Topic
Semantic computing
About: Semantic computing is a research topic. Over the lifetime, 11188 publications have been published within this topic receiving 241331 citations.
Papers published on a yearly basis
1 / 2
Papers
TL;DR: In this article , the authors investigated the spectral efficiency in the semantic domain and rethink the semantic-aware resource allocation issue, and proposed a transform method to convert the conventional bit-based spectral efficiency to the semantic spectral efficiency.
Abstract: Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime. However, resource allocation for semantic communications still remains unexplored, which is a critical issue in guaranteeing the semantic transmission reliability and the communication efficiency. To fill this gap, we investigate the spectral efficiency in the semantic domain and rethink the semantic-aware resource allocation issue. Specifically, taking text semantic communication as an example, the semantic spectral efficiency (S-SE) is defined for the first time, and is used to optimize resource allocation in terms of channel assignment and the number of transmitted semantic symbols. Additionally, for fair comparison of semantic and conventional communication systems, a transform method is developed to convert the conventional bit-based spectral efficiency to the S-SE. Simulation results demonstrate the validity and feasibility of the proposed resource allocation method, as well as the superiority of semantic communications in terms of the S-SE.
100 citations
2 Apr 2009
TL;DR: Experimental study conducted in three TREC collections reveals that semantic information can boost text retrieval performance with the use of the proposed GVSM, based on a new measure of semantic relatedness between terms.
Abstract: Generalized Vector Space Models (GVSM) extend the standard Vector Space Model (VSM) by embedding additional types of information, besides terms, in the representation of documents. An interesting type of information that can be used in such models is semantic information from word thesauri like WordNet. Previous attempts to construct GVSM reported contradicting results. The most challenging problem is to incorporate the semantic information in a theoretically sound and rigorous manner and to modify the standard interpretation of the VSM. In this paper we present a new GVSM model that exploits WordNet's semantic information. The model is based on a new measure of semantic relatedness between terms. Experimental study conducted in three TREC collections reveals that semantic information can boost text retrieval performance with the use of the proposed GVSM.
100 citations
TL;DR: A novel approach is presented, that interprets the concept of Semantic Wikis as a knowledge engineering environment, that effectively help to build decision-support systems and introduces the Semantic Wiki KnowWE, that provides the possibility to define and maintain ontologies together with strong problem-solving knowledge.
Abstract: Recently, Semantic Wikis showed reasonable success as collaboration platforms in the context of social semantic applications. In this paper, we present a novel approach, that interprets the concept of Semantic Wikis as a knowledge engineering environment, that effectively help to build decision-support systems. We introduce the Semantic Wiki KnowWE, that provides the possibility to define and maintain ontologies together with strong problem-solving knowledge. Thus, the wiki can be used to collaboratively build decision-support systems. These enhancements require extensions of the standard Semantic Wiki architecture by a task ontology for problem-solving and an adapted reasoning process. We discuss these extensions in detail, and we describe a case study in the field of medical emergency systems.
99 citations
TL;DR: S-Match as mentioned in this paper is an open source semantic matching framework that tackles the semantic interoperability problem by transforming several data structures such as business catalogs, web directories, conceptual models and web services descriptions into lightweight ontologies and establishing semantic correspondences between them.
Abstract: Achieving automatic interoperability among systems with diverse data structures and languages expressing different viewpoints is a goal that has been difficult to accomplish. This paper describes S-Match, an open source semantic matching framework that tackles the semantic interoperability problem by transforming several data structures such as business catalogs, web directories, conceptual models and web services descriptions into lightweight ontologies and establishing semantic correspondences between them. The framework is the first open source semantic matching project that includes three different algorithms tailored for specific domains and provides an extensible API for developing new algorithms, including possibility to plug-in specific background knowledge according to the characteristics of each application domain.
99 citations
TL;DR: In this paper, the authors present an approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source.
99 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 27 |
| 2024 | 39 |
| 2023 | 73 |
| 2022 | 100 |
| 2021 | 17 |
| 2020 | 22 |