Kaiserslautern - Fachbereich Informatik
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This dissertation was developed in the context of the BMBF and EU/ECSEL funded
projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods
of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2.
Goal of the projects is to improve communication and collaborative planning, especially
in early development stages. Besides SysML, the use of agreed vocabularies and on-
tologies for modeling requirements, overall context, variants, and many other items, is
targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning
capabilities.
For this purpose, state-of-the-art ontologies have been investigated and integrated that
entail domains like hardware/software, roadmapping, IoT, context, innovation and oth-
ers. New ontologies have been designed like a HW / SW allocation ontology and a
domain-specific "eFuse ontology" as well as some prototypes. The result is a modular
ontology suite and the GENIAL! Basic Ontology that allows us to model automotive
and microelectronic functions, components, properties and dependencies based on the
ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is
included. These knowledge bases are integrated in a novel tool that allows for collabo-
rative innovation planning and requirements communication along the automotive value
chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial
task, requiring a lot of context and background knowledge. Some of this background
knowledge has been selected for presentation and was utilized either in designing models
or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced
content like multi-level theory, foundational ontologies and reasoning.
Finally, at the end, we demonstrate the overall framework, and show the ontology with
reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip-
tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled
with a car model through a constraint solver.
In recent years, enormous progress has been made in the field of Artificial Intelligence (AI). Especially the introduction of Deep Learning and end-to-end learning, the availability of large datasets and the necessary computational power in form of specialised hardware allowed researchers to build systems with previously unseen performance in areas such as computer vision, machine translation and machine gaming. In parallel, the Semantic Web and its Linked Data movement have published many interlinked RDF datasets, forming the world’s largest, decentralised and publicly available knowledge base.
Despite these scientific successes, all current systems are still narrow AI systems. Each of them is specialised to a specific task and cannot easily be adapted to all other human intelligence tasks, as would be necessary for Artificial General Intelligence (AGI). Furthermore, most of the currently developed systems are not able to learn by making use of freely available knowledge such as provided by the Semantic Web. Autonomous incorporation of new knowledge is however one of the pre-conditions for human-like problem solving.
This work provides a small step towards teaching machines such human-like reasoning on freely available knowledge from the Semantic Web. We investigate how human associations, one of the building blocks of our thinking, can be simulated with Linked Data. The two main results of these investigations are a ground truth dataset of semantic associations and a machine learning algorithm that is able to identify patterns for them in huge knowledge bases.
The ground truth dataset of semantic associations consists of DBpedia entities that are known to be strongly associated by humans. The dataset is published as RDF and can be used for future research.
The developed machine learning algorithm is an evolutionary algorithm that can learn SPARQL queries from a given SPARQL endpoint based on a given list of exemplary source-target entity pairs. The algorithm operates in an end-to-end learning fashion, extracting features in form of graph patterns without the need for human intervention. The learned patterns form a feature space adapted to the given list of examples and can be used to predict target candidates from the SPARQL endpoint for new source nodes. On our semantic association ground truth dataset, our evolutionary graph pattern learner reaches a Recall@10 of > 63 % and an MRR (& MAP) > 43 %, outperforming all baselines. With an achieved Recall@1 of > 34% it even reaches average human top response prediction performance. We also demonstrate how the graph pattern learner can be applied to other interesting areas without modification.