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berkeleybop · Mar 28, 2024 · 000649b · 000649b
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diff --git a/aio-full.json b/aio-full.json
@@ -15787,8 +15787,8 @@
         } ]
       }
     }, {
-      "id" : "https://w3id.org/aio/DeepFeedFoward",
-      "lbl" : "Deep FeedFoward",
+      "id" : "https://w3id.org/aio/DeepFeedForward",
+      "lbl" : "Deep FeedForward",
       "type" : "CLASS",
       "meta" : {
         "definition" : {
@@ -21286,7 +21286,7 @@
       "pred" : "is_a",
       "obj" : "https://w3id.org/aio/DeepNeuralNetwork"
     }, {
-      "sub" : "https://w3id.org/aio/DeepFeedFoward",
+      "sub" : "https://w3id.org/aio/DeepFeedForward",
       "pred" : "is_a",
       "obj" : "https://w3id.org/aio/DeepNeuralNetwork"
     }, {

diff --git a/aio-full.obo b/aio-full.obo
@@ -2669,8 +2669,8 @@ synonym: "DCN" EXACT []
 is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network
 
 [Term]
-id: https://w3id.org/aio/DeepFeedFoward
-name: Deep FeedFoward
+id: https://w3id.org/aio/DeepFeedForward
+name: Deep FeedForward
 def: "The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network." [https://en.wikipedia.org/wiki/Feedforward_neural_network] {type="owl:Axiom"}
 comment: Input, Hidden, Output
 synonym: "DFF" EXACT []
@@ -3990,7 +3990,7 @@ name: Neural Turing Machine Network
 def: "A Neural Turing machine (NTMs) is a recurrent neural network model. The approach was published by Alex Graves et al. in 2014. NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers. An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent. An NTM with a long short-term memory (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from examples alone." [https://en.wikipedia.org/wiki/Neural_Turing_machine] {type="owl:Axiom"}
 comment: Input, Hidden, Spiking Hidden, Output
 synonym: "NTM" EXACT []
-is_a: https://w3id.org/aio/DeepFeedForward
+is_a: https://w3id.org/aio/DeepFeedForward ! Deep FeedForward
 is_a: https://w3id.org/aio/LongShortTermMemory ! Long Short Term Memory
 
 [Term]
@@ -4161,7 +4161,7 @@ comment: Input, Hidden, Output
 synonym: "Radial Basis Function Network" EXACT []
 synonym: "RBFN" EXACT []
 synonym: "RBN" EXACT []
-is_a: https://w3id.org/aio/DeepFeedFoward ! Deep FeedFoward
+is_a: https://w3id.org/aio/DeepFeedFoward
 
 [Term]
 id: https://w3id.org/aio/RandomBrightnessLayer

diff --git a/aio-full.owl b/aio-full.owl
@@ -7916,13 +7916,7 @@ class labels for these objects. The resulting predictor can be used to attach cl
 
     <!-- https://w3id.org/aio/DeepFeedForward -->
 
-    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedForward"/>
-
-
-
-    <!-- https://w3id.org/aio/DeepFeedFoward -->
-
-    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedFoward">
+    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedForward">
         <rdfs:subClassOf rdf:resource="https://w3id.org/aio/DeepNeuralNetwork"/>
         <obo:IAO_0000115>The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.</obo:IAO_0000115>
         <oboInOwl:hasExactSynonym>DFF</oboInOwl:hasExactSynonym>
@@ -7931,17 +7925,23 @@ class labels for these objects. The resulting predictor can be used to attach cl
         <oboInOwl:hasExactSynonym>MLP</oboInOwl:hasExactSynonym>
         <oboInOwl:hasExactSynonym>Multilayer Perceptoron</oboInOwl:hasExactSynonym>
         <rdfs:comment>Input, Hidden, Output</rdfs:comment>
-        <rdfs:label>Deep FeedFoward</rdfs:label>
+        <rdfs:label>Deep FeedForward</rdfs:label>
     </owl:Class>
     <owl:Axiom>
-        <owl:annotatedSource rdf:resource="https://w3id.org/aio/DeepFeedFoward"/>
+        <owl:annotatedSource rdf:resource="https://w3id.org/aio/DeepFeedForward"/>
         <owl:annotatedProperty rdf:resource="http://purl.obolibrary.org/obo/IAO_0000115"/>
         <owl:annotatedTarget>The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.</owl:annotatedTarget>
         <oboInOwl:hasDbXref>https://en.wikipedia.org/wiki/Feedforward_neural_network</oboInOwl:hasDbXref>
     </owl:Axiom>
 
 
 
+    <!-- https://w3id.org/aio/DeepFeedFoward -->
+
+    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedFoward"/>
+
+
+
     <!-- https://w3id.org/aio/DeepNeuralNetwork -->
 
     <owl:Class rdf:about="https://w3id.org/aio/DeepNeuralNetwork">

diff --git a/aio-src.csv b/aio-src.csv
@@ -11,7 +11,7 @@ word2vec-CBOW,AIO:word2vec-CBOW,Network,W2V-CBOW,CBOW,"In the continuous bag-of-
 word2vec-SkipGram,AIO:word2vec-SkipGram,Network,W2V-SkipGram,SkipGram,"In the continuous skip-gram architecture, the model uses the current word to predict the surrounding window of context words. The skip-gram architecture weighs nearby context words more heavily than more distant context words.",https://en.wikipedia.org/wiki/Word2vec,"Input, Hidden, Output",AIO:ArtificialNeuralNetwork
 Restricted Boltzmann Machine,AIO:RestrictedBoltzmannMachine,Network,RBM,,A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.,https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine,"Backfed Input, Probabilistic Hidden",AIO:BoltzmannMachineNetwork
 Deep Convolutional Network,AIO:DeepConvolutionalNetwork,Network,DCN|Convolutional Neural Network|CNN|ConvNet,,"A convolutional neural network (CNN, or ConvNet) is a class of artificial neural network, most commonly applied to analyze visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. CNNs are regularized versions of multilayer perceptrons. (https://en.wikipedia.org/wiki/Convolutional_neural_network)",https://en.wikipedia.org/wiki/Convolutional_neural_network,"Input, Kernel, Convolutional/Pool, Hidden, Output",AIO:DeepNeuralNetwork
-Deep FeedFoward,AIO:DeepFeedFoward,Network,DFF|Feedforward Network|FFN|Multilayer Perceptoron|MLP,,"The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.",https://en.wikipedia.org/wiki/Feedforward_neural_network,"Input, Hidden, Output",AIO:DeepNeuralNetwork
+Deep FeedForward,AIO:DeepFeedForward,Network,DFF|Feedforward Network|FFN|Multilayer Perceptoron|MLP,,"The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.",https://en.wikipedia.org/wiki/Feedforward_neural_network,"Input, Hidden, Output",AIO:DeepNeuralNetwork
 Deconvolutional Network,AIO:DeconvolutionalNetwork,Network,DN,,"Deconvolutional Networks, a framework that permits the unsupervised construction of hierarchical image representations. These representations can be used for both low-level tasks such as denoising, as well as providing features for object recognition. Each level of the hierarchy groups information from the level beneath to form more complex features that exist over a larger scale in the image. (https://ieeexplore.ieee.org/document/5539957)",https://ieeexplore.ieee.org/document/5539957,"Input, Kernel, Convolutional/Pool, Output",AIO:DeepNeuralNetwork
 Graph Convolutional Network,AIO:GraphConvolutionalNetwork,Network,GCN,,GCN is a type of convolutional neural network that can work directly on graphs and take advantage of their structural information. (https://arxiv.org/abs/1609.02907),https://arxiv.org/abs/1609.02907,"Input, Hidden, Hidden, Output",AIO:DeepNeuralNetwork
 Recurrent Neural Network,AIO:RecurrentNeuralNetwork,Network,RecNN|Recurrent Network|RN,,"A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs.",https://en.wikipedia.org/wiki/Recurrent_neural_network,"Input, Memory Cell, Output",AIO:DeepNeuralNetwork

diff --git a/aio.json b/aio.json
@@ -15787,8 +15787,8 @@
         } ]
       }
     }, {
-      "id" : "https://w3id.org/aio/DeepFeedFoward",
-      "lbl" : "Deep FeedFoward",
+      "id" : "https://w3id.org/aio/DeepFeedForward",
+      "lbl" : "Deep FeedForward",
       "type" : "CLASS",
       "meta" : {
         "definition" : {
@@ -21286,7 +21286,7 @@
       "pred" : "is_a",
       "obj" : "https://w3id.org/aio/DeepNeuralNetwork"
     }, {
-      "sub" : "https://w3id.org/aio/DeepFeedFoward",
+      "sub" : "https://w3id.org/aio/DeepFeedForward",
       "pred" : "is_a",
       "obj" : "https://w3id.org/aio/DeepNeuralNetwork"
     }, {

diff --git a/aio.obo b/aio.obo
@@ -2669,8 +2669,8 @@ synonym: "DCN" EXACT []
 is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network
 
 [Term]
-id: https://w3id.org/aio/DeepFeedFoward
-name: Deep FeedFoward
+id: https://w3id.org/aio/DeepFeedForward
+name: Deep FeedForward
 def: "The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network." [https://en.wikipedia.org/wiki/Feedforward_neural_network] {type="owl:Axiom"}
 comment: Input, Hidden, Output
 synonym: "DFF" EXACT []
@@ -3990,7 +3990,7 @@ name: Neural Turing Machine Network
 def: "A Neural Turing machine (NTMs) is a recurrent neural network model. The approach was published by Alex Graves et al. in 2014. NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers. An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent. An NTM with a long short-term memory (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from examples alone." [https://en.wikipedia.org/wiki/Neural_Turing_machine] {type="owl:Axiom"}
 comment: Input, Hidden, Spiking Hidden, Output
 synonym: "NTM" EXACT []
-is_a: https://w3id.org/aio/DeepFeedForward
+is_a: https://w3id.org/aio/DeepFeedForward ! Deep FeedForward
 is_a: https://w3id.org/aio/LongShortTermMemory ! Long Short Term Memory
 
 [Term]
@@ -4161,7 +4161,7 @@ comment: Input, Hidden, Output
 synonym: "Radial Basis Function Network" EXACT []
 synonym: "RBFN" EXACT []
 synonym: "RBN" EXACT []
-is_a: https://w3id.org/aio/DeepFeedFoward ! Deep FeedFoward
+is_a: https://w3id.org/aio/DeepFeedFoward
 
 [Term]
 id: https://w3id.org/aio/RandomBrightnessLayer

diff --git a/aio.owl b/aio.owl
@@ -7916,13 +7916,7 @@ class labels for these objects. The resulting predictor can be used to attach cl
 
     <!-- https://w3id.org/aio/DeepFeedForward -->
 
-    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedForward"/>
-
-
-
-    <!-- https://w3id.org/aio/DeepFeedFoward -->
-
-    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedFoward">
+    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedForward">
         <rdfs:subClassOf rdf:resource="https://w3id.org/aio/DeepNeuralNetwork"/>
         <obo:IAO_0000115>The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.</obo:IAO_0000115>
         <oboInOwl:hasExactSynonym>DFF</oboInOwl:hasExactSynonym>
@@ -7931,17 +7925,23 @@ class labels for these objects. The resulting predictor can be used to attach cl
         <oboInOwl:hasExactSynonym>MLP</oboInOwl:hasExactSynonym>
         <oboInOwl:hasExactSynonym>Multilayer Perceptoron</oboInOwl:hasExactSynonym>
         <rdfs:comment>Input, Hidden, Output</rdfs:comment>
-        <rdfs:label>Deep FeedFoward</rdfs:label>
+        <rdfs:label>Deep FeedForward</rdfs:label>
     </owl:Class>
     <owl:Axiom>
-        <owl:annotatedSource rdf:resource="https://w3id.org/aio/DeepFeedFoward"/>
+        <owl:annotatedSource rdf:resource="https://w3id.org/aio/DeepFeedForward"/>
         <owl:annotatedProperty rdf:resource="http://purl.obolibrary.org/obo/IAO_0000115"/>
         <owl:annotatedTarget>The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.</owl:annotatedTarget>
         <oboInOwl:hasDbXref>https://en.wikipedia.org/wiki/Feedforward_neural_network</oboInOwl:hasDbXref>
     </owl:Axiom>
 
 
 
+    <!-- https://w3id.org/aio/DeepFeedFoward -->
+
+    <owl:Class rdf:about="https://w3id.org/aio/DeepFeedFoward"/>
+
+
+
     <!-- https://w3id.org/aio/DeepNeuralNetwork -->
 
     <owl:Class rdf:about="https://w3id.org/aio/DeepNeuralNetwork">