knowledge representation explainible AI
210703572
Neurosymbolic Knowledge Representation for Explainable and Trustworthy AI
AI research and implementations are growing, and so are the risks associated with AI (Artificial Intelligence) developments, especially when it's difficult to understand exactly what they do and how they work, both at a localized level, and at deployment, in particular when distributed and on a large scale. Governments are pouring massive funding to promote AI research and education, yet research results and claims, as well as the effectiveness of educational programmes, can be difficult to evaluate given the limited reproducibility of computations based on ML (machine learning) and poor explainability, which in turn limits the accountability of the systems and can cause cascading systemic problems and challenges including poor reproducibility, reliability, and overall lack of trustworthiness. This paper addresses some of the issues in Knowledge Representation for AI at system level, identifies a number of knowledge gaps and epistemological challenges as root causes of risks and challenges for AI, and proposes that neurosymbolic and hybrid KR approaches can serve as mechanisms to address some of the challenges. The paper concludes with a postulate and points to related and future research
Paola Di Maio
218657654
Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning
Knowledge Graphs for eXplainable Artificial Intelligence
Marjan Alirezaie Martin Langkvist Amy Loutfi
235330013
Knowledge Representation in Artificial Intelligence and Expert Systems Using Inference Rule
Knowledge representation is a very important concept in expert systems and artificial intelligence (AI) in general. It involves the consideration of intelligent (expert) systems and how it presents knowledge. It is best understood in term of the roles it plays based on the task at hand. A knowledge representation involves reasoning about the world rather than taking action in it. It is a set of rules, i.e. an answer to the question and a medium for efficient computation, that is, the computational environment in which thinking is accomplished. In this paper, we discussed knowledge representation using inference rule and forward chaining. The paper demonstrates the use of inference rule in explaining forward chaining using an admission process using some premises or antecedents to derive the conclusion. Some propositions or atomic sentences consisting of logical operators AND and OR are also used to infer conclusions based on some truth of additional proposition symbols.
216233333
Real World Knowledge Representation and Reasoning
Apart from its theoretical significance, the AI must represent real world knowledge, and produce reasoning using that. The real world things are collections of entities in different classes. This chapter presents the representations structures for such knowledge, e.g. taxonomies and reasoning based on that. Other phenomena in real world, that are presented are, action and change, commonsense reasoning, ontology structures for different domains, like, language, and world. The Sowa's ontology for objects, and processes, both concrete and abstract, is explained. The situation calculus is presented in its formal details, along with worked exercises. The more prevalent real world reasoning, like nonmonotonic and default reasoning are also treated in sufficient details, along with supporting worked exercises. This is followed, with summary of the chapter, and exhaustive list of practice exercises.
Prof K R Chowdhary
210936790
Knowledge Representation and Reasoning meets Digital Forensics: The COST Action DigForASP (short paper)
Digital Forensics is a branch of criminalistics which deals with the identification, acquisition, preservation, analysis and presentation of the information content of digital devices. In this paper, we briefly describe DigForASP, a COST Action that aims to create a cooperation network for exploring the potential of the application of techniques from the field of Artificial Intelligence, in particular from the area of Knowledge Representation and Reasoning, in the Digital Forensics field, and to foster synergies between these fields. More precisely, in DigForASP the challenge is to address the so called Evidence Analysis phase, where evidence about possible crimes and crimes' perpetrators must be exploited so as to reconstruct possible events, event sequences and scenarios related to a crime. Results from this phase are then made available to the involved stakeholders (law enforcement, investigators, public prosecutors, lawyers and judges) Reliability, explainability and verifiability of the results are therefore crucial.
RCRA/RiCeRcA@AI*IA
Stefania Costantini Francesca A Lisi Raffaele Olivieri
57613254
From Knowledge Representation to Argumentation in AI, Law and Policy Making
Trevor Bench Capon is well recognised as an outstanding figure in Artificial Intelligence and Law, having published extensively over the long course of his career on legal knowledge representation, engineering methods for knowledge based systems, theoretical and applied argumentation, case based reasoning, policy making, reasoning about evidence, and many other related topics. He has deeply influenced many of his colleagues, particularly with his earlier work on principled methods for legal knowledge representation and the engineering of knowledge based systems, and later with the introduction of values in the study of argumentation. This Festschrift is in honour of Bench Capon's work and its seminal influence. The articles contained here by his colleagues extensively review, comment on, and extend Bench Capon's work. As a whole, the volume is a substantive introduction to the main topics and issues to which Bench Capon has contributed so much.
Hendrik Prakken Kenneth Atkinson Adam Zachary Wyner
216867646
Knowledge Graph Embeddings and Explainable AI
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state of the art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.
Knowledge Graphs for eXplainable Artificial Intelligence
Federico Bianchi Gaetano Rossiello Luca Costabello Matteo Palmonari Pasquale Minervini
204832291
On The Role of Knowledge Graphs in Explainable AI
The current hype of Artificial Intelligence (AI) mostly refers to the success of machine learning and its sub domain of deep learning. However, AI is also about other areas, such as Knowledge Representation and Reasoning, or Distributed AI, i.e. areas that need to be combined to reach the level of intelligence initially envisioned in the 1950s. Explainable AI (XAI) now refers to the core backup for industry to apply AI in products at scale, particularly for industries operating with critical systems. This paper reviews XAI not only from a Machine Learning perspective, but also from the other AI research areas, such as AI Planning or Constraint Satisfaction and Search. We expose the XAI challenges of AI fields, their existing approaches, limitations and opportunities for Knowledge Graphs and their underlying technologies.
PROFILES/SEMEX@ISWC
Freddy Lecue
209411881
Representation of Concepts in AI: Towards a Teleological Explanation
The representation of concepts is a lively research activity in several artificial intelligence (AI) areas, such as knowledge representation, machine learning, and natural language processing. So far, many solutions have been proposed adopting different assumptions about the nature of concepts. Each of these solutions has been developed for capturing some specific features and for supporting some specific (artificial) cognitive operations. This paper provides a teleological explanation of the most widely shared approaches in AI to the representation of concepts. The paper aims at providing four main contributions: i) an overview of the mainstream philosophical theories of concepts; ii) a categorization of a wide range of AI solutions inspired by such theories of concepts; iii) the proposal of a method for a comprehensive explanation of the current approaches to concepts in AI based on a teleosemantic perspective; iv) an illustration of how the proposed explanation could constitute a contribution in the context of explainable AI.
JOWO
Mattia Fumagalli Roberta Ferrario
53471426
The Knowledge and Meta Knowledge Representation Framework for Context based Searching for Lyric Writing in Telugu
Representation of knowledge and Meta knowledge plays a key role in context based searching. Recent advances in computational Linguistics and natural processing has brought linguistic formalism closer to the knowledge representation (KR) The techniques used in artificial Intelligence (AI) to represent knowledge, can be used successfully in computational linguistics in representing the grammar and other language and its literary requirements. Other areas where KR can be used in computational linguistics are common sense language, and default reasoning. In this paper the Knowledge representation related to the grammatical and "Chandasu" and "Alankara Sastra" related knowledge used in Telugu literature is explained. The paper discusses what kind of knowledge related information has to be represented, how the subject issues are represented, how they are related to each other and how reasoning is generated from this knowledge, using Meta knowledge related to this knowledge. In context based searching related to Telugu lyric the most important task is selecting the set of right words, the knowledge data base short lists these words, and aids in giving suggestions. Knowledge representation is one of the central and crucial parts for context based searching for word suggestion. The knowledge has to be organized based on purpose it will be used. Based on different types of knowledge forms, each form requires different kinds of representation. The Knowledge Representation models/mechanisms are often based on; logic, rules, Frames, Semantic Nets etc. Different types of knowledge require different kinds of reasoning. For context based searching used for lyric writing, the knowledge related to grammar and chandasu has to be represented for suggesting the right word suitable for the context. The lexical knowledge adds more connotations to the problem. The synonyms, thesaurus, the relationship of a word with other words, parts of speech etc. have to be taken into consideration to this problem of context based search. In this paper, we shall describe the knowledge and Meta knowledge representations done for context based searching.
Ramakantha Rao Chakalakonda Raghu Korrapati