Tensor modelling for fine-grained type entity inference in knowledge graphs

Top-[formula omitted] star queries on knowledge graphs through semantic-aware bounding match scores

Recently, large-scale knowledge graphs (KGs) have become a key asset for search, analytics, recommendations and data integration. Large-scale KGs provide millions of facts about the real world. Each fact is composed as (subject, relation, object), e.g., the triplet (“Cristiano Ronaldo”, playFor, “Real Madrid”). However, these facts are blind to the temporal dimension. Actually, knowledge in practice is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of building temporal knowledge graphs. In particular, knowledge in temporal KG is represented as (subject, relation, object, valid time), e.g., (“Cristiano Ronaldo”, playFor, “Real Madrid”, “[2009, 2018]”. However, research on temporal KG is very current and there are still many problems needed to be addressed. One obvious problem is that the size of temporal KG is still very small. For example, only 6.6% of the facts are time-aware in one of the largest knowledge graphs, YAGO3. In addition, 71% of people have no known place of birth, and 75% have no known nationality in Freebase. Furthermore, over 87.7% of the facts are uncovered in Japanese DBpedia compared with English DBpedia. Therefore, in this thesis, we study how to enlarge and enrich temporal knowledge graphs from three aspects, namely, temporal KG enrichment. In particular, we study the enrichment problem from the following aspects: (1) volume, (2) completeness, and (3) coverage.Our first solution is temporal knowledge harvesting which extracts temporal knowledge from free text directly. However, text corpus is noisy, and extracting structured temporal facts with high accuracy and coverage is very challenging. Inspired by pattern-based systems, we propose a temporal knowledge harvesting framework. In particular, we propose various techniques to extract temporal patterns, including corpus annotation, pattern generation, scoring and clustering. These techniques can reduce ambiguity in the text corpus and can improve both the accuracy and coverage of the extracted patterns. Second, we leverage the extracted patterns to harvest temporal knowledge. To improve the accuracy, we propose a parse-tree-based method. And to increase the coverage, we consider the relationships between tree components, including part of speech (POS), clause types, constituency and dependency. Experiments on real-world datasets verify the effectiveness of our proposed framework.Our second solution is temporal knowledge graph completion. Temporal KG completion is the task of inferring unobserved edges between entity pairs. Generally, temporal KG completion relies on the temporal KG embeddings technique, which learns the low-dimensional representations of all KG components. As research on temporal KG embedding is very current, we study both the embedding and the completion problems. In particular, we observe that contexts are extremely useful for learning the representations of entities and for inferring the unknown time intervals. As a result, we propose a context-aware embedding model for KG embeddings and a context-based temporal inference model for KG completion. In our embedding model, we not only capture factual plausibility as traditional methods did, but also propose a new measure on contexts, named temporal consistency. It measures how well the target entity interacts with its surrounding contexts on the temporal dimension. Our completion model is based on the embedding model, and further captures the interactions on the entity dimension. Extensive experiments verify the effectiveness of our models.Our third solution is temporal knowledge graph alignment which aims to discover the SameAs edges across two temporal KGs. Not like previous attribute-based alignment models, we further divide attribute facts into character facts, digit facts and time facts. In particular, we observe that the context information is extremely useful for the identification of the same entities. Therefore, we propose an alignment model on leveraging temporal contexts to represent entities. However, contexts of the same object can be very different. For example, the value of career predicate changed from “football player” to “coach” for the football player “Zidane”. This is because entities are evolving over time and thus the predicate values can be different. In our framework, we propose an alignment module that simulates the entity evolving process. Specifically, this module captures the interactions between contexts and aggregates context information to represent the entity. Lastly, we found that not all contexts are relevant, e.g., height v.s. career. Actually, only the relevant contexts are useful for capturing the evolving. Therefore, we propose a clustering approach for grouping relevant contexts together. Our experimental results validate the superiority of our proposed alignment model.

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning.

Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task. Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest. Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. Though much smaller, KG abstractions preserve inconsistency and their explanations. Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.

Knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. Knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called Hierarchy-Aware Paired Relation Vectors Knowledge Graph Embedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task.

EASC: An exception-aware semantic compression framework for real-world knowledge graphs

Entity set expansion with semantic features of knowledge graphs

Retrieval-augmented generation (RAG) enhances the reasoning capabilities of large language models (LLMs) by incorporating external knowledge. Among available sources, knowledge graphs (KGs) offer a structured and reliable foundation for factual information, making them increasingly popular in efforts to improve reasoning faithfulness in RAG. Most existing KG-based RAG methods rely on LLMs to extract knowledge from KGs. However, these approaches often require costly fine-tuning and struggle to navigate deep graph structures, limiting their effectiveness in multi-hop reasoning tasks. To address these challenges, we propose Stepwise Contrastive Reasoning (SCR), a lightweight framework that integrates graph structure and textual context for efficient and interpretable RAG over KGs. SCR combines relational message passing layers to encode KG entities with a Transformer encoder for processing question text. It decomposes reasoning into a series of alignment steps. At each step, SCR compares the current topic entity and its neighbors with the question representation, selecting the most relevant entity as the next topic entity. The question is then updated with this entity's textual description. This process continues until the selected entity no longer changes, indicating that the answer entity has been reached. Through stepwise alignment, SCR enables compact models to perform faithful and interpretable reasoning over large-scale KGs. Extensive experiments on several widely used KGQA benchmarks demonstrate that SCR not only achieves state-of-the-art performance but also effectively boosts the capabilities of smaller language models to match those of LLMs.

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study.

We examine the embedding approach to reason new relational facts from a largescale knowledge graph and a text corpus. We propose a novel method of jointly embedding entities and words into the same continuous vector space. The embedding process attempts to preserve the relations between entities in the knowledge graph and the concurrences of words in the text corpus. Entity names and Wikipedia anchors are utilized to align the embeddings of entities and words in the same space. Large scale experiments on Freebase and a Wikipedia/NY Times corpus show that jointly embedding brings promising improvement in the accuracy of predicting facts, compared to separately embedding knowledge graphs and text. Particularly, jointly embedding enables the prediction of facts containing entities out of the knowledge graph, which cannot be handled by previous embedding methods. At the same time, concerning the quality of the word embeddings, experiments on the analogical reasoning task show that jointly embedding is comparable to or slightly better than word2vec (Skip-Gram).

Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBpedia which supports a wide range of different semantics of relevance based on numerous types of relations and attributes. As users may lack the expertise to formalize the desired semantics, supervised methods have emerged to learn the hidden user-defined relevance from user-provided examples. Along this line, in this paper we propose a novel generative model over KGs for relevance search, named GREASE. The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities. It is also extended to support properties that constrain answer entities. Extensive experiments on two large-scale KGs demonstrate that GREASE has advanced the state of the art in effectiveness, expressiveness, and efficiency.

The prosperous development of cognition and human-level intelligence have made the creation of large-scale domain knowledge graphs (KGs) a hot research field. Entity alignment (EA), which discovers entity pairs that represent the same real object in different KGs, is the key technology for building large scale knowledge graph. Existing EA methods rely mainly on the vector representation of the entity and ignoring the semantic features of an entity in a KG, result in poor alignment quality. To address this limitation, we propose a new EA framework based on knowledge embeddings (KEs) and type matching constraints. By embedding two KGs into a unified vector space, we combine the similarity of the entity vector and the degree of the entity type matching to perform more accurate cross-KG EA. The experimental results show that our proposed method has very good performance on both cross-KG and cross-language data sets. On the DBP-YG dataset, in terms of Hit@1, Hits@10, and MRR, our methods have an average increase of 27% over the baseline method MTransE. In most cases, our method significantly improves the accuracy of EA compared with state-of-the-art methods.

Knowledge graph completion is an important research issue in knowledge graph construction, knowledge engineering, and natural language processing. A knowledge graph is a knowledge support for realizing accurate knowledge services in general and professional fields. It is also an important breakthrough foundation in information retrieval, question-and-answer interactions, and information recommendation. The low quality and small scale of the knowledge graph are the main bottlenecks that hinder its wide applications. The purpose of knowledge graph completion is to build a large-scale and high-quality knowledge graph for continuously updating and expanding the knowledge graph. Aiming at the difficulty of knowledge graph completion methods to extract deep semantic features from auxiliary information, such as unstructured texts, this study proposes a knowledge graph completion method based on parsing graph embedding and weighted graph convolutional network. This method uses the weighted graph convolutional network to model the semantic dependency parsing of the entity description and construct the semantic dependency parsing graph embedding. Furthermore, it introduces a multi-grained sentence-embedding generation method of the entity description, which is intended to build entity representation learning that can capture multi-grained semantics and deep-level semantic features. The experimental results on two public datasets show that the proposed knowledge graph completion approach outperforms the existing methods, thereby demonstrating its effectiveness and superiority.

Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths. In this paper, we study the problem of how to better embed entities and relations of knowledge graphs into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths and propose a novel relation path embedding model named as RPE. Specifically, with the corresponding relation and path projections, RPE can simultaneously embed each entity into two types of latent spaces. Moreover, type constraints are extended from traditional relation-specific type constraints to the proposed path-specific type constraints and both of the two type constraints can be seamlessly incorporated into RPE. The proposed model is evaluated on the benchmark tasks of link prediction and triple classification. The results of experiments demonstrate our method outperforms all baselines on both tasks. They indicate that our model is capable of catching the semantics of relation paths, which is significant for knowledge representation learning.

Relational graph convolutional networks (RGCNs) have been successful in learning from knowledge graphs. However, training on large-scale knowledge graphs becomes challenging due to the exponential growth of the neighborhood size across the network layers. Moreover, knowledge graphs have multiple relations, and often, the literals can have multimodal content; these properties make it extra challenging to scale up the training of RGCNs to large-scale graphs. Graph sampling techniques have been shown to be effective in scaling learning to large graphs by reducing the number of processed nodes and lowering memory usage. However, only a few studies have focused on sampling for knowledge graphs. In this work, we introduce ReWise, a relation-wise sampling framework that includes a family of sampling methods designed for knowledge graphs. Our experiments demonstrate that sampling reduces the memory usage up to 50% lower than the case without sampling while maintaining the same classification accuracy and, in some cases, outperforming it. Additionally, we show that our sampling strategy is compatible with the multimodal RGCN, showing the same behavior as RGCNs.