70 research outputs found
Building Ontologies in DAML + OIL
In this article we describe an approach to representing and building ontologies
advocated by the Bioinformatics and Medical Informatics groups at the University
of Manchester. The hand-crafting of ontologies offers an easy and rapid avenue to
delivering ontologies. Experience has shown that such approaches are unsustainable.
Description logic approaches have been shown to offer computational support for
building sound, complete and logically consistent ontologies. A new knowledge
representation language, DAML + OIL, offers a new standard that is able to support
many styles of ontology, from hand-crafted to full logic-based descriptions with
reasoning support. We describe this language, the OilEd editing tool, reasoning
support and a strategy for the language’s use. We finish with a current example,
in the Gene Ontology Next Generation (GONG) project, that uses DAML + OIL as
the basis for moving the Gene Ontology from its current hand-crafted, form to one
that uses logical descriptions of a concept’s properties to deliver a more complete
version of the ontology
Онтології у контексті інтеграції інформації: представлення, методи та інструменти побудови
У даному огляді розглядається використання онтологій для підтримки задач інтеграції в
семантично гетерогенних інформаційних системах. Представлені основні поняття та визначення
онтологій, причини та приклади побудови. Розглядаються моделі і мови для представлення онтологій
та використання відображень між ними. Досліджуються методи й інструментальні засоби для інженерії
онтологій і підтримки інтеграції інформації. Також наводяться приклади візуалізації, побудови та
злиття двох онтологій за допомогою сучасних інструментів
The DLV System for Knowledge Representation and Reasoning
This paper presents the DLV system, which is widely considered the
state-of-the-art implementation of disjunctive logic programming, and addresses
several aspects. As for problem solving, we provide a formal definition of its
kernel language, function-free disjunctive logic programs (also known as
disjunctive datalog), extended by weak constraints, which are a powerful tool
to express optimization problems. We then illustrate the usage of DLV as a tool
for knowledge representation and reasoning, describing a new declarative
programming methodology which allows one to encode complex problems (up to
-complete problems) in a declarative fashion. On the foundational
side, we provide a detailed analysis of the computational complexity of the
language of DLV, and by deriving new complexity results we chart a complete
picture of the complexity of this language and important fragments thereof.
Furthermore, we illustrate the general architecture of the DLV system which
has been influenced by these results. As for applications, we overview
application front-ends which have been developed on top of DLV to solve
specific knowledge representation tasks, and we briefly describe the main
international projects investigating the potential of the system for industrial
exploitation. Finally, we report about thorough experimentation and
benchmarking, which has been carried out to assess the efficiency of the
system. The experimental results confirm the solidity of DLV and highlight its
potential for emerging application areas like knowledge management and
information integration.Comment: 56 pages, 9 figures, 6 table
Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn
AbstractBio-ontology development is a resource-consuming task despite the many open source ontologies available for reuse. Various strategies and tools for bottom-up ontology development have been proposed from a computing angle, yet the most obvious one from a domain expert perspective is unexplored: the abundant diagrams in the sciences. To speed up and simplify bio-ontology development, we propose a detailed, micro-level, procedure, DiDOn, to formalise such semi-structured biological diagrams availing also of a foundational ontology for more precise and interoperable subject domain semantics. The approach is illustrated using Pathway Studio as case study
Ontology evolution: a process-centric survey
Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage
Working notes of the KI \u2796 Workshop on Agent Oriented Programming and Distributed Systems
Agent-oriented techniques are likely to be the next significant breakthrough in software development process. They provide a uniform approach throughout the analysis, design and implementation phases in the development life cycle. Agent-oriented techniques are a natural extension to object-oriented techniques, but while there is a whole pIethora of analysis and design methods in the object-oriented paradigm, very little work has been reported on design and analysis methods in the agent-oriented community. After surveying and examining a number of well-known object-oriented design and analysis methods, we argue that none of these methods, provide the adequate model for the design and analysis of multi-agent systems. Therefore, we propose a new agent-specific methodology that is based on and builds upon object-oriented methods. We identify three major models that need to be build during the development of multi-agent applications and describe the process of building these models
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
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