1,720,970 research outputs found
Evolving ontologies with online learning and forgetting algorithms
Full text linkAgents that require vocabularies to complete tasks can be limited by static vocabularies which cannot evolve to meet unforeseen domain tasks, or reflect its changing needs or environment. However, agents can benefit from using evolution algorithms to evolve their vocabularies, namely the ability to support new domain tasks. While an agent can capitalise on being able support more domain tasks, using existing techniques can hinder them because they do not consider the associated costs involved with evolving an agent's ontology. With this motivation, we explore the area of ontology evolution in agent systems, and focus on the reduction of the costs associated with an evolving ontology.In more detail, we consider how an agent can reduce the costs of evolving an ontology, these include costs associated with: the acquisition of new concepts; processing new concepts; the increased memory usage from storing new concepts; and the removal of unnecessary concepts. Previous work reported in the literature has largely failed to analyse these costs in the context of evolving an agent's ontology. Against this background, we investigate and develop algorithms to enable agents to evolve their ontologies.More specifically, we present three online evolution algorithms that enable agents to: i) augment domain related concepts, ii) use prediction to select concepts to learn, and iii) prune unnecessary concepts from their ontology, with the aim to reduce the costs associated with the acquisition, processing and storage of acquired concepts. In order to evaluate our evolution algorithms, we developed an agent framework which enables agents to use these algorithms and measure an agent's performance. Finally, our empirical evaluation shows that our algorithms are successful in reducing the costs associated with evolving an agent's ontology
Evolving Ontological Knowledge Bases through Agent Collaboration
No full textThis paper presents initial work that will enable an agent to augment its ontology to incorporate required knowledge from other agents, in order to let it answer domain related queries. Specifically, our agents are heterogeneous, whereby an agent has its own interest domain and represents this with an ontology that contains relevant conceptualisations. These agents have intersecting domain interests and their ontologies represent a set of overlapping concepts with alternative symbolic representations. In this setting, our proposed approach focuses on reducing the costs associated with acquiring knowledge through collaboration, and augmenting axioms into an agent’s ontology. In order to achieve this, we consider incorporating knowledge to reduce the number of messages required to answer repetitive domain related queries that require mediation, and select a shared set of axioms that represent conceptual knowledge. We present results from our approach and identify the number of messages and axioms required for a repeated transaction. These preliminary results show that augmenting an agent’s ontology can indeed reduce the number of messages and axioms required
Generating narratives from provenance relationship chains
No full textProvenance data is a rich data structured source that has a similar role to narratives, since they can both provide an account of connected events. Consuming prov data can be hard for both technical and non-technical users, because of its potential scale and the complexity of the relationships captured. Explicitly, it can be hard for users to follow and understand the chain of relationships connecting elements together. In this paper, we present an approach that generates narratives explaining chains of relationships and describe its nature with examples from a Ride Share applicatio
Ontology evolution through agent collaboration
No full textWe present a technique that enables a software agent to augment its ontology with domain related concepts by collaborating with other agents. The collaborating agents have their own individual ontologies, they can share concepts and relationships that relate to a requested specific concept (which is known as a fragment). Thus, specifically, our technique selects the fragments that will be shared. This approach enables agents to answer queries with more range and detail, and it also enables an agent to infer new exploitable knowledge. Without this capability, an agent may be limited by its domain model, and cannot reflect changes in the environment. Through empirical evaluation, we show that our technique reduces the cost of acquiring concepts that are regularly used (compared with learning nothing) and reduces the complexity of the agent's ontology by augmenting it with selected concepts and relationships which are related to its domain (compared with learning everything)
Collaborative Learning of Ontology Fragments by Cooperating Agents
Full text linkCollaborating agents require either prior agreement on the shared vocabularies that they use for communication, or some means of translating between their private ontologies. Thus, techniques that enable agents to build shared vocabularies allow them to share and learn new concepts, and are therefore beneficial when these concepts are required on multiple occasions. However, if this is not carried out in an effective manner then the performance of an agent may be adversely affected by the time required to infer over large augmented ontologies, so causing problems in time-critical scenarios such as search and rescue. In this paper, we present a new technique that enables agents to augment their ontology with carefully selected concepts into their ontology. We contextualise this generic approach in the domain of RoboCup Rescue. Specifically, we show, through empirical evaluation, that our approach saves more civilians, reduces the percentage of the city burnt, and spends the least amount of time accessing its ontology compared with other state of the art benchmark approaches
Forgetting Fragments from Evolving Ontologies
Full text linkOntologies underpin the semantic web; they define the concepts and their relationships contained in a data source. An increasing number of ontologies are available on-line, but an ontology that combines information from many different sources can grow extremely large. As an ontology grows larger, more resources are required to use it, and its response time becomes slower. Thus, we present and evaluate an on-line approach that forgets fragments from an OWL ontology that are infrequently or no longer used, or are cheap to relearn, in terms of time and resources. In order to evaluate our approach, we situate it in a controlled simulation environment, RoboCup OWLRescue, which is an extension of the widely used RoboCup Rescue platform, which enables agents to build ontologies automatically based on the tasks they are required to perform. We benchmark our approach against other comparable techniques and show that agents using our approach spend less time forgetting concepts from their ontology, allowing them to spend more time deliberating their actions, to achieve a higher average score in the simulation environment
An on-line algorithm for semantic forgetting
No full textOntologies that evolve through use to support new domain tasks can grow extremely large. Moreover, large ontologies require more resources to use and have slower response times than small ones. To help address this problem, we present an on-line semantic forgetting algorithm that removes ontology fragments containing infrequently used or cheap to relearn concepts. We situate our algorithm in an extension of the widely used RoboCup Rescue platform, which provides simulated tasks to agents. We show that our agents send fewer messages and complete more tasks, and thus achieve a greater degree of success, than other state-of-the-art approaches
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