scholarly journals Message Passing for Hyper-Relational Knowledge Graphs

2020 ◽  
Author(s):  
Mikhail Galkin ◽  
Priyansh Trivedi ◽  
Gaurav Maheshwari ◽  
Ricardo Usbeck ◽  
Jens Lehmann
Keyword(s):  
Message Passing ◽  
Relational Knowledge ◽  
Knowledge Graphs

scholarly journals Message Passing for Complex Question Answering over Knowledge Graphs

2019 ◽  
Author(s):  
Svitlana Vakulenko ◽  
Javier David Fernandez Garcia ◽  
Axel Polleres ◽  
Maarten de Rijke ◽  
Michael Cochez
Keyword(s):  
Message Passing ◽  
Question Answering ◽  
Knowledge Graphs ◽  
Complex Question

scholarly journals Representation Learning of Knowledge Graphs with Embedding Subspaces

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhua Li ◽  
Xuefeng Xian ◽  
Xusheng Ai ◽  
Zhiming Cui
Keyword(s):  
Language Model ◽  
Representation Learning ◽  
Training Data ◽  
Knowledge Graph ◽  
Two Phase ◽  
Long Tail ◽  
Relational Knowledge ◽  
Supervised Methods ◽  
Knowledge Graphs ◽  
The Cost

Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.

scholarly journals A semantic search approach for hyper relational knowledge graphs

2021 ◽  
Author(s):  
Veronica dos Santos ◽  
Sérgio Lifschitz
Keyword(s):  
Information Retrieval ◽  
Semantic Search ◽  
Similarity Function ◽  
Search Results ◽  
Relational Knowledge ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems ◽  
Knowledge Graphs ◽  
Search Approach

Information Retrieval Systems usually employ syntactic search techniques to match a set of keywords with the indexed content to retrieve results. But pure keyword-based matching lacks on capturing user's search intention and context and suffers of natural language ambiguity and vocabulary mismatch. Considering this scenario, the hypothesis raised is that the use of embeddings in a semantic search approach will make search results more meaningfully. Embeddings allow to minimize problems arising from terminology and context mismatch. This work proposes a semantic similarity function to support semantic search based on hyper relational knowledge graphs. This function uses embeddings in order to find the most similar nodes that satisfy a user query.

scholarly journals A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning

2019 ◽  
Author(s):  
Sebastijan Dumancic ◽  
Alberto Garcia-Duran ◽  
Mathias Niepert
Keyword(s):  
Euclidean Space ◽  
Knowledge Base ◽  
Relational Learning ◽  
Statistical Relational Learning ◽  
Representation Learning ◽  
The Other ◽  
Learning Approaches ◽  
Relational Knowledge ◽  
Knowledge Graphs ◽  
Relational Classification

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches such as Statistical relational learning, recent methods in (deep) representation learning have shown promising results for specialised tasks such as knowledge base completion. These approaches, also known as distributional, abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare distributional and symbolic relational learning approaches on various standard relational classification and knowledge base completion tasks. Furthermore, we analyse the properties of the datasets and relate them to the performance of the methods in the comparison. The results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.

scholarly journals Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification

2021 ◽  
pp. 107-124
Author(s):  
Ruud van Bakel ◽  
Teodor Aleksiev ◽  
Daniel Daza ◽  
Dimitrios Alivanistos ◽  
Michael Cochez
Keyword(s):  
Message Passing ◽  
Binary Classification ◽  
Extraction Methods ◽  
Query Answering ◽  
Graph Query ◽  
Heterogeneous Datasets ◽  
Community Effort ◽  
External Sources ◽  
Knowledge Graphs ◽  
Special Case

AbstractLarge, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing. To overcome this problem, several algorithms for approximate structured query answering have been proposed. Inspired by modern Information Retrieval metrics, these algorithms produce a ranking of all entities in the graph, and their performance is further evaluated based on how high in this ranking the correct answers appear.In this work we take a critical look at this way of evaluation. We argue that performing a ranking-based evaluation is not sufficient to assess methods for complex query answering. To solve this, we introduce Message Passing Query Boxes (MPQB), which takes binary classification metrics back into use and shows the effect this has on the recently proposed query embedding method MPQE.

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

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