scholarly journals Knowledge Graphs Representation for Event-Related E-News Articles

2021 ◽  
Vol 3 (4) ◽  
pp. 802-818
Author(s):  
M.V.P.T. Lakshika ◽  
H.A. Caldera
Keyword(s):  
Knowledge Representation ◽  
Contextual Information ◽  
Background Information ◽  
Knowledge Graph ◽  
Learning Approaches ◽  
Text Documents ◽  
Precise Knowledge ◽  
Knowledge Graphs ◽  
News Corpus ◽  
Constructing Knowledge

E-newspaper readers are overloaded with massive texts on e-news articles, and they usually mislead the reader who reads and understands information. Thus, there is an urgent need for a technology that can automatically represent the gist of these e-news articles more quickly. Currently, popular machine learning approaches have greatly improved presentation accuracy compared to traditional methods, but they cannot be accommodated with the contextual information to acquire higher-level abstraction. Recent research efforts in knowledge representation using graph approaches are neither user-driven nor flexible to deviations in the data. Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents. We propose an enhanced representation of a scalable knowledge graph by automatically extracting the information from the corpus of e-news articles and determine whether a knowledge graph can be used as an efficient application in analyzing and generating knowledge representation from the extracted e-news corpus. This knowledge graph consists of a knowledge base built using triples that automatically produce knowledge representation from e-news articles. Inclusively, it has been observed that the proposed knowledge graph generates a comprehensive and precise knowledge representation for the corpus of e-news articles.

scholarly journals Mapping of ImageNet and Wikidata for Knowledge Graphs Enabled Computer Vision

2021 ◽  
pp. 151-161
Author(s):  
Dominik Filipiak ◽  
Anna Fensel ◽  
Agata Filipowska
Keyword(s):  
Computer Vision ◽  
Performance Metrics ◽  
Contextual Information ◽  
Ground Truth ◽  
Knowledge Graph ◽  
Graph Structure ◽  
Exact Match ◽  
Ground Truth Data ◽  
Final Performance ◽  
Knowledge Graphs

Knowledge graphs are used as a source of prior knowledge in numerous computer vision tasks. However, such an approach requires to have a mapping between ground truth data labels and the target knowledge graph. We linked the ILSVRC 2012 dataset (often simply referred to as ImageNet) labels to Wikidata entities. This enables using rich knowledge graph structure and contextual information for several computer vision tasks, traditionally benchmarked with ImageNet and its variations. For instance, in few-shot learning classification scenarios with neural networks, this mapping can be leveraged for weight initialisation, which can improve the final performance metrics value. We mapped all 1000 ImageNet labels – 461 were already directly linked with the exact match property (P2888), 467 have exact match candidates, and 72 cannot be matched directly. For these 72 labels, we discuss different problem categories stemming from the inability of finding an exact match. Semantically close non-exact match candidates are presented as well. The mapping is publicly available athttps://github.com/DominikFilipiak/imagenet-to-wikidata-mapping.

scholarly journals Knowledge Graphs

2021 ◽  
Vol 5 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Knowledge Management ◽  
Large Scale ◽  
Personal Knowledge ◽  
Knowledge Graph ◽  
Specific Knowledge ◽  
Management Tools ◽  
Global Projects ◽  
Knowledge Graphs ◽  
Constructing Knowledge ◽  
Knowledge Management Tools

Knowledge graphs embody the idea of "everything connected to everything else." As attractive as this seems, there is a substantial gap between the dream of fully interconnected knowledge and the reality of data that is still mostly siloed, or weakly connected by shared strings such as taxonomic names. How do we move forward? Do we focus on building our own domain- or project-specific knowledge graphs, or do we engage with global projects such as Wikidata? Do we construct knowledge graphs, or focus on making our data "knowledge graph ready" by adopting structured markup in the hope that knowledge graphs will spontaneously self-assemble from that data? Do we focus on large-scale, database-driven projects (e.g., triple stores in the cloud), or do we rely on more localised and distributed approaches, such as annotations (e.g., hypothes.is), "content-hash" systems where a cryptographic hash of the data is also its identifier (Elliott et al. 2020), or the growing number of personal knowledge management tools (e.g., Roam, Obsidian, LogSeq)? This talk will share experiences (the good, bad, and the ugly) as I have tried to transition from naïve advocacy to constructing knowledge graphs (Page 2019), or participating in their construction (Page 2021).

scholarly journals ContextNet: representation and exploration for painting classification and retrieval in context

2019 ◽  
Vol 9 (1) ◽  
pp. 17-30
Author(s):  
Noa Garcia ◽  
Benjamin Renoust ◽  
Yuta Nakashima
Keyword(s):  
Contextual Information ◽  
Visual Representations ◽  
Fine Art ◽  
Multitask Learning ◽  
Knowledge Graph ◽  
Multiple Sources ◽  
Visual Elements ◽  
Knowledge Graphs ◽  
Art Analysis ◽  
Analysis Models

AbstractIn automatic art analysis, models that besides the visual elements of an artwork represent the relationships between the different artistic attributes could be very informative. Those kinds of relationships, however, usually appear in a very subtle way, being extremely difficult to detect with standard convolutional neural networks. In this work, we propose to capture contextual artistic information from fine-art paintings with a specific ContextNet network. As context can be obtained from multiple sources, we explore two modalities of ContextNets: one based on multitask learning and another one based on knowledge graphs. Once the contextual information is obtained, we use it to enhance visual representations computed with a neural network. In this way, we are able to (1) capture information about the content and the style with the visual representations and (2) encode relationships between different artistic attributes with the ContextNet. We evaluate our models on both painting classification and retrieval, and by visualising the resulting embeddings on a knowledge graph, we can confirm that our models represent specific stylistic aspects present in the data.

scholarly journals Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

2019 ◽  
Author(s):  
David N. Nicholson ◽  
Daniel S. Himmelstein ◽  
Casey S. Greene
Keyword(s):  
Contextual Information ◽  
Relation Extraction ◽  
Publication Rate ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Text Annotation ◽  
Label Function ◽  
Manual Curation ◽  
Function Combination ◽  
Knowledge Graphs

AbstractKnowledge graphs support multiple research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via some form of manual curation, which is difficult to scale in the context of an increasing publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to automatically annotate textual data. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This makes populating a knowledge graph with multiple nodes and edge types practically infeasible. We sought to accelerate the label function creation process by evaluating the extent to which label functions could be re-used across multiple edge types. We used a subset of an existing knowledge graph centered on disease, compound, and gene entities to evaluate label function re-use. We determined the best label function combination by comparing a baseline database-only model with the same model but added edge-specific or edge-mismatch label functions. We confirmed that adding additional edge-specific rather than edge-mismatch label functions often improves text annotation and shows that this approach can incorporate novel edges into our source knowledge graph. We expect that continued development of this strategy has the potential to swiftly populate knowledge graphs with new discoveries, ensuring that these resources include cutting-edge results.

scholarly journals A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution

2019 ◽  
Vol 9 (20) ◽  
pp. 4399
Author(s):  
Yanjun Wang ◽  
Yaqiong Qiao ◽  
Jiangtao Ma ◽  
Guangwu Hu ◽  
Chaoqin Zhang ◽  
...  
Keyword(s):  
Conflict Resolution ◽  
Classical Method ◽  
Error Rates ◽  
Knowledge Graph ◽  
Semantic Embedding ◽  
Conflict Problem ◽  
Noise Data ◽  
Soft Logic ◽  
Knowledge Graphs ◽  
Constructing Knowledge

Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge graphs, which is liable to cause high error rates in dynamic knowledge graph construction. In this article, we propose a knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which can resolve facts confliction with high precision by combing time attributes, semantic embedding representations, and graph structure features. Kgedl first trains the semantic embedding vector through the relationships between entities. Then, the path embedding vector is trained from the graph structures of knowledge graphs, and the time attributes of entities are combined with the semantic and path embedding vectors. Finally, Kgedl uses a recurrent neural network to make the inconsistent facts appear in the dynamic evolution of the knowledge graph consistent. A large number of experiments on real datasets show that Kgedl outperforms the state-of-the-art methods. Especially, Kgedl achieves 23% higher performance than the classical method numerical Probabilistic Soft Logic (nPSL).in the metric HITS@10. Also, extensive experiments verified that our proposal possess better robustness by adding noise data.

scholarly journals A survey on visual transfer learning using knowledge graphs

Semantic Web ◽  
2022 ◽  
pp. 1-34
Author(s):  
Sebastian Monka ◽  
Lavdim Halilaj ◽  
Achim Rettinger
Keyword(s):  
Transfer Learning ◽  
Real World ◽  
Image Data ◽  
Graph Embedding ◽  
Future Research ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Learning Approaches ◽  
Knowledge Graphs ◽  
Relational Graphs

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 prevent these errors. Especially, approaches that augment image data using auxiliary knowledge encoded in language embeddings or knowledge graphs (KGs) have achieved promising results in recent years. This survey focuses on visual transfer learning approaches using KGs, as we believe that KGs are well suited to store and represent any kind of auxiliary knowledge. KGs can represent auxiliary knowledge either in an underlying graph-structured schema or in a vector-based knowledge graph embedding. Intending to enable the reader to solve visual transfer learning problems with the help of specific KG-DL configurations we start with a description of relevant modeling structures of a KG of various expressions, such as directed labeled graphs, hypergraphs, and hyper-relational graphs. We explain the notion of feature extractor, while specifically referring to visual and semantic features. We provide a broad overview of knowledge graph embedding methods and describe several joint training objectives suitable to combine them with high dimensional visual embeddings. The main section introduces four different categories on how a KG can be combined with a DL pipeline: 1) Knowledge Graph as a Reviewer; 2) Knowledge Graph as a Trainee; 3) Knowledge Graph as a Trainer; and 4) Knowledge Graph as a Peer. To help researchers find meaningful evaluation benchmarks, we provide an overview of generic KGs and a set of image processing datasets and benchmarks that include various types of auxiliary knowledge. Last, we summarize related surveys and give an outlook about challenges and open issues for future research.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Constructing Knowledge Graphs for Online Collaborative Programming

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 117969-117980
Author(s):  
Yuanyi Zhen ◽  
Lanqin Zheng ◽  
Penghe Chen
Keyword(s):  
Collaborative Programming ◽  
Knowledge Graphs ◽  
Constructing Knowledge
Sign in / Sign up

Export Citation Format

Share Document