2,527 research outputs found
RaDON - Repair and Diagnosis in Ontology Networks
One of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users
Logic, self-awareness and self-improvement: The metacognitive loop and the problem of brittleness
This essay describes a general approach to building perturbation-tolerant autonomous systems, based on the conviction that artificial agents should be able notice when something is amiss, assess the anomaly, and guide a solution into place. We call this basic strategy of self-guided learning the metacognitive loop; it involves the system monitoring, reasoning about, and, when necessary, altering its own decision-making components. In this essay, we (a) argue that equipping agents with a metacognitive loop can help to overcome the brittleness problem, (b) detail the metacognitive loop and its relation to our ongoing work on time-sensitive commonsense reasoning, (c) describe specific, implemented systems whose perturbation tolerance was improved by adding a metacognitive loop, and (d) outline both short-term and long-term research agendas
Completing and Debugging Ontologies: state of the art and challenges
As semantically-enabled applications require high-quality ontologies,
developing and maintaining ontologies that are as correct and complete as
possible is an important although difficult task in ontology engineering. A key
step is ontology debugging and completion. In general, there are two steps:
detecting defects and repairing defects. In this paper we discuss the state of
the art regarding the repairing step. We do this by formalizing the repairing
step as an abduction problem and situating the state of the art with respect to
this framework. We show that there are still many open research problems and
show opportunities for further work and advancing the field.Comment: 56 page
Contextualized Structural Self-supervised Learning for Ontology Matching
Ontology matching (OM) entails the identification of semantic relationships
between concepts within two or more knowledge graphs (KGs) and serves as a
critical step in integrating KGs from various sources. Recent advancements in
deep OM models have harnessed the power of transformer-based language models
and the advantages of knowledge graph embedding. Nevertheless, these OM models
still face persistent challenges, such as a lack of reference alignments,
runtime latency, and unexplored different graph structures within an end-to-end
framework. In this study, we introduce a novel self-supervised learning OM
framework with input ontologies, called LaKERMap. This framework capitalizes on
the contextual and structural information of concepts by integrating implicit
knowledge into transformers. Specifically, we aim to capture multiple
structural contexts, encompassing both local and global interactions, by
employing distinct training objectives. To assess our methods, we utilize the
Bio-ML datasets and tasks. The findings from our innovative approach reveal
that LaKERMap surpasses state-of-the-art systems in terms of alignment quality
and inference time. Our models and codes are available here:
https://github.com/ellenzhuwang/lakermap
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