CUTE: Querying Knowledge Graphs by Tabular Examples

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

Neural Machine Translation for Semantic-Driven Q&A Systems in the Factory Planning

Last years witnessed a shift from the potential utility in digitisation to a crucial need to enjoy activities virtually. In fact, before 2019, data curators recognised the utility of performing data digitisation, while during the lockdown caused by the COVID-19, investing in virtual and remote activities to make culture survive became crucial as no one could enjoy Cultural Heritage in person. The Cultural Heritage community heavily invested in digitisation campaigns, mainly modelling data as Knowledge Graphs by becoming one of the most successful Semantic Web technologies application domains. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers’ technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of the visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. By accessible to all, we mean that the proposed software framework is freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers’ technical challenges, which are carefully considered in the design and evaluation phases. This article first analyses the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data developers can rely on in designing and implementing data exploitation tools in this domain. Moreover, we point out challenges developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation in terms of performance and perceived utility according to end-users. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice.

We present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world knowledge graphs are difficult to use due to their sheer size and complexity and the challenging task of writing complex structured graph queries. Toward better usability of query systems over knowledge graphs, GQBE allows users to query knowledge graphs by example entity tuples without writing complex queries. In this demo we present: 1) a detailed description of the various features and user-friendly GUI of GQBE, 2) a brief description of the system architecture, and 3) a demonstration scenario that we intend to show the audience.

We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over “simple” tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, Graph Query By Example ( $\mathsf {GQBE}$ ), automatically discovers a weighted hidden maximum query graph based on input query tuples, to capture a user’s query intent. It then efficiently finds and ranks the top approximate matching answer graphs and answer tuples. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.

Taxonomic treatments, sections of publications documenting the features or distribution of a related group of organisms (called a “taxon”, plural “taxa”) in ways adhering to highly formalized conventions, and published in scientific journals, shape our understanding of global biodiversity (Catapano 2019). Treatments are the building blocks of the evolving scientific consensus on taxonomic entities. The semantics of these treatments and their relationships are highly structured: taxa are introduced, merged, made obsolete, split, renamed, associated with specimens and so on. Plazi makes this content available in machine-readable form using Resource Description Framework (RDF) . RDF is the standard model for Linked Data and the Semantic Web. RDF can be exchanged in different formats (aka concrete syntaxes) such as RDF/XML or Turtle. The data model describes graph structures and relies on Internationalized Resource Identifiers (IRIs) , ontologies such as Darwin Core basic vocabulary are used to assign meaning to the identifiers. For Synospecies, we unite all treatments into one large knowledge graph, modelling taxonomic knowledge and its evolution with complete references to quotable treatments. However, this knowledge graph expresses much more than any individual treatment could convey because every referenced entity is linked to every other relevant treatment. On synospecies.plazi.org, we provide a user-friendly interface to find the names and treatments related to a taxon. An advanced mode allows execution of queries using the SPARQL query language.

Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into the SPARQL format, thus bridging the gap between user-friendly interfaces and the sophisticated architecture of KGs. The challenge remains in adequately informing LLMs about the relevant context and structure of domain-specific KGs, e.g., in manufacturing, to improve the accuracy of generated queries. In this paper, we evaluate multiple strategies that use LLMs as mediators to facilitate information retrieval from KGs. We focus on the manufacturing domain, particularly on the Bosch Line Information System KG and the I40 Core Information Model. In our evaluation, we compare various approaches for feeding relevant context from the KG to the LLM and analyze their proficiency in transforming real-world questions into SPARQL queries. Our findings show that LLMs can significantly improve their performance on generating correct and complete queries when provided only the adequate context of the KG schema. Such context-aware prompting techniques help LLMs to focus on the relevant parts of the ontology and reduce the risk of hallucination. We anticipate that the proposed techniques help LLMs to democratize access to complex data repositories and empower informed decision-making in manufacturing settings.

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.

SummaryRDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired results efficiently on RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the “schema‐free” feature of RDF KG. In this paper, we study the problem of acquiring semantic approximate results for a simple SPARQL query instead of the complex expression. The basic idea behind is to use the real knowledge semantics to extend the query intention of given simplified SPARQL query, and then a semantic based greedy search over is designed to return top‐k similar results. To obtain the knowledge semantics efficiently, we define type similarity to reorganize the original KG as a corpus with semantic locality and then a context aware text embedding model is adopted to achieve the semantic vectors of existing knowledge. Afterwards, an approximate query method over RDF KG is designed to obtain top‐k similar results based on the knowledge semantics above. Extensive experiments over DBpedia dataset and QALD‐4 benchmark confirm our solution's superiority on both effectiveness and efficiency.

Over the past decade, Knowledge Graphs (KG) have emerged as a prominent repository for storing facts about the world in a linked data architecture. Providing machines with the capability of exploring such Knowledge Graphs and answering natural language questions over them, has been an active area of research. The purpose of this work, is to delve further into the research of retrieving information stored in KGs, based on the natural language questions posed by the user. Knowledge Graph Question Answering (KGQA) aims to produce a concise answer to a user question, such that the user is exempt from using KG vocabulary and overheads of learning a formal query language. Existing KGQA systems have achieved excellent results over Simple Questions, where the information required is limited to a single triple and a single formal query pattern. Our motivation is to improve the performance of KGQA over Complex Questions, where formal query patterns significantly vary, and a single triple is not confining for all the required information. Complex KGQA provides several challenges such as understanding semantics and syntactic structure of questions, Entity Linking, Relation Linking and Answer Representation. Lack of suitable datasets for complex question answering further adds to research gaps. Hence, in this thesis, we focus the research objective of laying the foundations for the advancement of the state-of-the-art for Complex Question Answering over Knowledge Graphs, by providing techniques to solve various challenges and provide resources to fill the research gaps. First, we propose Normalized Query Structure (NQS), which is a linguistic analyzer module that helps the QA system to detect inputs and intents and the relation between them in the users' question. NQS acts like an intermediate language between natural language questions and formal expressions to ease the process of query formulation for complex questions. We then developed a framework named LC-QuAD to generate large scale question answering dataset by reversing the process of question answering, thereby translating natural language questions from the formal query using intermediate templates. Our goal is to use this framework for high variations in the query patterns and create a large size dataset with minimum human effort. The first version of the dataset consists of 5,000 complex questions. By extending the LC-QuAD framework to support Reified KGs and crowd-sourcing, we published the second version of the dataset as LC-QuAD 2.0, consisting of 30,000 questions with their paraphrases and has higher complexity and new variations in the questions. To overcome the problem of Entity Linking and Relation Linking in KGQA, we develop EARL, a module performing these two tasks as a single joint task for complex question answering. We develop approaches for this module, first by formalizing the task as an instance of the Generalized Traveling Salesman Problem (GTSP) and the second approach uses machine learning to exploit the connection density between nodes in the Knowledge Graph. Lastly, we create another large scale dataset to answer verbalization and provide results for multiple baseline system s on it. The Verbalization dataset is introduced to make the system's response more human-like. The NQS based KGQA system was next to the best system in terms of accuracy on the QALD-5 dataset. We empirically prove that NQS is robust to tackle paraphrases of the questions. EARL achieves the state of the art results in Entity Linking and Relation Linking for question answering on several KGQA datasets. The dataset curated in this thesis has helped the research community to move forward in the direction of improving the accuracy of complex question answering as a task as other researchers too developed several KGQA systems and modules around these published datasets. With the large-scale datasets, we have encouraged the use of large scale machine learning, deep learning and emergence of new techniques to advance the state-of-the-art in complex question answering over knowledge graphs. We further developed core components for the KGQA pipeline to overcome the challenges of Question Understanding, Entity-Relation Linking and Answer Verbalization and thus achieve our research objective. All the proposed approaches mentioned in this thesis and the published resources are available at https://github.com/AskNowQA and are released under the umbrella project AskNow.

Purpose: The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Most of the storage solutions that support GraphQL rely on a translation layer to map the said language to another query language that they support natively, for example SPARQL. Methodology: Our main innovation is a multi-way left-join algorithm inspired by worst-case optimal multi-way join algorithms. This novel algorithm enables the native execution of GraphQL queries over RDF knowledge graphs. We evaluate our approach in two settings using the LinGBM benchmark generator. Findings: The experimental results suggest that our solution outperforms the state-of-the-art graph storage solution for GraphQL with respect to both query runtimes and scalability. Value: Our solution is implemented in an open-sourced triple store, and is intended to advance the development of representation-agnostic storage solutions for knowledge graphs.

Medical knowledge graph can potentially help knowledge discovery from clinical data, assisting clinical decision making and personalized treatment recommendation. This paper proposes a framework for automated medical knowledge graph construction based on semantic analysis. The framework consists of a number of modules including a medical ontology constructor, a knowledge element generator, a structured knowledge dataset generator, and a graph model constructor. We also present the implementation and application of the constructed knowledge graph with the framework for personalized treatment recommendation. Our experiment dataset contains 886 patient records with hypertension. The result shows that the application of the constructed knowledge graph achieves dramatic accuracy improvements, demonstrating the effectiveness of the framework in automated medical knowledge graph construction and application.

One table and seven figures of this paper. Table 1 presents Wang Zai’s personal information for more consistent question answering. Figure 1 is an overview of Sogou knowledge graph construction framework. The framework could be divided into three parts: Data Preparation contains operations including collecting data from various sources, extracting data from both structured source and free text and normalizing data; Knowledge graph construction contains all models to build a knowledge graph based on the extracted and normalized data; Application is composed of applications or services of a knowledge graph. A box with solid line represents an operation or model to process data while a box with dashed line represents the intermediate data. Figure 2 shows overview of Sogou knowledge graph storage architecture. Figure 3 shows after query entity linking, entity-related information and pictures are shown in search results. Figure 4 shows that search engine also gives related entity recommendations, due to entity relations in our KG. Figure 5 shows long text entity service used for Anchor Text generation. Figure 6 presents KBQA in Sogou Search Engine, which answers the query directly in the first search result. In Figure 7, a user is having a dialogue with our conversational assistant, Wang Zai.

Linked open data in authoring virtual exhibitions

Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose a Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.