Addressing the Scalability Bottleneck of Semantic Technologies at Bosch

At the heart of smart manufacturing is real-time semi-automatic decision-making. Such decisions are vital for optimizing production lines, e.g., reducing resource consumption, improving the quality of discrete manufacturing operations, and optimizing the actual products, e.g., optimizing the sampling rate for measuring product dimensions during production. Such decision-making relies on massive industrial data thus posing a real-time processing bottleneck

FAIR Knowledge Graph construction from text, an approach applied to fictional novels

A Knowledge Graph (KG) is a form of structured human knowledge depicting relations between entities, destined to reflect cognition and human-level intelligence. Large and openly available knowledge graphs (KGs) like DBpedia, YAGO, WikiData are universal cross-domain knowledge bases and are also accessible within the Linked Open Data (LOD) cloud, according to the FAIR principles that make data findable, accessible, interoperable and reusable. This work aims at proposing a methodological approach to construct domain-oriented knowledge graphs by parsing natural language content to extract simple triple-based sentences that summarize the analyzed text. The triples coded in RDF are in the form of subject, predicate, and object. The goal is to generate a KG that, through the main identified concepts, can be navigable and linked to the existing KGs to be automatically found and usable on the Web LOD cloud. © 2022 Copyright for this paper by its authors

Enhancing downstream tasks in Knowledge Graphs Embeddings: A Complement Graph-based Approach Applied to Bilateral Trade

Forecasting international trade flows is crucial for understanding global economic activity and serving as critical economic indicators used by economists and policymakers with significant implications for respective countries’ economic policies. However, this is a challenging task due to the complexity of the relationships between entities involved in international trade. While several approaches have been proposed, knowledge graph-based models have emerged as promising solutions for predicting international trade flows. In this paper, we introduce a synthetic triple-generation algorithm for enhancing downstream tasks in knowledge graph embeddings based on the graph complement. The algorithm identifies missing relationships between entities by exploiting the complement graph and generates high-quality synthetic triples that improve the accuracy of predictions. We validate the generated triples using several knowledge graph embedding methods and computing metrics. To perform an initial result screening, an international trade scenario is explored, demonstrating the effectiveness of the proposed approach in enhancing the performance of knowledge graph-based models for predicting international trade flows

A Quick Prototype for Assessing OpenIE Knowledge Graph-Based Question-Answering Systems

Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires a thorough understanding of the underlying graph-oriented structures and, at the same time, an appropriate query language, such as SPARQL, to access relevant data. Natural language interfaces are needed to enable non-technical users to query ever more complex data. The paper proposes a question-answering approach to support end users in querying graph-oriented knowledge bases. The system pipeline is composed of two main modules: one is dedicated to translating a natural language query submitted by the user into a triple of the form , while the second module implements knowledge graph embedding (KGE) models, exploiting the previous module triple and retrieving the answer to the question. Our framework delivers a fast OpenIE-based knowledge extraction system and a graph-based answer prediction model for question-answering tasks. The system was designed by leveraging existing tools to accomplish a simple prototype for fast experimentation, especially across different knowledge domains, with the added benefit of reducing development time and costs. The experimental results confirm the effectiveness of the proposed system, which provides promising performance, as assessed at the module level. In particular, in some cases, the system outperforms the literature. Finally, a use case example shows the KG generated by user questions in a graphical interface provided by an ad-hoc designed web application

Harnessing Graph Neural Networks to Predict International Trade Flows

In the realm of international trade and economic development, the prediction of trade flows between countries is crucial for identifying export opportunities. Commonly used log-linear regression models are constrained due to difficulties when dealing with extensive, high-cardinality datasets, and the utilization of machine learning techniques in predictions offers new possibilities. We examine the predictive power of Graph Neural Networks (GNNs) in estimating the value of bilateral trade between countries. We work with detailed UN Comtrade data that represent annual bilateral trade in goods between any two countries in the world and more than 5000 product groups. We explore two different types of GNNs, namely Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), by applying them to trade flow data. This study evaluates the effectiveness of GNNs relative to traditional machine learning techniques such as random forest and examines the possible effects of data drift on their performance. Our findings reveal the superior predictive capability of GNNs, suggesting their effectiveness in modeling complex trade relationships. The research presented in this work offers a data-driven foundation for decision-making and is relevant for business strategies and policymaking as it helps in identifying markets, products, and sectors with significant development potential

Towards a semantic model for IoT-based seismic event detection and classification

In the seismic domain, collecting seismic signal and alerting movements of earth crust is crucial for monitoring and forecasting seismic activities. At the same time, with the advent of the Internet of Things (IoT) paradigm, the device interoperability is the minimum requirement for communication among any available sensing device. Semantic web technologies promote this interoperability, by enhancing the quality of data that become ontology-annotated. The paper introduces an ontology model for describing the seismic domain, through the data collection from sensors, to gather seismic signals aimed at the seismic event recognition. The ontology has been built on the well-known SOSA and SSN ontologies, modeled to describe systems of sensors, actuators, and observations. The ontology, namely VEO (Volcano Event Ontology), has been modeled on actual data sensors, collected by a monitoring network at Mt. Vesuvius (Naples, Italy). Along with the ontology model of the seismic domain, a machine learning-based classification has been accomplished to identify seismic events (underwater explosions, quarry blasts, and thunders). A VEO-driven knowledge-base collects raw seismic data and detects events, accessible by SPARQL queries

Enabling a Semantic Sensor Knowledge Approach for Quality Control Support in Cleanrooms

With the explosion of big data technologies (BD), the pos-sibility to integrate those tools into daily company operations is more affordable and straightforward. On the other hand, Knowledge-based approaches such as graphs and different semantic approaches, although those have not been so popular in the industry in the past years, nowa-days, thanks to the high availability of heterogeneous data inside of the company context, those tools are being used more to enhance or enrich data and processes, and make more informed decisions about the busi-ness. The SEMT platform is presented; this system combines a Big Data recollection approach from a legacy/manual sensor environment to per-form a knowledge enhancement process to support the semi-conductor development and production operations inside a cleanroom

Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings

Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a contextual and human-like representation of knowledge. In international economics, KGs have proven valuable in capturing complex interactions between commodities, companies, and countries. By putting the gravity model, which is a common economic framework, into the process of building KGs, important factors that affect trade relationships can be taken into account, making it possible to predict international trade patterns. This paper proposes an approach that leverages Knowledge Graph embeddings for modeling international trade, focusing on link prediction using embeddings. Thus, valuable insights are offered to policymakers, businesses, and economists, enabling them to anticipate the effects of changes in the international trade system. Moreover, the integration of traditional machine learning methods with KG embeddings, such as decision trees and graph neural networks are also explored. The research findings demonstrate the potential for improving prediction accuracy and provide insights into embedding explainability in knowledge representation. The paper also presents a comprehensive analysis of the influence of embedding methods on other intelligent algorithms

Alirocumab and cardiovascular outcomes after acute coronary syndrome

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