Topic
Semantic interoperability
About: Semantic interoperability is a research topic. Over the lifetime, 5523 publications have been published within this topic receiving 81328 citations.
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TL;DR: This paper provides a tutorial introduction to the primary components of semantic models, which are the explicit representation of objects, attributes of and relationships among objects, type constructors for building complex types, ISA relationships, and derived schema components.
Abstract: Most common database management systems represent information in a simple record-based format. Semantic modeling provides richer data structuring capabilities for database applications. In particular, research in this area has articulated a number of constructs that provide mechanisms for representing structurally complex interrelations among data typically arising in commercial applications. In general terms, semantic modeling complements work on knowledge representation (in artificial intelligence) and on the new generation of database models based on the object-oriented paradigm of programming languages.This paper presents an in-depth discussion of semantic data modeling. It reviews the philosophical motivations of semantic models, including the need for high-level modeling abstractions and the reduction of semantic overloading of data type constructors. It then provides a tutorial introduction to the primary components of semantic models, which are the explicit representation of objects, attributes of and relationships among objects, type constructors for building complex types, ISA relationships, and derived schema components. Next, a survey of the prominent semantic models in the literature is presented. Further, since a broad area of research has developed around semantic modeling, a number of related topics based on these models are discussed, including data languages, graphical interfaces, theoretical investigations, and physical implementation strategies.
1,236 citations
TL;DR: This work presents an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications.
Abstract: Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.
948 citations
TL;DR: The CDA R2 model is richly expressive, enabling the formal representation of clinical statements such that they can be interpreted and acted upon by a computer, and offers a low bar for adoption.
731 citations
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TL;DR: In this paper, the semantic sensor web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge.
Abstract: Sensors are distributed across the globe leading to an avalanche of data about our environment It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge With a view to addressing this problem, the semantic sensor Web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge
658 citations
TL;DR: This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.
605 citations