A Mixed Initiative Semantic Web Framework for Process Composition

• URL: http://arxiv.org/abs/2006.02168v1
• Date: Wed, 3 Jun 2020 11:02:31 GMT
• Title: A Mixed Initiative Semantic Web Framework for Process Composition
• Authors: Jinghai Rao and Dimitar Dimitrov and Paul Hofmann and Norman Sadeh
• Abstract summary: We argue that the assumption that all functionality has already been encapsulated in the form of semantic web services is often unrealistic. We describe a mixed initiative framework for semantic web service discovery and composition that aims at flexibly interleaving human decision making and automated functionality.
• Score: 3.601465722798586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic Web technologies offer the prospect of significantly reducing the amount of effort required to integrate existing enterprise functionality in support of new composite processes; whether within a given organization or across multiple ones. A significant body of work in this area has aimed to fully automate this process, while assuming that all functionality has already been encapsulated in the form of semantic web services with rich and accurate annotations. In this article, we argue that this assumption is often unrealistic. Instead, we describe a mixed initiative framework for semantic web service discovery and composition that aims at flexibly interleaving human decision making and automated functionality in environments where annotations may be incomplete and even inconsistent.

Related papers

• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Unified Language-driven Zero-shot Domain Adaptation [55.64088594551629]
  Unified Language-driven Zero-shot Domain Adaptation (ULDA) is a novel task setting. It enables a single model to adapt to diverse target domains without explicit domain-ID knowledge.
  arXiv  Detail & Related papers  (2024-04-10T16:44:11Z)
• On the Multi-turn Instruction Following for Conversational Web Agents [83.51251174629084]
  We introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment. We propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques.
  arXiv  Detail & Related papers  (2024-02-23T02:18:12Z)
• Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning [68.63380306259742]
  Despite its promising potential, semantic communications and semantic-aware networking are still at their infancy. We propose a novel reasoning-based implicit semantic-aware communication network architecture that allows multiple tiers of CDC and edge servers to collaborate. We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users.
  arXiv  Detail & Related papers  (2022-10-28T13:26:08Z)
• Intelligent Software Web Agents: A Gap Analysis [0.0]
  We examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components. We propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards, and point to existing work in the broader semantic web community any beyond that could help us to make the semantic web agent vision a reality.
  arXiv  Detail & Related papers  (2021-02-12T16:32:02Z)
• Interactive Fusion of Multi-level Features for Compositional Activity Recognition [100.75045558068874]
  We present a novel framework that accomplishes this goal by interactive fusion. We implement the framework in three steps, namely, positional-to-appearance feature extraction, semantic feature interaction, and semantic-to-positional prediction. We evaluate our approach on two action recognition datasets, Something-Something and Charades.
  arXiv  Detail & Related papers  (2020-12-10T14:17:18Z)
• Using Semantic Web Services for AI-Based Research in Industry 4.0 [0.8164433158925591]
  We present semantic web services for AI-based research in Industry 4.0. We developed more than 300 semantic web services for a physical simulation factory based on Web Ontology Language for Web Services and Web Service Modeling Ontology. We evaluate our implementation by executing a cyber-physical workflow composed of semantic web services.
  arXiv  Detail & Related papers  (2020-07-07T15:58:10Z)
• A Cloud Computing Capability Model for Large-Scale Semantic Annotation [0.0]
  This paper focuses on leveraging cloud computing for web content semantic annotation challenges. A set of requirements is defined and mapped with cloud computing mechanisms to facilitate them. The paper presents an approach towards delivering large scale semantic annotation on the web via a cloud platform.
  arXiv  Detail & Related papers  (2020-06-24T17:23:06Z)
• Semantic Web Environments for Multi-Agent Systems: Enabling agents to use Web of Things via semantic web [6.85316573653194]
  Multi-agent system (MAS) technologies are the right abstraction for developing decentralized and open Web applications. The aim of the project is to transform "Agent and propose an approach to transform "Agent and A&A" into a Web-readable format with in line web formats.
  arXiv  Detail & Related papers  (2020-02-20T11:18:29Z)
• Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images [91.41909587856104]
  We present a Panoptic Feature Fusion Net (PFFNet) that unifies the semantic and instance features in this work. Our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features. It outperforms several state-of-the-art methods on various biomedical and biological datasets.
  arXiv  Detail & Related papers  (2020-02-15T09:19:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.