An approach with multi-tier automotive knowledge formalization for troubleshooting activities

Automotive troubleshooting process integrates repairing activities that are executed through auto professionals when they note phenomenon or conditions and determine about inspections, instructions, or checks, so as to tackle the trouble that affects a car. This study is focused on the knowledge representation for the aim of decision making in automotive troubleshooting process for automotive braking system. To reach this purpose, there are three phases followed: (1) a knowledge representation with procedural mode is investigated from an aspect of decision making; (2) a simple, instinctive, and efficient architecture of automotive knowledge formalization is presented; (3) an approach to generate troubleshooting procedures is defined. A new form, named diagram of expanded transformation (DoET), to represent knowledge and depict three fundamental tiers of decision making in the present or future disposal: processing strategy, quantity, and inapplicability. The approach can be also utilized manually to create DoETs from auto repair manuals (ARMs) or to build them spontaneously applying the messages feasible on workshop lists regarding single, multi-tier troubleshooting processes. The DoETs with auto repair manuals for auto braking system is validated. The acquired model can be utilized as a base structure for troubleshooting assisted systems generation.

Biomedical relation extraction via knowledge-enhanced reading comprehension

Background In biomedical research, chemical and disease relation extraction from unstructured biomedical literature is an essential task. Effective context understanding and knowledge integration are two main research problems in this task. Most work of relation extraction focuses on classification for entity mention pairs. Inspired by the effectiveness of machine reading comprehension (RC) in the respect of context understanding, solving biomedical relation extraction with the RC framework at both intra-sentential and inter-sentential levels is a new topic worthy to be explored. Except for the unstructured biomedical text, many structured knowledge bases (KBs) provide valuable guidance for biomedical relation extraction. Utilizing knowledge in the RC framework is also worthy to be investigated. We propose a knowledge-enhanced reading comprehension (KRC) framework to leverage reading comprehension and prior knowledge for biomedical relation extraction. First, we generate questions for each relation, which reformulates the relation extraction task to a question answering task. Second, based on the RC framework, we integrate knowledge representation through an efficient knowledge-enhanced attention interaction mechanism to guide the biomedical relation extraction. Results The proposed model was evaluated on the BioCreative V CDR dataset and CHR dataset. Experiments show that our model achieved a competitive document-level F1 of 71.18% and 93.3%, respectively, compared with other methods. Conclusion Result analysis reveals that open-domain reading comprehension data and knowledge representation can help improve biomedical relation extraction in our proposed KRC framework. Our work can encourage more research on bridging reading comprehension and biomedical relation extraction and promote the biomedical relation extraction.

Formalization and Knowledge Representation in Advanced Engineering Informatics

The primary goal of this article in the research area of Advanced Engineering Informatics (AEIs) is to depict and formalize engineering knowledge that is multidimensional. This paper introduces conceptual framework and rationality as implicit methodologies to regularize knowledge. The objective of professionals, as well as the circumstances in which they work, should be considered when depicting and standardizing knowledge. The constructs of epistemology, rationality, and context are used to communicate various alternative data analysis techniques and practices that expert can use to institutionalize intricate engineering expertise and to substantiate whether a specialized conceptual model can support engineers with their challenging operations. A bottom-up method of research in advanced engineering, encompassing engineers, is suggested in this article. A social scientific approach to engendering knowledge for formalization and validating it is also recommended by us for scientists.

Knowledge representation for explainable artificial intelligence

Alongside the particular need to explain the behavior of black box artificial intelligence (AI) systems, there is a general need to explain the behavior of any type of AI-based system (the explainable AI, XAI) or complex system that integrates this type of technology, due to the importance of its economic, political or industrial rights impact. The unstoppable development of AI-based applications in sensitive areas has led to what could be seen, from a formal and philosophical point of view, as some sort of crisis in the foundations, for which it is necessary both to provide models of the fundamentals of explainability as well as to discuss the advantages and disadvantages of different proposals. The need for foundations is also linked to the permanent challenge that the notion of explainability represents in Philosophy of Science. The paper aims to elaborate a general theoretical framework to discuss foundational characteristics of explaining, as well as how solutions (events) would be justified (explained). The approach, epistemological in nature, is based on the phenomenological-based approach to complex systems reconstruction (which encompasses complex AI-based systems). The formalized perspective is close to ideas from argumentation and induction (as learning). The soundness and limitations of the approach are addressed from Knowledge representation and reasoning paradigm and, in particular, from Computational Logic point of view. With regard to the latter, the proposal is intertwined with several related notions of explanation coming from the Philosophy of Science.