Semantic Technologies: Enabler for Knowledge 4.0

Numerous large knowledge graphs, such as DBpedia, Wikidata, Yago and Freebase, have been developed in the last decade, which contain millions of facts about various entities in the world. These knowledge graphs have proven to be incredibly useful for intelligent Web search, question understanding, in-context advertising, social media mining, and biomedicine. As some researchers have pointed out, a knowledge graph is not just a graph database, but it should have a layer of conceptual knowledge, which is usually represented as a set of first-order rules. However, it is challenging to automatically extract first-order rules from large knowledge graphs. In particular, traditional models are usually unable to handle rule learning in large knowledge graphs. This chapter aims to present state-of-the-art techniques and models on learning first-order rules using representation learning. By first recalling some basics of rule learning in knowledge graphs, we will introduce useful techniques of embedding-based rule learning through major models such as RLvLR and TyRuLe, which embed paths in knowledge graphs into latent spaces for candidate rule search. Then we evaluate the rule learning efficiency and the quality of automatically learned rules by applying them in link prediction. Before the chapter is concluded, we will also discuss some future research problems in the area.

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 (KGs) represent heterogeneous domain knowledge on the Web and within organizations. There exist shapes constraint languages to define validating shapes to ensure the quality of the data in KGs. Existing techniques to extract validating shapes often fail to extract complete shapes, are not scalable, and are prone to produce spurious shapes. To address these shortcomings, we propose the Quality Shapes Extraction (QSE) approach to extract validating shapes in very large graphs, for which we devise both an exact and an approximate solution. QSE provides information about the reliability of shape constraints by computing their confidence and support within a KG and in doing so allows to identify shapes that are most informative and less likely to be affected by incomplete or incorrect data. To the best of our knowledge, QSE is the first approach to extract a complete set of validating shapes from WikiData. Moreover, QSE provides a 12x reduction in extraction time compared to existing approaches, while managing to filter out up to 93% of the invalid and spurious shapes, resulting in a reduction of up to 2 orders of magnitude in the number of constraints presented to the user, e.g., from 11,916 to 809 on DBpedia.

Scheduling satellite tasks requires intricate coordination of numerous interconnected activities, posing challenges for ground station operators who struggle to decipher autonomous decision-making systems without explanatory responses. This paper advocates for explainable artificial intelligence to address this gap, leveraging large language models and knowledge graphs to enhance the transparency of scheduling logic. The study explores how operators can retrieve relevant information through natural language queries by integrating language processing with knowledge graphs as core data structures. This approach allows for manual access to knowledge graphs, supplemented with textual explanations, and enables operators to explore different scheduling scenarios, thus improving system robustness and adaptability. Additionally, this paper analyzes how current knowledge graphs and natural language processing techniques are utilized and how they can enhance explainability in satellite scheduling. It investigates the modeling of satellite schedules and environmental data within the knowledge graph. The study introduces a novel approach for generating categorized queries, executable code for knowledge graph data extraction via language modeling, and providing explanatory answers. These are evaluated for correctness, validity, and linguistic quality, demonstrating the effectiveness of this approach.

Smart city systems are increasingly built with data analytics and machine learning techniques, basing on massive data sets. They have a heavy impact on human behavior and quality of life, and thus need to deliver a controllable and sufficiently transparent experience for the users.The aim of my work is to make smart city systems more interoperable and explainable, involving data visualization and communication techniques, sensor data processing, as well as the associated intelligent data value chain production and consumption. Here, the data and the information are shared employing Knowledge Graphs, that are becoming a key enabler for large-scale processing of massive collections of interrelated facts. Examples include the Google Knowledge Graph with dozens of billion facts, dataCommons, DBPedia, YAGO, and Knowledge Vault, a very large scale probabilistic knowledge graph created with information extraction methods for unstructured or semi-structured information. Specifically, Knowledge Graphs provide the means of development of the newest methods for data management, data fusion, data merging, and graph and network optimization and modeling, serving as a source of high quality data and a base for information integration.In particular, Knowledge Graphs help to infer new relationships out of existing facts, giving context and meaning to the content, and can be used in applications. For example, the data generated by a computer vision system could be semantically represented and shared across numerous systems, taking into account the needs and requirements of these systems, as well as the context, provenance, licensing and consent aspects of the generated data. I demonstrate Knowledge Graphs-based methods in advanced smart city applications from the domains such as automation and construction of buildings, energy efficiency, tourism, transport.

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber–physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.

Currently knowledge graph has been widely applied to various fields. Although the data scale is large, there is still a lot of useful but implicit information in it. Thus, a powerful reasoning system is required to derive these data. However, current reasoning systems cannot accomplish this task very well. On the one hand, stand-alone reasoning systems cannot meet the demand of large data. On the other hand, the reasoning ability of existing distributed reasoning systems is limited because of the lack of expressive inference rules. In this paper, we propose and implement a distributed reasoning system KGRL for knowledge graph based on OWL2 RL. It has a more powerful reasoning ability due to more expressive rules. It also supports optimization for redundant data. Besides, a rule-based algorithm is designed to find the inconsistent data. Experimental results show that KGRL can derive more implicit information efficiently compared to other reasoning systems. Moreover, KGRL is capable of eliminating redundant data, which can reduce the storage of knowledge graph by an average of 42%. Finally, KGRL also performs well for the detection of inconsistencies in knowledge graph.

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.

Learning structured embeddings of knowledge graphs with generative adversarial framework

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.

Besides the challenge that a human can ask one question in many different ways, a key aspect in Question Answering approaches over Knowledge Graphs (KGQA) is to deal with the vast amount of information present in the knowledge graphs. Modern real-world knowledge graphs contain nearly millions of entities and relationships. Additionally, they are enriched with new facts every day. However, not all facts are relevant for answering particular questions, thus fostering several challenges to KGQA systems, which require interpretable and query-able data. One solution to filtering the extra data in knowledge graphs is to rely on graph summarization techniques. Graph-based summarization approaches aim to resize knowledge graphs to be more concise and precise by storing only relevant information. In this paper, we propose a framework named LAUREN that applies different summarization techniques on knowledge graphs to be used in KGQA systems. Our experiments show that LAUREN summarizes large knowledge graphs such as DBpedia by 2 million entities and its summarization still achieves the same performance on both question answering and linking tasks compared to the complete DBpedia.

In recent years, Large Language Models (LLMs) have rapidly advanced in language understanding, reasoning, and generation, and are increasingly adopted as the “brain” of industrial intelligent systems. Nevertheless, in high‑risk and strongly regulated domains they still exhibit hallucination, weak domain grounding, limited interpretability, and privacy as well as security constraints. Knowledge graphs (KGs) encode domain entities, relations, rules, and events explicitly, providing controllable semantics and an explainable reasoning substrate. Retrieval‑augmented generation (RAG) injects external evidence into LLM prompting, while GraphRAG further introduces graph indexing and community‑level retrieval to preserve global structure and support multi‑hop reasoning. This review summarizes the evolution of LLMs, KG modeling and extraction, GraphRAG mechanisms, and a general fusion framework. Typical industrial applications are surveyed, and a coal mine flood emergency plan generation and evaluation approach is discussed to illustrate the practical value of graph‑grounded large models. KG‑enhanced retrieval also supports provenance tracking, allowing industrial users to audit the evidence behind model outputs.

We consider the task of exploratory search through graph queries on knowledge graphs. We propose to assist the user by expanding the query with intuitive suggestions to provide a more informative (full) query that can retrieve more detailed and relevant answers. To achieve this result, we propose a model that can bridge graph search paradigms with well-established techniques for information-retrieval. Our approach does not require any additional knowledge from the user and builds on principled language modelling approaches. We empirically show the effectiveness and efficiency of our approach on a large knowledge graph and how our suggestions are able to help build more complete and informative queries.

In recent years, the landscape of digital biomedicine and healthcare has been reshaped due to the disruptive breakthroughs in AI-facilitated by tremendous data and high-performance computers, large language models (LLMs) have transformed information technology from accessing data to performing analytical tasks. While demonstrating unprecedented capabilities, LLMs have been found unreliable in tasks requiring factual knowledge and rigorous reasoning. Biomedicine and healthcare, as an important vertical domain rapidly benefitting from progress in AI, necessitates strict requirements on the accuracy, controllability, and interpretability of analytical models, posing critical challenges for LLMs. Despite recent studies addressing the hallucination problem of LLMs, research on empowering LLMs with the ability to plan, reason, and ground with explicit knowledge has also started to prosper, especially in the biomedicine and healthcare domain. On the other hand, biomedical data are enormous and notoriously complex, coming from various sources (e.g., biomedical knowledge bases, online literature, and hospitals) and bearing various modalities (e.g., tables, texts, images and time-series). Healthcare professionals have spent decades collecting, cleaning, and curating various types of data. The processes are extremely costly, producing various datasets with different data schemas, coding systems, and quality standards, many privately owned by the creators, making their integrative analysis and utilization through unified AI techniques still rather challenging. The generalizability of LLMs across different types of data endow them strong promises in automating the processing of large-scale complex healthcare data such as into unified knowledge graphs (KGs). Our goal in this survey is to systematically investigate and summarize recent studies on the unification of LLMs and KGs, towards fully utilizing the value of complex data, unleashing the power of generative AI, and expediting next-generation AI for biomedicine and healthcare applications.

The unification of large language models (LLMs) and knowledge graphs (KGs) has emerged as a hot topic. At the LLM+KG'24 workshop, co-located with VLDB 2024 in Guangzhou, China, the key theme explored was important data management challenges and opportunities due to the effective interaction between LLMs and KGs. The report outlines major directions and approaches presented by various speakers during the workshop.