Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs

Inductive link prediction -- where entities during training and inference stages can be different -- has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing approaches do not take into account semantic correlations between relations, which are commonly seen in real-world knowledge graphs. To address this challenge, we propose a novel inductive reasoning approach, namely TACT, which can effectively exploit Topology-Aware CorrelaTions between relations in an entity-independent manner. TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs. Specifically, we categorize all relation pairs into several topological patterns, and then propose a Relational Correlation Network (RCN) to learn the importance of the different patterns for inductive link prediction. Experiments demonstrate that TACT can effectively model semantic correlations between relations, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the inductive link prediction task.Comment: Accepted to AAAI 202

Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions

Knowledge graph embeddings (KGE) have been validated as powerful methods for inferring missing links in knowledge graphs (KGs) that they typically map entities into Euclidean space and treat relations as transformations of entities. Recently, some Euclidean KGE methods have been enhanced to model semantic hierarchies commonly found in KGs, improving the performance of link prediction. To embed hierarchical data, hyperbolic space has emerged as a promising alternative to traditional Euclidean space, offering high fidelity and lower memory consumption. Unlike Euclidean, hyperbolic space provides countless curvatures to choose from. However, it is difficult for existing hyperbolic KGE methods to obtain the optimal curvature settings manually, thereby limiting their ability to effectively model semantic hierarchies. To address this limitation, we propose a novel KGE model called $\textbf{Hyp}$erbolic $\textbf{H}$ierarchical $\textbf{KGE}$ (HypHKGE). This model introduces attention-based learnable curvatures for hyperbolic space, which helps preserve rich semantic hierarchies. Furthermore, to utilize the preserved hierarchies for inferring missing links, we define hyperbolic hierarchical transformations based on the theory of hyperbolic geometry, including both inter-level and intra-level modeling. Experiments demonstrate the effectiveness of the proposed HypHKGE model on the three benchmark datasets (WN18RR, FB15K-237, and YAGO3-10). The source code will be publicly released at https://github.com/wjzheng96/HypHKGE

Relational structure-aware knowledge graph representation in complex space

Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Geospatial Query Answering Using Knowledge Graph Embeddings

Τα γραφήματα γεωχωρικής γνώσης πάσχουν από ελλιπή στοιχεία, τα οποία οφείλονται στις όχι πάντα αξιόπιστες πηγές δεδομένων. Αυτό επηρεάζει δραματικά τα αποτελέσματα της απάντησης γεωχωρικών ερωτημάτων με τις παραδοσιακές τεχνικές που χρησιμοποιούν τυποποιημένες γλώσσες ερωτημάτων όπως η stSPARQL ή η GeoSPARQL. Τα μοντέλα που βασίζονται στην ενσωμάτωση προβάλλουν τις οντότητες και τις σχέσεις του ερωτήματος που τίθεται στον συνεχή διανυσματικό χώρο, προβλέποντας, με αυτόν τον τρόπο, τις απαντήσεις στο ερώτημα που τίθεται. Ως εκ τούτου, μπορούν να χειριστούν ερωτήματα για τα οποία τα δεδομένα που απαιτούνται για την απάντησή τους δεν δηλώνονται ρητά στον γράφο γνώσης. Στην παρούσα ερευνητική εργασία, αναπτύξαμε το μοντέλο απάντησης γεωχωρικών ερωτημάτων με βάση την ενσωμάτωση, SQABo, το οποίο κωδικοποιεί τα γεωχωρικά ερωτήματα ως κουτιά στον χώρο ενσωμάτωσης και επιστρέφει τις απαντήσεις εντός του κουτιού. Δείχνουμε ότι αυτή η προσέγγιση έχει καλύτερες επιδόσεις από τις υπάρχουσες εργασίες στη βιβλιογραφία, οι οποίες κωδικοποιούν τα ερωτήματα ως σημεία στο διανυσματικό χώρο. Επιπλέον, διαθέτουμε ελεύθερα στην ερευνητική κοινότητα ένα σύνολο δεδομένων για την απάντηση ερωτημάτων για το YAGO2geo, έναν από τους πλουσιότερους και ακριβέστερους γράφους γεωχωρικής γνώσης, για μελλοντική έρευνα.Geospatial knowledge graphs suffer from incompleteness which is due to the not-alwaysreliable data sources. This dramatically affects the results of geospatial query answering with traditional techniques which use standard query languages like stSPARQL or GeoSPARQL.An alternative method for query answering is by using KG embeddings. Embedding-based models project entities and relations of the posed query onto the continuous vector space, predicting, this way, the answers to the posed query. Hence, they can handle queries for which the data required for their answering is not explicitly stated in the knowledge graph. In this research work, we have developed the embedding-based geospatial query answering model, SQABo, which encodes the geospatial queries as boxes into the embedding space and returns the answers inside the box. We show that this approach performs better than existing work in the literature, which encodes the queries as points in the vector space. Additionally, we make freely available a query-answering dataset for YAGO2geo, one of the richest and most precise geospatial knowledge graphs, to the research community for future research