scholarly journals Extensible Knowledge Representation: the Case of Description Reasoners

1999 ◽  
Vol 10 ◽  
pp. 399-434 ◽  
Author(s):  
A. Borgida
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Software Architecture ◽  
Description Logics ◽  
Knowledge Representation And Reasoning ◽  
Heuristic Methodology

This paper offers an approach to extensible knowledge representation and reasoning for a family of formalisms known as Description Logics. The approach is based on the notion of adding new concept constructors, and includes a heuristic methodology for specifying the desired extensions, as well as a modularized software architecture that supports implementing extensions. The architecture detailed here falls in the normalize-compared paradigm, and supports both intentional reasoning (subsumption) involving concepts, and extensional reasoning involving individuals after incremental updates to the knowledge base. The resulting approach can be used to extend the reasoner with specialized notions that are motivated by specific problems or application areas, such as reasoning about dates, plans, etc. In addition, it provides an opportunity to implement constructors that are not currently yet sufficiently well understood theoretically, but are needed in practice. Also, for constructors that are provably hard to reason with (e.g., ones whose presence would lead to undecidability), it allows the implementation of incomplete reasoners where the incompleteness is tailored to be acceptable for the application at hand.

scholarly journals A Framework for Reasoning about Dynamic Axioms in Description Logics

2020 ◽  
Author(s):  
Bartosz Bednarczyk ◽  
Stephane Demri ◽  
Alessio Mansutti
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Data Structures ◽  
Description Logics ◽  
Knowledge Bases ◽  
Dynamic Data Structures ◽  
Dynamic Data ◽  
Logical Connectives ◽  
Consistency Problem ◽  
Natural Means

Description logics are well-known logical formalisms for knowledge representation. We propose to enrich knowledge bases (KBs) with dynamic axioms that specify how the satisfaction of statements from the KBs evolves when the interpretation is decomposed or recomposed, providing a natural means to predict the evolution of interpretations. Our dynamic axioms borrow logical connectives from separation logics, well-known specification languages to verify programs with dynamic data structures. In the paper, we focus on ALC and EL augmented with dynamic axioms, or to their subclass of positive dynamic axioms. The knowledge base consistency problem in the presence of dynamic axioms is investigated, leading to interesting complexity results, among which the problem for EL with positive dynamic axioms is tractable, whereas EL with dynamic axioms is undecidable.

scholarly journals Formal Ontologies in Biomedical Knowledge Representation

2013 ◽  
Vol 22 (01) ◽  
pp. 132-146 ◽  
Author(s):  
L. Jansen ◽  
S. Schulz
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Description Logics ◽  
Clinical Decision Support Systems ◽  
Knowledge Bases ◽  
Medical Decision ◽  
Knowledge Representation And Reasoning ◽  
Digital Information ◽  
Biomedical Knowledge ◽  
Formal Ontologies

Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.

scholarly journals Reasoning with Very Expressive Fuzzy Description Logics

2007 ◽  
Vol 30 ◽  
pp. 273-320 ◽  
Author(s):  
G. Stoilos ◽  
G. Stamou ◽  
J. Z. Pan ◽  
V. Tzouvaras ◽  
I. Horrocks
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
High Performance ◽  
Description Logics ◽  
Decision Procedures ◽  
Satisfiability Problem ◽  
Fuzzy Description Logics ◽  
Knowledge Based ◽  
Fuzzy Description

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN.

Common-sense Knowledge Representation and Reasoning, and its Application to Face Detection

1998 ◽  
Vol 2 (3) ◽  
pp. 96-103 ◽  
Author(s):  
Abbas Z. Kouzani ◽  
◽  
Fangpo He ◽  
Karl Sammut
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Face Detection ◽  
Common Sense ◽  
Detection System ◽  
Basic Element ◽  
Knowledge Representation And Reasoning ◽  
Final Decision ◽  
Connectionist Model ◽  
Common Sense Knowledge

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is used as a basic element for the connectionist model. The representation and reasoning ability of the model are described through examples. A common-sense knowledge base is employed to develop a human face detection system. The system consists of three stages: preprocessing, face-components extraction, and final decision making. A neural-network-based algorithm is utilised to extract face components. Five networks are trained to detect the mouth, nose, eyes, and full face. The detected face components and their corresponding possibility degrees enable the knowledge base to locate faces in the image and to generate a membership degree for the detected faces within the face class. The experimental results obtained using this method are presented.

Project Halo Update—Progress Toward Digital Aristotle

AI Magazine ◽  
2010 ◽  
Vol 31 (3) ◽  
pp. 33 ◽  
Author(s):  
David Gunning ◽  
Vinay K. Chaudhri ◽  
Peter E. Clark ◽  
Ken Barker ◽  
Shaw-Yi Chaw ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Advanced Placement ◽  
Language Processing ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Domain Experts ◽  
Knowledge Based ◽  
Novice Users ◽  
Applied Knowledge

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

Acquiring Knowledge in Extended Hierarchical Censored Production Rules (EHCPRS) System

2010 ◽  
Vol 1 (4) ◽  
pp. 10-28 ◽  
Author(s):  
Sarika Jain ◽  
N.K. Jain
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Common Sense ◽  
Knowledge Structure ◽  
Knowledge Representation And Reasoning ◽  
Production Rules ◽  
Proper Place ◽  
Continuous Growth ◽  
Reasoning System ◽  
Common Sense Knowledge

EHCPRs system is a knowledge representation and reasoning system for representing common sense knowledge and reasoning with it. In such a system an EHCPR is used as a unit of knowledge for representing any universal concept. There are a number of EHCPRs at various levels of hierarchy of knowledge structure in the EHCPRs system, which results in a tree of EHCPRs. This EHCPRs tree has the capability of continuous growth through new added EHCPRs to it at proper place as well as to get refined continuously with time through improvement in the already acquired EHCPRs. The EHCPRs tree will become stronger in terms of strength of implication and richer in knowledge as time passes. This paper discusses different schemes for enhancing the intelligence, i.e., the knowledge base and the database in the EHCPRs system. By simple and general snippets of code, the EHCPRs system is able to acquire new pieces of knowledge and assimilate it properly in the already acquired knowledge base. The EHCPRs system dynamically restructures the EHCPRs tree in each learning phase by maintaining consistency and minimizing redundancy as well.

scholarly journals If Nothing Is Accepted -- Repairing Argumentation Frameworks

2019 ◽  
Vol 66 ◽  
pp. 1099-1145
Author(s):  
Markus Ulbricht ◽  
Ringo Baumann
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Search Space ◽  
Knowledge Representation And Reasoning ◽  
Hitting Set ◽  
Abstract Argumentation ◽  
Conflicting Information ◽  
All Solutions ◽  
First Results ◽  
Grounded Semantics

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.

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