Projecting knowledge graph queries into an embedding space using geometric models (points, boxes and spheres) can help to answer queries for large incomplete knowledge graphs. In this work, we propose a symbolic learning-free approach using fuzzy logic to address the shape-closure problem that restricted geometric-based embedding models to only a few shapes (e.g. ConE) for answering complex logical queries. The use of symbolic approach facilitates non-closure geometric models (e.g. point, box) to handle logical operators (including negation). This enabled our newly proposed spherical embeddings (SpherE) in this work to use a polar coordinate system to effectively represent hierarchical relation. Results show that the SpherE model can answer existential positive first-order logic and negation queries. We show that SpherE significantly outperforms the point and box embeddings approaches while generating semantically meaningful hierarchy-aware embeddings.

Maintenance short texts (MST), derived from maintenance work order records, encapsulate crucial information in a concise yet information-rich format. These user-generated technical texts provide critical insights into the state and maintenance activities of machines, infrastructure, and other engineered assets–pillars of the modern economy. Despite their importance for asset management decision-making, extracting and leveraging this information at scale remains a significant challenge. This paper presents MaintIE, a multi-level fine-grained annotation scheme for entity recognition and relation extraction, consisting of 5 top-level classes: PhysicalObject, State, Process, Activity and Property and 224 leaf entities, along with 6 relations tailored to MSTs. Using MaintIE, we have curated a multi-annotator, high-quality, fine-grained corpus of 1,076 annotated texts. Additionally, we present a coarse-grained corpus of 7,000 texts and consider its performance for bootstrapping and enhancing fine-grained information extraction. Using these corpora, we provide model performance measures for benchmarking automated entity recognition and relation extraction. The MaintIE scheme, corpus, and model are publicly available at https://github.com/nlp-tlp/maintie under the MIT license, encouraging further community exploration and innovation in extracting valuable insights from MSTs.

Recent geometric-based approaches have been shown to efficiently model complex logical queries (including the intersection operation) over Knowledge Graphs based on the natural representation of Venn diagram.Existing geometric-based models (using points, boxes embeddings), however, cannot handle the logical negation operation.Further, those using cones embeddings are limited to representing queries by two-dimensional shapes, which reduced their effectiveness in capturing entities query relations for correct answers.To overcome this challenge, we propose unbounded cylinder embeddings (namely CylE), which is a novel geometric-based model based on threedimensional shapes.Our approach can handle a complete set of basic first-order logic operations (conjunctions, disjunctions and negations).CylE considers queries as Cartesian products of unbounded sector-cylinders and consider a set of nearest boxes corresponds to the set of answer entities.Precisely, the conjunctions can be represented via the intersections of unbounded sector-cylinders.Transforming queries to Disjunctive Normal Form can handle queries with disjunctions.The negations can be represented by considering the closure of complement for an arbitrary unbounded sector-cylinder.Empirical results show that the performance of multihop reasoning task using CylE significantly increases over state-of-the-art geometric-based query embedding models for queries without negation.For queries with negation operations, though the performance is on a par with the best performing geometric-based model, CylE significantly outperforms a recent distributionbased model.

Knowledge Graph Construction (KGC) from text unlocks information held within unstructured text and is critical to a wide range of downstream applications. General approaches to KGC from text are heavily reliant on the existence of knowledge bases, yet most domains do not even have an external knowledge base readily available. In many situations this results in information loss as a wealth of key information is held within "non-entities". Domain-specific approaches to KGC typically adopt unsupervised pipelines, using carefully crafted linguistic and statistical patterns to extract co-occurred noun phrases as triples, essentially constructing text graphs rather than true knowledge graphs. In this research, for the first time, in the same flavour as Collobert et al.'s seminal work of "Natural language processing (almost) from scratch" in 2011, we propose a Seq2KG model attempting to achieve "Knowledge graph construction (almost) from scratch". An end-to-end Sequence to Knowledge Graph (Seq2KG) neural model jointly learns to generate triples and resolves entity types as a multi-label classification task through deep learning neural networks. In addition, a novel evaluation metric that takes both semantic and structural closeness into account is developed for measuring the performance of triple extraction. We show that our end-to-end Seq2KG model performs on par with a state of the art rule-based system which outperformed other neural models and won the first prize of the first Knowledge Graph Contest in 2019. A new annotation scheme and three high-quality manually annotated datasets are available to help promote this direction of research.

We present an overview of our triple extraction system for the ICDM 2019 Knowledge Graph Contest. Our system uses a pipeline-based approach to extract a set of triples from a given document. It offers a simple and effective solution to the challenge of knowledge graph construction from domain-specific text. It also provides the facility to visualise useful information about each triple such as the degree, betweenness, structured relation type(s), and named entity types.