Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective

A knowledge base is a large repository of facts usually represented as triples, each consisting of a subject, a predicate, and an object. The triples together form a graph, i.e., a knowledge graph. The triple representation in a knowledge graph offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of triples (i.e., a graph) into natural sentences. We take an encoder-decoder based approach. Specifically, we propose a Graph encoder with Content-Planning capability (GCP) to encode an input graph. GCP not only works as an encoder but also serves as a content-planner by using an entity-order aware topological traversal to encode a graph. This way, GCP can capture the relationships between entities in a knowledge graph as well as providing information regarding the proper entity order for the decoder. Hence, the decoder can generate sentences with a proper entity mention ordering. Experimental results show that GCP achieves improvements over state-of-the-art models by up to 3.6%, 4.1%, and 3.8% in three common metrics BLEU, METEOR, and TER, respectively. The code is available at (https://github.com/ruizhang-ai/GCP/).

Natural language processing (NLP) is an important and challenging field of computer science. With the advent of computers and related technologies, NLP has grown out of the field of linguistics and is getting more attention in the computer field to support a wide variety of intelligent applications. NLP basically refers to automatic processing of natural languages by computers or machines in order to understand human language in written or spoken form. Ontologies are semantic models that provide general description of the types and structures of objects in a particular subject area or domain. Core components of ontology include concepts, relationships, instances, and axioms. Ontology is used to depict the hierarchical organization of concepts and their relationships. Knowledge graphs represent a set of interconnected descriptions of objects or concepts, and are sometimes referred to as semantic networks. In short, an ontology can be treated as a meta-model for data and knowledge graphs as a representation of the actual data or facts. An ontology (a formal description of relationships) plays a vital role in building a knowledge graph. Ontology and knowledge graphs have proven to be powerful tools to manage enterprise or big data. Today, a number of applications rely on ontology and knowledge graphs, such as knowledge management, information discovery, text extraction, semantic search, content-based recommendation, and intelligent chatbots. This chapter focuses on the importance of ontology and knowledge graphs for Developing NLP-based applications is difficult because computers require structured data, and human speech is highly complex, extremely diverse, unstructured in nature, and often ambiguous. In general, manual creation of knowledge graphs is expensive, therefore any automation of the process is highly desirable. Here, ontologies and knowledge graphs play a crucial role by providing a well-structured knowledge base, allowing semi-automatic creation of a lexicon to be used effectively in language translations, spellcheckers, keyword extraction and similar NLP applications.

Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried knowledge graph's vocabulary and structure, as well as the syntax of the structured query language used to express the user's information needs. To increase the user base of these KGs, a set of Question Answering (QA) systems that use natural language to query these knowledge graphs have been introduced. However, finding a benchmark that accurately evaluates the quality of a QA system is a difficult task due to (1) the high degree of variation in the fine-grained properties among the existing benchmarks, (2) the static nature of the existing benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real-world deployment over KGs that are different from those that were used in the evaluation of the QA systems. In this paper, we introduce Maestro, a benchmark generation system for question answering over knowledge graphs. Maestro can generate a new benchmark for any KG given the KG and, optionally, a text corpus that covers this KG. The benchmark generated by Maestro is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties. Maestro also generates high-quality natural language questions with various utterances that are on par with manually-generated ones to better evaluate QA systems.

Millions of HTML tables containing structured data can be found on the Web. With their wide coverage, these tables are potentially very useful for filling missing values and extending cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. As a prerequisite for being able to use table data for knowledge base extension, the HTML tables need to be matched with the knowledge base, meaning that correspondences between table rows/columns and entities/schema elements of the knowledge base need to be found. This paper presents the T2D gold standard for measuring and comparing the performance of HTML table to knowledge base matching systems. T2D consists of 8 700 schema-level and 26 100 entity-level correspondences between the WebDataCommons Web Tables Corpus and the DBpedia knowledge base. In contrast related work on HTML table to knowledge base matching, the Web Tables Corpus (147 million tables), the knowledge base, as well as the gold standard are publicly available. The gold standard is used afterward to evaluate the performance of T2K Match, an iterative matching method which combines schema and instance matching. T2K Match is designed for the use case of matching large quantities of mostly small and narrow HTML tables against large cross-domain knowledge bases. The evaluation using the T2D gold standard shows that T2K Match discovers table-to-class correspondences with a precision of 94%, row-to-entity correspondences with a precision of 90%, and column-to-property correspondences with a precision of 77%.

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.

Multi-source temporal knowledge graph embedding for edge computing enabled internet of vehicles

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.

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

Extracting information from unstructured and semi-structured geoscience literature is a crucial step in conducting geological research. The traditional machine learning extraction paradigm requires a substantial amount of high-quality manually annotated data for model training, which is time-consuming, labor-intensive, and not easily transferable to new fields. Recently, large language models (LLMs) (e.g., ChatGPT, GPT-4, and LLaMA), have shown great performance in various natural language processing (NLP) tasks, such as question answering, machine translation, and text generation. A substantial body of work has demonstrated that LLMs possess strong in-context learning (ICL) and even zero-shot learning capabilities to solve downstream tasks without specifically designed supervised fine-tuning. In this paper, we propose utilizing LLMs for geoscience literature information extraction. Specifically, we design a hierarchical PDF parsing pipeline and an automated knowledge extraction process, which can significantly reduce the need for manual data annotation, assisting geoscientists in literature data mining. For the hierarchical PDF parsing pipeline, firstly, a document layout detection model fine-tuned on geoscience literature is employed for layout detection, obtaining layout detection information for the document. Secondly, based on the document layout information, an optical character content parsing model is used for content parsing, obtaining the text structure and plain text corresponding to the content. Finally, the text structure and plain text are combined and reconstructed to ultimately obtain the parsed structured data. For the automated knowledge extraction process, firstly, the parsed long text is segmented into paragraphs to adapt to the input length limit of LLMs. Subsequently, a few-shot prompting method is employed for structured knowledge extraction, encompassing two tasks: attribute value extraction and triplet extraction. In attribute value extraction, prompts are generated automatically by the LLMs based on the subdomain and attribute names, facilitating the location and extraction of values related to subdomain attribute names in the text. For triplet extraction, the LLMs employ a procedural approach to entity extraction, entity type extraction, and relation extraction, following the knowledge graph structure pattern. Finally, the extracted structured knowledge is stored in the form of knowledge graphs, facilitating further analysis and integration of various types of knowledge from the literature. Our proposed approach turns out to be simple, flexible, and highly effective in geoscience literature information extraction. Demonstrations of information extraction in subdomains such as radiolarian fossils and fluvial facies have yielded satisfactory results. The extraction efficiency has significantly improved, and feedback from domain experts indicates a relatively high level of accuracy in the extraction process. The extracted results can be used to construct a foundational knowledge graph for geoscience literature, supporting the comprehensive construction and efficient application of a geoscience knowledge graph.

Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs). We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as "Psychiatric Knowledge Graphs", "Biomedical Knowledge Graphs", "Knowledge Graph Machine Learning Applications", "Knowledge Graph Applications for Biomedical Sciences". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications. A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry. This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.

In this chapter, we introduce the basic concept of a knowledge graph (KG). While a knowledge graph seems to be a very simple way of representing information, it turns out to be quite powerful and is almost a lingua franca of sorts between humans and machines. On the one hand, knowledge graphs, if constructed properly, contain much of the useful information that can be found in ‘natural language’ documents like newswire or even social media. On the other hand, machines can process knowledge graphs in a variety of ways, leading to applications and breakthroughs in areas like semantic search, question answering, entity resolution, and representation learning.

In real-world situations, several threat alerts are being investigated by the specialised staff. In order to prompt response to serve incidents or ignore false alarms, alerts are prioritised and analysed. Security professionals rely on information provided in the alert message. Insufficient information in alert messages raises challenges for security analysts that require them to keep track of all internal and external sources to identify the relevant information. In this paper, a Narrative Analytics-Assisted System (NAAS) is proposed, and a knowledge graph is used in the proposed system to present the relationships. The knowledge graph is proposed to capture the complex relationships between the alert and relevant information from the Internal and External knowledge bases to reduce the cognitive effort in information digestion and to understand a wealth of security data. To enable cooperation in the cyber risk management process, it is an inevitable necessity to generate the knowledge graph and interpret it in a human-friendly format. The current machine-friendly formats for reporting incidents from alerts are complex and of an extensive nature. These characteristics hamper the readability and contribution, therefore preventing humans from understanding and being up to date about the incident. NAAS contains four life cycles to assist an analyst to have a better perception of the elements of the environment by involving more staff in the risk management: (1) Analyses the alert, (2) designs the knowledge graph with the natural language sentences, (3) automatically implements the incident report in natural language by applying novel storytelling techniques from the knowledge graph, and (4) maintains it with the contribution of different levels of expertise. The performance of various NAAS's cycles is demonstrated in a case study with an example scenario from the Security Operations Centre (SOC) at an educational institution, highlighting its useability.

Chinese mineral question and answering system based on knowledge graph

In recent years, the ever-increasing quantities of entities in large knowledge bases on the Web, such as DBpedia, Freebase and YAGO, pose new challenges but at the same time open up new opportunities for intelligent information access. These knowledge bases (KBs) have become valuable resources in many research areas, such as natural language processing (NLP) and information retrieval (IR). Recently, almost every major commercial Web search engine has incorporated entities into their search process, including Google’s Knowledge Graph, Yahoo!’s Web of Objects and Microsoft’s Satori Graph/Bing Snapshots. The goal is to bridge the semantic gap between natural language text and formalized knowledge. Within the context of globalization, multilingual and cross-lingual access to information has emerged as an issue of major interest. Nowadays, more and more people from different countries are connecting to the Internet, in particular the Web, and many users can understand more than one language. While the diversity of languages on the Web has been growing, for most people there is still very little content in their native language. As a consequence of the ability to understand more than one language, users are also interested in Web content in other languages than their mother tongue. There is an impending need for technologies that can help in overcoming the language barrier for multilingual and cross-lingual information access. In this thesis, we face the overall research question of how to allow for semantic-aware and cross-lingual processing of Web documents and user queries by leveraging knowledge bases. With the goal of addressing this complex problem, we provide the following solutions: (1) semantic annotation for addressing the semantic gap between Web documents and knowledge; (2) semantic search for coping with the semantic gap between keyword queries and knowledge; (3) the exploitation of cross-lingual semantics for overcoming the language barrier between natural language expressions (i.e., keyword queries and Web documents) and knowledge for enabling cross-lingual semantic annotation and search. We evaluated these solutions and the results showed advances beyond the state-of-the-art. In addition, we implemented a framework of cross-lingual semantic annotation and search, which has been widely used for cross-lingual processing of media content in the context of our research projects.

Rule-verification is essential for the construction of high-quality knowledge bases. Compared with the traditional approach of inviting experts with domain knowledge to accomplish the rule-verification task, crowdsourcing is a more efficient way, which could save time and effort. However, the current presentation mode of rules relies primarily on predicate logic. On one hand, predicate logic is rather abstract and complex, making it difficult for ordinary users without domain knowledge to comprehend. On the other hand, the singular presentation mode lacks personalization, failing to provide targeted assistance to different users. In this work, to facilitate users’ understanding of rules, we are motivated to design three explainable interfaces by incorporating the information presentation modes including instance (INS), natural language (NL), and knowledge graph (KG). Through our first-round quasi-experimental study, we find that NL and KG interfaces are preferred by users in general for the rule-verification crowdsourcing tasks. More notably, we identify the important role of users’ personality, cognitive style, and tasks’ difficulty level in influencing users’ perception of the interface design. For instance, among those who score low on OpennesstoExperience , verbalizers perceive NL interface more useful than visualizers. In addition, users are more likely to use KG interface when dealing with some complicated tasks. Inspired by our findings, we further develop a personalized interface recommendation model with the consideration of individual-level factors and tasks’ features. According to the results of both modeling comparison and the second-round user evaluation, we observe that recommending personalized interface based on our designed model could not only enhance users’ subjective perception like perceived usefulness and satisfaction but also improve their task performance.