An Ontology for Defect Detection in Metal Additive Manufacturing

A key challenge for Industry 4.0 applications is to develop control systems for automated manufacturing services that are capable of addressing both data integration and semantic interoperability issues, as well as monitoring and decision making tasks. To address such an issue in advanced manufacturing systems, principled knowledge representation approaches based on formal ontologies have been proposed as a foundation to information management and maintenance in presence of heterogeneous data sources. In addition, ontologies provide reasoning and querying capabilities to aid domain experts and end users in the context of constraint validation and decision making. Finally, ontology-based approaches to advanced manufacturing services can support the explainability and interpretability of the behaviour of monitoring, control, and simulation systems that are based on black-box machine learning algorithms. In this work, we provide a novel ontology for the classification of process-induced defects known from the metal additive manufacturing literature. Together with a formal representation of the characterising features and sources of defects, we integrate our knowledge base with state-of-the-art ontologies in the field. Our knowledge base aims at enhancing the modelling capabilities of additive manufacturing ontologies by adding further defect analysis terminology and diagnostic inference features

Transition Constraints for Temporal Attributes

Representing temporal data in conceptual data models and ontologies is required by various application domains. For it to be useful for modellers to represent the information precisely and reason over it, it is essential to have a language that is expressive enough to capture the required operational semantics of the time-varying information. Temporal modelling languages have little support for temporal attributes, if at all, yet attributes are a standard element in the widely used conceptual modelling languages such as EER and UML. This hiatus prevents one to utilise a complete temporal conceptual data model and keep track of evolving values of data and its interaction with temporal classes. A rich axiomatisation of fully temporised attributes is possible with a minor extension to the already very expressive description logic language DLRUS. We formalise the notion of transition of attributes, and their interaction with transition of classes. The transition specified for attributes are extension, evolution, and arbitrary quantitative extension

Two-dimensional rule language for querying sensor log data: a framework and use cases

Motivated by two industrial use cases that involve detecting events of interest in (asynchronous) time series from sensors in manufacturing rigs and gas turbines, we design an expressive rule language DslD equipped with interval aggregate functions (such as weighted average over a time interval), Allen’s interval relations and various metric constructs. We demonstrate how to model events in the uses cases in terms of DslD programs. We show that answering DslD queries in our use cases can be reduced to evaluating SQL queries. Our experiments with the use cases, carried out on the Apache Spark system, show that such SQL queries scale well on large real-world datasets

Developing Ontology-based Data access techniques for temporal data

Η παρούσα πτυχιακή εργασία αφορά τη επέκταση του συστήματος Ontop-spatial για την υποστήριξη χρονικών δεδομένων (temporal data). Το Ontop-Spatial είναι ένα σύστημα το οποίο υποστηρίζει την επεξεργασία γεωχωρικών επερωτήσεων στη γλώσσα GeoSPARQL, επαναγράφοντάς τις στη γλώσσα SQL ώστε να αποττιμηθούν τελικά σε μια γεωχωρική βάση δεδομένων που είναι συνδεδεμένη με το Ontop-spatial. Με παρόμοια τεχνική, χρησιμοποιώντας οντολογίες και αντιστοιχήσεις δεδομένων από το σχεσιακό μοντέλο στο μοντέλο RDF γίνεται δυνατή η μετάφραση σε πραγματικό χρόνο και των χρονικών επερωτήσεων που εκφράζονται στη γλώσσα stSPARQL στα αντίστοιχα SQL ερωτήματα τα οποία μπορούν να αποτιμηθούν σε μια χρονική βάση δεδομένων. Η γλώσσα stSPARQL είναι μια επέκταση της γλώσσας SPARQL με χρονικά και γεωχωρικά χαρακτηριστικά. Στην παρούσα πτυχιακή περιγράφουμε την προσέγγισή μας και διεξάγουμε πειραματική μελέτη για να αξιολογήσουμε την απόδοση της υλοποίησης.We propose an additional enhancement on top of the already extended Ontop's SPARQL-to-SQL translation, which supports geospatial data, with the addition of stSPARQL-to-SQL translation regarding temporal data. Ontop is a mature, open-source Ontology-Based Data Access (OBDA) system that allows posing SPARQL queries on top of relational data sources through provided ontologies and mappings. The system Ontop-spatial is an extension of the system Ontop that performs on-the-fly GeoSPARQL-to-SQL translation on top of geospatial enabled databases. GeoSPARQL is a spatial extension of the SPARQL query language and has been standardized by OGC. In this thesis, we extend the system Ontop-spatial with the ability to execute temporal queries as well, on top of temporal enabled databases

A cookbook for temporal conceptual data modelling with description logic

We design temporal description logics suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models

Ontology-based access to temporal data with Ontop: a framework proposal

Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop

Temporalised Description Logics for Monitoring Partially Observable Events

Inevitably, it becomes more and more important to verify that the systems surrounding us have certain properties. This is indeed unavoidable for safety-critical systems such as power plants and intensive-care units. We refer to the term system in a broad sense: it may be man-made (e.g. a computer system) or natural (e.g. a patient in an intensive-care unit). Whereas in Model Checking it is assumed that one has complete knowledge about the functioning of the system, we consider an open-world scenario and assume that we can only observe the behaviour of the actual running system by sensors. Such an abstract sensor could sense e.g. the blood pressure of a patient or the air traffic observed by radar. Then the observed data are preprocessed appropriately and stored in a fact base. Based on the data available in the fact base, situation-awareness tools are supposed to help the user to detect certain situations that require intervention by an expert. Such situations could be that the heart-rate of a patient is rather high while the blood pressure is low, or that a collision of two aeroplanes is about to happen. Moreover, the information in the fact base can be used by monitors to verify that the system has certain properties. It is not realistic, however, to assume that the sensors always yield a complete description of the current state of the observed system. Thus, it makes sense to assume that information that is not present in the fact base is unknown rather than false. Moreover, very often one has some knowledge about the functioning of the system. This background knowledge can be used to draw conclusions about the possible future behaviour of the system. Employing description logics (DLs) is one way to deal with these requirements. In this thesis, we tackle the sketched problem in three different contexts: (i) runtime verification using a temporalised DL, (ii) temporalised query entailment, and (iii) verification in DL-based action formalisms