Recent machine learning approaches have been effective in Artificial Intelligence (AI) applications. They produce robust results with a high level of accuracy. However, most of these techniques do not provide human-understandable explanations for supporting their results and decisions. They usually act as black boxes, and it is not easy to understand how decisions have been made. Explainable Artificial Intelligence (XAI), which has received much interest recently, tries to provide human-understandable explanations for decision-making and trained AI models. For instance, in digital agriculture, related domains often present peculiar or input features with no link to background knowledge. The application of the data mining process on agricultural data leads to results (knowledge), which are difficult to explain. In this paper, we propose a knowledge map model and an ontology design as an XAI framework (OAK4XAI) to deal with this issue. The framework does not only consider the data analysis part of the process, but it takes into account the semantics aspect of the domain knowledge via an ontology and a knowledge map model, provided as modules of the framework. Many ongoing XAI studies aim to provide accurate and verbalizable accounts for how given feature values contribute to model decisions. The proposed approach, however, focuses on providing consistent information and definitions of concepts, algorithms, and values involved in the data mining models. We built an Agriculture Computing Ontology (AgriComO) to explain the knowledge mined in agriculture. AgriComO has a well-designed structure and includes a wide range of concepts and transformations suitable for agriculture and computing domains.

In this context, additive manufacturing (AM), and specifically metal additive manufacturing (MAM), is particularly suited to industrial paradigms based on automation, flexibility, and efficiency. Indeed, MAM can be considered as a native digital technology, providing a seamless workflow from the digital design environment to the final product, which can be potentially completed without any human intervention [30]. However, a broader adoption of MAM technologies in industry is still hindered by such factors as: (i) lack of widely adopted standardisations and specifications of material properties, machines, and processes [40]; (ii) lack of adequate digital infrastructures, and interoperability issues between different production environments [7]; (iii) lack of accessible interfaces providing process information that is easily interpretable by non-experts [47]; (iv) lack of advanced control systems capable of automatically adjusting, at run-time, the production parameters [54]; (v) challenges in quality assurance due part accuracy and variability [48]. Thus, achieving semantically transparent and interoperable data sets and systems, to address Points (i), (ii) and (iii) above, is arguably of paramount importance. In this direction, several approaches based on ontology engineering and knowledge representation techniques have been proposed [29, 10, 66, 67, 60]. Broadly conceived as formal specifications of conceptualisations over a domain of interest, computational ontologies (cf.

More and more, with the growing focus on large scale analytics, we are confronted with the need of integrating data from multiple sources. The problem is that these data are impossible to reuse as-is. The net result is high cost, with the further drawback that the resulting integrated data will again be hardly reusable as-is.

We formalise some aspects of the neural-symbol design patterns of van Bekkum et al., such that we can formally define notions of refinement of patterns, as well as modular combination of larger patterns from smaller building blocks. These formal notions have been implemented in the heterogeneous tool set (Hets), such that patterns and refinements can be checked for well-formedness, and combinations can be computed.

DatalogMTL is an extension of Datalog with metric temporal operators that has found applications in temporal ontology-based data access and query answering, as well as in stream reasoning. Practical algorithms for DatalogMTL are reliant on materialisation-based reasoning, where temporal facts are derived in a forward chaining manner in successive rounds of rule applications. Current materialisation-based procedures are, however, based on a naive evaluation strategy, where the main source of inefficiency stems from redundant computations. In this paper, we propose a materialisation-based procedure which, analogously to the classical seminaive algorithm in Datalog, aims at minimising redundant computation by ensuring that each temporal rule instance is considered at most once during the execution of the algorithm. Our experiments show that our optimised seminaive strategy for DatalogMTL is able to significantly reduce materialisation times.

Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph. In this paper, we show that it can also be used to explain data differentials, for example in the context of Explainable AI (XAI), and we show that it can in fact be done in a way that is meaningful to a human observer.

Background: Access to medical care is strongly dependent on resource allocation, such as the geographical distribution of medical facilities. Nevertheless, this data is usually restricted to country official documentation, not available to the public. While some medical facilities' data is accessible as semantic resources on the Web, it is not consistent in its modeling and has yet to be integrated into a complete, open, and specialized repository. This work focuses on generating a comprehensive semantic dataset of medical facilities worldwide containing extensive information about such facilities' geo-location. Results: For this purpose, we collect, align, and link various open-source databases where medical facilities' information may be present. This work allows us to evaluate each data source along various dimensions, such as completeness, correctness, and interlinking with other sources, all critical aspects of current knowledge representation technologies. Conclusions: Our contributions directly benefit stakeholders in the biomedical and health domain (patients, healthcare professionals, companies, regulatory authorities, and researchers), who will now have a better overview of the access to and distribution of medical facilities.

Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical natural products are co-consumed with pharmaceutical drugs. Understanding mechanisms of NPDIs is key to preventing adverse events. We constructed a knowledge graph framework, NP-KG, as a step toward computational discovery of pharmacokinetic NPDIs. NP-KG is a heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature, constructed with the Phenotype Knowledge Translator framework and the semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through path searches and meta-path discovery to determine congruent and contradictory information compared to ground truth data. The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature. NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify pharmacokinetic interactions involving enzymes, transporters, and pharmaceutical drugs. We envision that NP-KG will facilitate improved human-machine collaboration to guide researchers in future studies of pharmacokinetic NPDIs. The NP-KG framework is publicly available at https://doi.org/10.5281/zenodo.6814507 and https://github.com/sanyabt/np-kg.

Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results.

Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings.