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Knowledge Representation: Knowledge has played a pivotal role in the development of civilizations. The 21st century has become the century of explosion of human

by MLres 7y ago

Knowledge Representation: Knowledge has played a pivotal role in the development of civilizations. The 21st century has become the century of explosion of humans knowledge thanks to the development of sophisticated communication systems. This indeed provides a great opportunity in front of the human being. However, proper representation and management of such a huge amount of knowledge is a challenging task. The other challenge is to reason over such a knowledge to get new knowledge. Knowledge Graph (KG) properly addresses the challenge of representation of the knowledge obtained by human. The KG represents knowledge in the form of multi-relational graph where entities are connected by different relations. This indeed more consistent with the essence of real world knowledge where the things are connected to each others.

MLres 7y ago
Learning over Symbolic Data: Knowledge graphs mostly contain symbolic data which are connected. Learning over such a data is indeed a challenging task in Machine Learning (ML) where ML models mostly work on vectors. Knowledge Graph Embedding properly addresses this challenge. The core idea is that there is an equivalent vector space for the symbolic KG space that follows the underlying structure. KGE aims to map symbolic KG to a vector space.
- MLres 7y ago
  Neural Based Embedding: Neural Networks (NNs) have been widely used to learn over data represented in the form of vectors. NNs can be used to learn over symbolic connected data (e.g., KG). They can be used to learn knowledge graph representation and provide a mapping between symbolic KG space and the corresponding vector space. In other words, the NNs embeds a symbolic KG to a vector space by preserving the underlying structure.
  - MLres 7y ago
    Reasoning by Learning:When the Neural Network maps the KG to a vector space and learns the KG representation, it can do reasoning: the NN gets a triple (e.g., (Bonn, IsAcityIn, Germany)) and determines if this is true or not. This prediction is resulted by a reasoning over the KG in the vector space.
    - MLres 7y ago
      From Logic to Algebra: After embedding a KG in its corresponding vector space, each symbolic elements (entities and relations) will be assigned a vector. Moving from symbols to vectors is equivalent with moving from logic to algebra. In other words, after mapping a KG to a vector space, logical rules can be converted to their corresponding algebraic formula. More concretely, considering the target vector space provided for the KG, one can derive the formula for each logical rules. Therefore, as there is an equivalent vector for each of the symbols, there will be an equivalent algebraic formula for each of the logical rules.
      - MLres 7y ago
        Capability vs practical rule injection: The question is that if the NNs are capable of encoding logical rules? The paper proposes a neural network and proves that the network is capable of expressing any ground truth over encoded rules in the KG. The paper focuses on one class NNs as well as one class of logical rules. Investigation of capability of different class of NNs in encoding different class of logical rules is indeed an important problem which can open a new window in front of ML. When one is sure that the KGE model has enough capability to encode a class of logical rules, deriving a formula for each of logical rules is essential. The derived formula can be used to guide the learning process. The paper reported that injection of rules in the learning process significantly improves performance the learner (KGE model). Therefore, the prior knowledge which is encoded as logical rules significantly improves the performance of the NN.

by MLres 7y ago

MLres 7y ago

Learning over Symbolic Data: Knowledge graphs mostly contain symbolic data which are connected. Learning over such a data is indeed a challenging task in Machine Learning (ML) where ML models mostly work on vectors. Knowledge Graph Embedding properly addresses this challenge. The core idea is that there is an equivalent vector space for the symbolic KG space that follows the underlying structure. KGE aims to map symbolic KG to a vector space.

MLres 7y ago
Neural Based Embedding: Neural Networks (NNs) have been widely used to learn over data represented in the form of vectors. NNs can be used to learn over symbolic connected data (e.g., KG). They can be used to learn knowledge graph representation and provide a mapping between symbolic KG space and the corresponding vector space. In other words, the NNs embeds a symbolic KG to a vector space by preserving the underlying structure.
- MLres 7y ago
  Reasoning by Learning:When the Neural Network maps the KG to a vector space and learns the KG representation, it can do reasoning: the NN gets a triple (e.g., (Bonn, IsAcityIn, Germany)) and determines if this is true or not. This prediction is resulted by a reasoning over the KG in the vector space.
  - MLres 7y ago
    From Logic to Algebra: After embedding a KG in its corresponding vector space, each symbolic elements (entities and relations) will be assigned a vector. Moving from symbols to vectors is equivalent with moving from logic to algebra. In other words, after mapping a KG to a vector space, logical rules can be converted to their corresponding algebraic formula. More concretely, considering the target vector space provided for the KG, one can derive the formula for each logical rules. Therefore, as there is an equivalent vector for each of the symbols, there will be an equivalent algebraic formula for each of the logical rules.
    - MLres 7y ago
      Capability vs practical rule injection: The question is that if the NNs are capable of encoding logical rules? The paper proposes a neural network and proves that the network is capable of expressing any ground truth over encoded rules in the KG. The paper focuses on one class NNs as well as one class of logical rules. Investigation of capability of different class of NNs in encoding different class of logical rules is indeed an important problem which can open a new window in front of ML. When one is sure that the KGE model has enough capability to encode a class of logical rules, deriving a formula for each of logical rules is essential. The derived formula can be used to guide the learning process. The paper reported that injection of rules in the learning process significantly improves performance the learner (KGE model). Therefore, the prior knowledge which is encoded as logical rules significantly improves the performance of the NN.