Questions tagged [knowledge-graph]
The knowledge-graph tag has no usage guidance.
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Convert specific domain knowledge text to a knowledge graph
As part of this semester assignment , I'm working on a project that aims to to represent the knowledge in "PMBOK 6th edition, section 11: Project Risk Management (page 395 -> 458)" and the knowledge ...
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Open question - approach to node classification in a directed multi-relational graph
The problem in hand is: In a strategy game, I need to select characters to form my team, in order to win against a second team, chosen by an opponent player. The universe of characters contains U ...
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A French version of Rebel
Is there an end-to-end trained transformer like Rebel for french data?
Rebel can extract entities and relations from text, yet as far as I know, it works only with english texts.
Is there any other ...
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Ontology-based knowledge graph construction from free-text
TL;DR
What is the best way to extract SPO triples from free-text with as little information loss as possible under the constraint that the nodes and relationships in the extracted triples are ...
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Need an example to understand Entity and Relation Types in Knowledge Graphs
I tried to ask ChatGPT about this but still could not understand its answer and it seems to be a generic answer like it gives to most of my questions.
I could understand Entity Types, but could not ...
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Augmented text classification with Knowledge Graphs
There is a paper 'Learning beyond datasets: Knowledge Graph Augmented Neural
Networks for Natural language Processing'. In this paper a method for classifying texts with additional knowledge graph ...
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Are there methods to represent entire knowledge graphs using a single embedding vector?
In a knowledge graph, embedding vectors can be learned for nodes (node embedding) and edges (edge embeddings). Is there a method to learn one single embedding vector for the entire knowledge graph?
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Exemplify key differences between entity linking and entity matching?
It seems to me that entity linking and entity matching are very similar.
Entity linking is also named named entity disambiguation; while entity matching is also called record linkage or deduplication, ...
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LSTM Feature engineering: using different Knowledge Graph data types
For a research project, I'm planning to use an LSTM to learn from sequences of KG entities. However, I have little experience using LSTMs or RNNs in general. During planning, a few questions ...
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How to deal with sentence without event triple?
I am new to machine learning, I want to extract event triple from news headlines. But there are many news headlines without event triples, only containing subject and predicate. How shall I deal with ...
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How can deep learning be applied to association rule mining?
Association rule mining is considered to be an old technique of AI. Rules are mined on statistical support. How can deep learning be applied to this? What are approaches for structured data (in a ...
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getting actual concepts value instead of its URI in ontology
I am using owl ontology for semantic analysis in emotional sentiment analysis project ,
I am trying to navigate the ontology to check a concepts and its relation ,
my ontology has classes like this :
<...
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How to unify weights in my dataset
I have a symptom-disease network that consists of four attributes: symptom, disease,
co-occurrence and TF-IDF.
I'm considering the TF-IDF attribute as the weight of my network edges and symptom and ...
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How to feed a Knowledge Base into Language Models?
I’m a CS undergrad trying to make my way into NLP Research. For some time, I have been wanting to incorporate "everyday commonsense reasoning" within the existing state-of-the-art Language ...
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Graph embeddings of Wikidata items
I'm trying to use PyTorch BigGraph pre-trained embeddings of Wikidata items for disambiguation. The problem is that the results I am getting by using dot (or cosine) similarity are not great. For ...
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(Graph Convolutional Network (GCN) based recommender system maintenance issue [closed]
I have built an item-item recommender model using (Graph Convolutional Network (GCN) for an E-commerce website. Could you please help me with the maintenance of the model.
How often should I retrain ...
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Knowledge Graph as an input to a neural network
I want to create a neural network that takes as an input a knolwedge subgraph(different types of nodes and different types of edges) to predict some properties. For instance an input in the graph can ...
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How to use ontologies for text classification?
I am new to machine learning and want to classify sentences using ontologies (taxonomies/ knowledge graphs) and supervised learning methods (I have an annotated training dataset).
My question is how ...
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What is the model architectural difference between transductive GCN and inductive GraphSAGE?
Difference of the model design.
It seems the difference is that GraphSAGE sample the data.
But what is the difference in model architecture.
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