi, Wei, Weiguo Zheng, Jeffrey Xu Yu, Hong Cheng, and Lei Zou. "Keyphrase Extraction Using Knowledge Graphs." Data Science and Engineering 2, no. 4 (November 16, 2017): 275–88. http://dx.doi.org/10.1007/s41019-017-0055-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Yan, Jihong, Chengyu Wang, Wenliang Cheng, Ming Gao, and Aoying Zhou. "A retrospective of knowledge graphs." Frontiers of Computer Science 12, no. 1 (September 26, 2016): 55–74. http://dx.doi.org/10.1007/s11704-016-5228-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Chen, Zhengxin. "Knowledge graphs for information systems." Computers & Education 18, no. 4 (May 1992): 267–72. http://dx.doi.org/10.1016/0360-1315(92)90098-p.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Cheng, Gong. "Relationship search over knowledge graphs." ACM SIGWEB Newsletter, Summer 2020 (July 27, 2020): 1–8. http://dx.doi.org/10.1145/3409481.3409484.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

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

Full text

Abstract:

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 information into the underlying model that utilizes triple structural information. In this paper, we propose an embedding model for uncertain temporal KGs called the confidence score, time, and ranking information embedded jointly model (CTRIEJ), which aims to preserve the uncertainty, temporal and structural information of relation facts in the embedding space. To further enhance the precision of the CTRIEJ model, we also introduce a self-adversarial negative sampling technique to generate negative samples. We use the embedding vectors obtained from our model to complete the missing relation facts and predict their corresponding confidence scores. Experiments are conducted on an uncertain temporal KG extracted from Wikidata via three tasks, i.e., confidence prediction, link prediction, and relation fact classification. The CTRIEJ model shows effectiveness in capturing uncertain and temporal knowledge by achieving promising results, and it consistently outperforms baselines on the three downstream experimental tasks.

APA, Harvard, Vancouver, ISO, and other styles

22

Noss, Richard, Arthur Bakker, Celia Hoyles, and Phillip Kent. "Situating graphs as workplace knowledge." Educational Studies in Mathematics 65, no. 3 (January 23, 2007): 367–84. http://dx.doi.org/10.1007/s10649-006-9058-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Dutkiewicz, Jakub, and Czesław Jędrzejek. "Knowledge Graphs in Information Retrieval." European Conference on Knowledge Management 25, no. 1 (September 3, 2024): 208–17. http://dx.doi.org/10.34190/eckm.25.1.2876.

Full text

Abstract:

This paper introduces an information retrieval model that leverages knowledge graphs, specifically tailored for Clinical Trials. In these scenarios, the document in question takes the form of a semi-structured clinical trial, containing details about enrolled patients, descriptions of experiments and procedures conducted during the trial, relevant diseases, and specific enrollment criteria. While the document retains a semi-structured format, the majority of the information is expressed in natural language. Queries in this context consist of specific patient characteristics, such as disease type, genetic information, and demographic data. The primary aim of this paper is to develop and utilize a knowledge graph capable of storing this information, including links to external resources like the Disease Ontology. We propose an Object-Relational model, which is then transformed into a knowledge graph. This graph is subsequently employed to identify semantic connections between concepts present in the clinical trials and those in the queries. These connections are then utilized to formulate a retrieval model for each aspect of the query. To achieve this, we design a relevance formula that incorporates weights to account for ontological relationships between concepts. We evaluate the effectiveness of our model by comparing the results with manual annotations.

APA, Harvard, Vancouver, ISO, and other styles

24

Krause, Sebastian, Leonhard Hennig, Andrea Moro, Dirk Weissenborn, Feiyu Xu, Hans Uszkoreit, and Roberto Navigli. "Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs." Journal of Web Semantics 37-38 (March 2016): 112–31. http://dx.doi.org/10.1016/j.websem.2016.03.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Baclawski, Ken, Michael Bennett, Gary Berg-Cross, Todd Schneider, Ravi Sharma, Janet Singer, and Ram D. Sriram. "Ontology summit 2020 communiqué: Knowledge graphs." Applied Ontology 16, no. 2 (April 27, 2021): 229–47. http://dx.doi.org/10.3233/ao-210249.

Full text

Abstract:

An increasing amount of data is now available from public and private sources. Furthermore, the types, formats, and number of sources of data are also increasing. Techniques for extracting, storing, processing, and analyzing such data have been developed in the last few years for managing this bewildering variety based on a structure called a knowledge graph. Industry has devoted a great deal of effort to the development of knowledge graphs, and knowledge graphs are now critical to the functions of intelligent virtual assistants such as Siri, Alexa, and Google Assistant. The goal of the Ontology Summit 2020 was to understand not only what knowledge graphs are but also where they originated, why they are so popular, the current issues, and their future prospects. The summit sessions examined many examples of knowledge graphs and surveyed the relevant standards that exist and are in development for knowledge graphs. The purpose of this Communiqué is to summarize our understanding from the Summit in order to foster research and development of knowledge graphs.

APA, Harvard, Vancouver, ISO, and other styles

26

Trisedya, Bayu Distiawan, Jianzhong Qi, and Rui Zhang. "Entity Alignment between Knowledge Graphs Using Attribute Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 297–304. http://dx.doi.org/10.1609/aaai.v33i01.3301297.

Full text

Abstract:

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.

APA, Harvard, Vancouver, ISO, and other styles

27

Ma, Yunpu, and Volker Tresp. "Quantum Machine Learning Algorithm for Knowledge Graphs." ACM Transactions on Quantum Computing 2, no. 3 (September 30, 2021): 1–28. http://dx.doi.org/10.1145/3467982.

Full text

Abstract:

Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs. Since most tensor problems are NP-hard [18], it is challenging to devise quantum algorithms to support the inference task. We simplify the modeling task by making the plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves speedup with a polylogarithmic runtime in the dimension of knowledge graph tensor.

APA, Harvard, Vancouver, ISO, and other styles

28

Huang, Jinchao, Zhipu Xie, Han Zhang, Bin Yang, Chong Di, and Runhe Huang. "Enhancing Knowledge-Aware Recommendation with Dual-Graph Contrastive Learning." Information 15, no. 9 (September 2, 2024): 534. http://dx.doi.org/10.3390/info15090534.

Full text

Abstract:

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graphs can introduce user–item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user–item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: (1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and (2) how to merge interaction information from the two graphs while ensuring that user–item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC strengthens and represents the information of different connecting edges in both graphs, and extracts the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results.

APA, Harvard, Vancouver, ISO, and other styles

29

Liu, Xiangwen, Shengyu Mao, Xiaohan Wang, and Jiajun Bu. "Generative Transformer with Knowledge-Guided Decoding for Academic Knowledge Graph Completion." Mathematics 11, no. 5 (February 21, 2023): 1073. http://dx.doi.org/10.3390/math11051073.

Full text

Abstract:

Academic knowledge graphs are essential resources and can be beneficial in widespread real-world applications. Most of the existing academic knowledge graphs are far from completion; thus, knowledge graph completion—the task of extending a knowledge graph with missing entities and relations—attracts many researchers. Most existing methods utilize low-dimensional embeddings to represent entities and relations and follow the discrimination paradigm for link prediction. However, discrimination approaches may suffer from the scaling issue during inference with large-scale academic knowledge graphs. In this paper, we propose a novel approach of a generative transformer with knowledge-guided decoding for academic knowledge graph completion. Specifically, we introduce generative academic knowledge graph pre-training with a transformer. Then, we propose knowledge-guided decoding, which leverages relevant knowledge in the training corpus as guidance for help. We conducted experiments on benchmark datasets for knowledge graph completion. The experimental results show that the proposed approach can achieve performance gains of 30 units of the MRR score over the baselines on the academic knowledge graph AIDA.

APA, Harvard, Vancouver, ISO, and other styles

30

Zhou, Ziwei. "Analysis of recommendation systems based on knowledge graphs." Applied and Computational Engineering 69, no. 1 (July 31, 2024): 200–206. http://dx.doi.org/10.54254/2755-2721/69/20241529.

Full text

Abstract:

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 decomposition and translation, which primarily designed for static knowledge graphs. This study thoroughly explains the advantages and disadvantages of these methods in practical application. Moreover, this paper discusses the challenges faced by dynamic knowledge graphs, such as temporal data processing, real-time updates, and inference, proposing potential solutions and future research areas. Integrating knowledge graphs with machine learning techniques enhances the ability of social media recommendation systems to understand user preferences and deliver highly personalized recommendations effectively. This study provides theoretical support and practical guidance for the implementing of knowledge graphs in recommendation systems, holding significant academic value and practical significance.

APA, Harvard, Vancouver, ISO, and other styles

31

Reshma G. Nehe. "A Statistical Analysis of Knowledge Graph and its Applications." Panamerican Mathematical Journal 34, no. 3 (October 1, 2024): 145–59. http://dx.doi.org/10.52783/pmj.v34.i3.1781.

Full text

Abstract:

In today's fast-paced world driven by big data and artificial intelligence, organizing and representing vast amounts of knowledge is crucial. Knowledge graphs are dynamic structures serve as repositories for real-world knowledge, presented in the form of interconnected graph data. With their ability to encapsulate complex information, knowledge graphs have swiftly captured the attention of both academia and industry alike. In this paper, we dive deep into understanding knowledge graphs. In first Section, we start by looking at their origins and evolution. In second section describes knowledge graphs, compares them with other graph forms, and explores how academic societies have incorporated them. In conclusion, we discuss the various applications of knowledge graphs in various domains and highlight the software tools that are facilitating their advancement. By providing a systematic overview of knowledge graphs, we aim to illuminate new avenues for research and foster advancements in this burgeoning field.

APA, Harvard, Vancouver, ISO, and other styles

32

Sheth, Amit, Swati Padhee, Amelie Gyrard, and Amit Sheth. "Knowledge Graphs and Knowledge Networks: The Story in Brief." IEEE Internet Computing 23, no. 4 (July 1, 2019): 67–75. http://dx.doi.org/10.1109/mic.2019.2928449.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Popping, Roel, and Inge Strijker. "Representation and integration of sociological knowledge using knowledge graphs." Social Science Information 36, no. 4 (December 1997): 731–47. http://dx.doi.org/10.1177/053901897036004006.

Full text

Abstract:

The representation and integration of sociological knowledge using knowledge graphs, a specific kind of semantic network, is discussed. Knowledge is systematically searched; this reveals inconsistencies, reducing superfluous research and knowledge, and showing gaps in a theory. This representation is conceivable under certain conditions, which are discussed. A graph for sociological theories about labour markets is presented.

APA, Harvard, Vancouver, ISO, and other styles

34

Hyvönen, Eero, and Heikki Rantala. "Knowledge-based Relation Discovery in Cultural Heritage Knowledge Graphs." Digital Humanities in the Nordic and Baltic Countries Publications 2, no. 1 (May 17, 2019): 230–39. http://dx.doi.org/10.5617/dhnbpub.11098.

Full text

Abstract:

This paper presents a new knowledge-based approach for finding serendipitous semantic relations between resources in a knowledge graph. The idea is to characterize the notion of “interesting connection” in terms of generic ontological explanation patterns that are applied to an underlying linked data repository to instantiate connections. In this way, 1) semantically uninteresting connections can be ruled out effectively, and 2) natural language explanations about the connections can be created for the end-user. The idea has been implemented and tested based on a knowledge graph of biographical data extracted from the biographies of 13 000 prominent historical persons in Finland, enriched by data linking to collection databases of museums, libraries, and archives. The demonstrator is in use as part of the BiographySampo portal of interlinked biographies.

APA, Harvard, Vancouver, ISO, and other styles

35

Shi, Longxiang, Shijian Li, Xiaoran Yang, Jiaheng Qi, Gang Pan, and Binbin Zhou. "Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services." BioMed Research International 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/2858423.

Full text

Abstract:

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.

APA, Harvard, Vancouver, ISO, and other styles

36

Rajabi, Enayat, and Somayeh Kafaie. "Knowledge Graphs and Explainable AI in Healthcare." Information 13, no. 10 (September 28, 2022): 459. http://dx.doi.org/10.3390/info13100459.

Full text

Abstract:

Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare experts and AI-based models. We also discuss how to leverage knowledge graphs in pre-model, in-model, and post-model XAI models in healthcare to make them more explainable.

APA, Harvard, Vancouver, ISO, and other styles

37

Li, Jingyu, Mingxu Han, Guangyu Dong, Huimei Wei, and Yao Li. "Intelligent Medical Knowledge Robot Based on Knowledge Graph." Journal of Physics: Conference Series 2501, no. 1 (May 1, 2023): 012032. http://dx.doi.org/10.1088/1742-6596/2501/1/012032.

Full text

Abstract:

Abstract Artificial intelligence has been gradually integrated into life, and knowledge graph is also particularly important, and its application is gradually maturing. In recent years researchers have used the rich entity and relationship information in the knowledge graph to overcome many difficulties. Through the fusion, reasoning, and deep learning of large-scale knowledge graphs, knowledge graphs link and apply these memories to generate wisdom. It can be said that knowledge graph has become the infrastructure of the era of artificial intelligence. The article combines Neo4j with ECharts visualization to develop a visual question answering robot in the medical field. This uses a more scientific method to understand and present data, provides a humanized visual question answering robot. And finally shows the effect of visual knowledge graph as well as assists users in the analysis of specific diseases.

APA, Harvard, Vancouver, ISO, and other styles

38

Duan, Yucong, Lixu Shao, and Gongzhu Hu. "Specifying Knowledge Graph with Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph." International Journal of Software Innovation 6, no. 2 (April 2018): 10–25. http://dx.doi.org/10.4018/ijsi.2018040102.

Full text

Abstract:

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graphs have abundant natural semantics and can contain various and more complete information. It is an expression mechanism close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. The authors propose to clarify the expression of knowledge graph as a whole. They clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. The authors also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

APA, Harvard, Vancouver, ISO, and other styles

39

Tsamoura, Efthymia, David Carral, Enrico Malizia, and Jacopo Urbani. "Materializing knowledge bases via trigger graphs." Proceedings of the VLDB Endowment 14, no. 6 (February 2021): 943–56. http://dx.doi.org/10.14778/3447689.3447699.

Full text

Abstract:

Thechaseis a well-established family of algorithms used to materialize Knowledge Bases (KBs) for tasks like query answering under dependencies or data cleaning. A general problem of chase algorithms is that they might perform redundant computations. To counter this problem, we introduce the notion ofTrigger Graphs(TGs), which guide the execution of the rules avoiding redundant computations. We present the results of an extensive theoretical and empirical study that seeks to answer when and how TGs can be computed and what are the benefits of TGs when applied over real-world KBs. Our results include introducing algorithms that compute (minimal) TGs. We implemented our approach in a new engine, called GLog, and our experiments show that it can be significantly more efficient than the chase enabling us to materialize Knowledge Graphs with 17B facts in less than 40 min using a single machine with commodity hardware.

APA, Harvard, Vancouver, ISO, and other styles

40

Fionda, Valeria, and Giuseppe Pirrò. "Learning Triple Embeddings from Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3874–81. http://dx.doi.org/10.1609/aaai.v34i04.5800.

Full text

Abstract:

Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem of directly learning triple embeddings. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the endpoint nodes. The goal of this paper is to introduce Triple2Vec, a new technique to directly embed knowledge graph triples. We leverage the idea of line graph of a graph and extend it to the context of knowledge graphs. We introduce an edge weighting mechanism for the line graph based on semantic proximity. Embeddings are finally generated by adopting the SkipGram model, where sentences are replaced with graph walks. We evaluate our approach on different real-world knowledge graphs and compared it with related work. We also show an application of triple embeddings in the context of user-item recommendations.

APA, Harvard, Vancouver, ISO, and other styles

41

Reinanda, Ridho, Edgar Meij, and Maarten de Rijke. "Knowledge Graphs: An Information Retrieval Perspective." Foundations and Trends® in Information Retrieval 14, no. 4 (2020): 289–444. http://dx.doi.org/10.1561/1500000063.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Chaudhri, Vinay K., Chaitanya Baru, Naren Chittar, Xin Luna Dong, Michael Genesereth, James Hendler, Aditya Kalyanpur, et al. "Knowledge graphs: Introduction, history, and perspectives." AI Magazine 43, no. 1 (March 2022): 17–29. http://dx.doi.org/10.1002/aaai.12033.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Abu-Salih, Bilal. "Domain-specific knowledge graphs: A survey." Journal of Network and Computer Applications 185 (July 2021): 103076. http://dx.doi.org/10.1016/j.jnca.2021.103076.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Sequeda, Juan, and Ora Lassila. "Designing and Building Enterprise Knowledge Graphs." Synthesis Lectures on Data, Semantics, and Knowledge 11, no. 1 (August 3, 2021): 1–165. http://dx.doi.org/10.2200/s01105ed1v01y202105dsk020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Chen, Ya, Samuel Mensah, Fei Ma, Hao Wang, and Zhongan Jiang. "Collaborative filtering grounded on knowledge graphs." Pattern Recognition Letters 151 (November 2021): 55–61. http://dx.doi.org/10.1016/j.patrec.2021.07.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Chaudhri, Vinay, Chaitanya Baru, Naren Chittar, Xin Dong, Michael Genesereth, James Hendler, Aditya Kalyanpur, et al. "Knowledge Graphs: Introduction, History and, Perspectives." AI Magazine 43, no. 1 (March 31, 2022): 17–29. http://dx.doi.org/10.1609/aimag.v43i1.19119.

Full text

Abstract:

Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.

APA, Harvard, Vancouver, ISO, and other styles

47

Pirrò, Giuseppe. "Building relatedness explanations from knowledge graphs." Semantic Web 10, no. 6 (October 28, 2019): 963–90. http://dx.doi.org/10.3233/sw-190348.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Kejriwal, Mayank, Juan Sequeda, and Vanessa Lopez. "Knowledge graphs: Construction, management and querying." Semantic Web 10, no. 6 (October 28, 2019): 961–62. http://dx.doi.org/10.3233/sw-190370.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Kastha, Pramita. "Fake News Detection using Knowledge Graphs." International Journal for Research in Applied Science and Engineering Technology 8, no. 12 (December 31, 2020): 514–15. http://dx.doi.org/10.22214/ijraset.2020.32545.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Ayala-Gómez, Frederick, Bálint Daróczy, András Benczúr, Michael Mathioudakis, and Aristides Gionis. "Global citation recommendation using knowledge graphs." Journal of Intelligent & Fuzzy Systems 34, no. 5 (May 24, 2018): 3089–100. http://dx.doi.org/10.3233/jifs-169493.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Knowledge Graphs'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles