Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation

Autonomous object detection powered by cutting-edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine-grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine-grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories-subcategories and subcategories-attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high-level image features. Extensive experiments on the Caltech-UCSD Birds-200-2011 dataset verify the superiority of our proposed framework over several existing state-of-the-art methods.

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SNOMED-Based Knowledge Representation

Methods of Information in Medicine ◽

10.1055/s-0038-1634589 ◽

1995 ◽

Vol 34 (01/02) ◽

pp. 209-213 ◽

Cited By ~ 6

Author(s):

D. J. Rothwell

Keyword(s):

Veterinary Medicine ◽

Data Structure ◽

Knowledge Representation ◽

Patient Record ◽

Conceptual Scheme ◽

Fine Grained ◽

Clinical Events ◽

Share Data ◽

Computer Based

Abstract:A standardized vocabulary and a standardized representation for this vocabulary are necessary prerequisites for the development of a computer-based patient record. A standard conceptual scheme or data structure for this vocabulary must be in place to define clinical events and to share data. SNOMED International is a detailed, fine grained, semantically typed and comprehensive computer processable vocabulary encompassing both human and veterinary medicine. Each term is placed in a standardized data structure that shows the term relationship within its own and other related taxonomic hierarchies. SNOMED International is a standardized vocabulary and data structure suitable for use in the computer-based patient record.

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Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration

Journal of Cheminformatics ◽

10.1186/1758-2946-3-20 ◽

2011 ◽

Vol 3 (1) ◽

Cited By ~ 14

Author(s):

Leonid L Chepelev ◽

Michel Dumontier

Keyword(s):

Knowledge Representation ◽

Data Integration ◽

Chemical Entity

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Collectivist semantics

10.1093/oso/9780192844842.003.0018 ◽

2021 ◽

pp. 303-319

Author(s):

Dick Crouch ◽

Aikaterini-Lida Kalouli

Keyword(s):

Knowledge Representation ◽

Natural Language ◽

Semantic Representation ◽

Formal Semantics ◽

Fine Grained ◽

Computational Implementation ◽

Graphical Knowledge

The Graphical Knowledge Representation was introduced as a graph-based semantic representation for natural language. Although its computational implementation has already been presented, a formal account for the semantics behind the representation is still missing. This chapter seeks to fill this gap by proposing a formal semantics for the representation. The main proposal of the semantics is to take concepts, and not individuals, as primitive and thus achieve a fine-grained intensional semantics. This chapter explores how such a collectivist semantics can come about and how it can be employed within the Graphical Knowledge Representation.

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To Know and To Learn - About the Integration of Knowledge Representation and Deep Learning for Fine-Grained Visual Categorization

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications ◽

10.5220/0006651803870392 ◽

2018 ◽

Author(s):

Francesco Setti

Keyword(s):

Deep Learning ◽

Knowledge Representation ◽

Visual Categorization ◽

Fine Grained

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Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/87 ◽

2018 ◽

Cited By ~ 19

Author(s):

Tianshui Chen ◽

Liang Lin ◽

Riquan Chen ◽

Yang Wu ◽

Xiaonan Luo

Keyword(s):

Neural Network ◽

Knowledge Representation ◽

Image Recognition ◽

Feature Learning ◽

Representation Learning ◽

Feature Representation ◽

Image Feature ◽

Knowledge Graph ◽

Feature Maps ◽

Fine Grained

Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition (e.g., categorizing hundreds of subordinate categories of birds) usually requires a comprehensive visual concept organization including category labels and part-level attributes. In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition. Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation. By introducing a novel gated mechanism, our KERL framework incorporates this knowledge representation into the discriminative image feature learning, i.e., implicitly associating the specific attributes with the feature maps. Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus facilitating distinguishing the subtle differences among subordinate categories. ii) Our framework can learn feature maps with a meaningful configuration that the highlighted regions finely accord with the nodes (specific attributes) of the knowledge graph. Extensive experiments on the widely used Caltech-UCSD bird dataset demonstrate the superiority of our KERL framework over existing state-of-the-art methods.

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Application of an image management system in microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100129899 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1052-1053

Author(s):

Richard S. Chemock

Keyword(s):

Data Storage ◽

Cost Savings ◽

Image Data ◽

Analytical Techniques ◽

Operational Mode ◽

Fine Grained ◽

Image Recording ◽

Primary Output ◽

Capacity Data ◽

Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

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Use of fractals to describe microstructures of porous thick-film platinum electrodes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100121971 ◽

1992 ◽

Vol 50 (1) ◽

pp. 314-315

Author(s):

Steven D. Toteda

Keyword(s):

Fractal Dimension ◽

Power Plants ◽

Electrical Response ◽

Cubic Phase ◽

Platinum Electrodes ◽

Fine Grained ◽

Impedance Response ◽

Platinum Particles ◽

Energy Surfaces ◽

Highly Porous

Zirconia oxygen sensors, in such applications as power plants and automobiles, generally utilize platinum electrodes for the catalytic reaction of dissociating O2 at the surface. The microstructure of the platinum electrode defines the resulting electrical response. The electrode must be porous enough to allow the oxygen to reach the zirconia surface while still remaining electrically continuous. At low sintering temperatures, the platinum is highly porous and fine grained. The platinum particles sinter together as the firing temperatures are increased. As the sintering temperatures are raised even further, the surface of the platinum begins to facet with lower energy surfaces. These microstructural changes can be seen in Figures 1 and 2, but the goal of the work is to characterize the microstructure by its fractal dimension and then relate the fractal dimension to the electrical response. The sensors were fabricated from zirconia powder stabilized in the cubic phase with 8 mol% percent yttria. Each substrate was sintered for 14 hours at 1200°C. The resulting zirconia pellets, 13mm in diameter and 2mm in thickness, were roughly 97 to 98 percent of theoretical density. The Engelhard #6082 platinum paste was applied to the zirconia disks after they were mechanically polished ( diamond). The electrodes were then sintered at temperatures ranging from 600°C to 1000°C. Each sensor was tested to determine the impedance response from 1Hz to 5,000Hz. These frequencies correspond to the electrode at the test temperature of 600°C.

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Amorphous silica coating on α-alumina particles

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100137422 ◽

1995 ◽

Vol 53 ◽

pp. 210-211

Author(s):

J. W. Mellowes ◽

C. M. Chun ◽

I. A. Aksay

Keyword(s):

Amorphous Silica ◽

Heterogeneous Nucleation ◽

Homogeneous Nucleation ◽

Silica Coating ◽

Full Density ◽

Optimal Conditions ◽

Fine Grained ◽

Alumina Particles ◽

Complete Mullitization ◽

Powder Compacts

Mullite (3Al2O32SiO2) can be fabricated by transient viscous sintering using composite particles which consist of inner cores of a-alumina and outer coatings of amorphous silica. Powder compacts prepared with these particles are sintered to almost full density at relatively low temperatures (~1300°C) and converted to dense, fine-grained mullite at higher temperatures (>1500°C) by reaction between the alumina core and the silica coating. In order to achieve complete mullitization, optimal conditions for coating alumina particles with amorphous silica must be achieved. Formation of amorphous silica can occur in solution (homogeneous nucleation) or on the surface of alumina (heterogeneous nucleation) depending on the degree of supersaturation of the solvent in which the particles are immersed. Successful coating of silica on alumina occurs when heterogeneous nucleation is promoted and homogeneous nucleation is suppressed. Therefore, one key to successful coating is an understanding of the factors such as pH and concentration that control silica nucleation in aqueous solutions. In the current work, we use TEM to determine the optimal conditions of this processing.

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Interfacial structures of dissimilar materials joined by capacitor-discharge welding

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100170402 ◽

1994 ◽

Vol 52 ◽

pp. 534-535

Author(s):

C. P. Doğan ◽

R. D. Wilson ◽

J. A. Hawk

Keyword(s):

Rapid Solidification ◽

Fusion Zone ◽

Dissimilar Materials ◽

Solidification Process ◽

Weld Interface ◽

Capacitor Discharge ◽

Fine Grained ◽

Iron Aluminum ◽

Conductive Ceramics ◽

Capacitor Discharge Welding

Capacitor Discharge Welding is a rapid solidification technique for joining conductive materials that results in a narrow fusion zone and almost no heat affected zone. As a result, the microstructures and properties of the bulk materials are essentially continuous across the weld interface. During the joining process, one of the materials to be joined acts as the anode and the other acts as the cathode. The anode and cathode are brought together with a concomitant discharge of a capacitor bank, creating an arc which melts the materials at the joining surfaces and welds them together (Fig. 1). As the electrodes impact, the arc is extinguished, and the molten interface cools at rates that can exceed 106 K/s. This process results in reduced porosity in the fusion zone, a fine-grained weldment, and a reduced tendency for hot cracking.At the U.S. Bureau of Mines, we are currently examining the possibilities of using capacitor discharge welding to join dissimilar metals, metals to intermetallics, and metals to conductive ceramics. In this particular study, we will examine the microstructural characteristics of iron-aluminum welds in detail, focussing our attention primarily on interfaces produced during the rapid solidification process.

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