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Semantic Integration
Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information (physical, psychological, and social), documents of all sorts, contacts (including social graphs), search results, and advertising and marketing relevance derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value. Applications and methods In enterprise application integration (EAI), semantic integration can facilitate or even automate the communication between computer systems using metadata publishing. Metadata publishing potentially offers the ability to automatically link ontology (computer science), ontologies. One approach to (semi-)automated ontology mapping requires the definition of a semantic distance or its inverse, semantic similarit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Social Graph
A social graph is a graph that represents social relations between entities. It is a model or representation of a social network. The social graph has been referred to as "the global mapping of everybody and how they're related". The term was used as early as 1964, albeit in the context of isoglosses. Leo Apostel uses the term in the context here in 1978. The concept was originally called sociogram. The term was popularized at the Facebook F8 conference on May 24, 2007, when it was used to explain how the newly introduced Facebook Platform would take advantage of the relationships between individuals to offer a richer online experience. The definition has been expanded to refer to a social graph of all Internet users. Since explaining the concept of the social graph, Mark Zuckerberg, one of the founders of Facebook, has often touted Facebook's goal of offering the website's social graph to other websites so that a user's relationships can be put to use on websites outsi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dataspaces
A dataspace is an abstraction in data management that aims to overcome some of the problems encountered in a data integration system. A dataspace is defined as a set of "participants", or data sources, and the relations between them: for example that dataset A is a duplicate of dataset B. It can contain all data sources of an organization regardless of their format, physical location, or data model. The data space then provides a unified interface to query data regardless of format, sometimes in a "best-effort" fashion, and ways to further integrate the data when necessary. It is very different than a traditional relational database, which requires that all data be in the same format. The aim of the concept is to reduce the effort required to set up a data integration system by relying on existing matching and mapping generation techniques, and to improve the system in "pay-as-you-go" fashion as it is used. Labor-intensive aspects of data integration are postponed until they are a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Management
Data management comprises all disciplines related to handling data as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for decision making. Concept The concept of data management emerged alongside the evolution of computing technology. In the 1950s, as computers became more prevalent, organizations began to grapple with the challenge of organizing and storing data efficiently. Early methods relied on punch cards and manual sorting, which were labor-intensive and prone to errors. The introduction of database management systems in the 1970s marked a significant milestone, enabling structured storage and retrieval of data. By the 1980s, relational database models revolutionized data management, emphasizing the importance of data as an asset and fostering a data-centric mindset in business. This era also saw the rise of data governance practices, which prioritized the organization and regulation of data to ensure quality and complian ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology (information Science)
In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as ''applied ontology''. Every academic discipline or field, in creating its terminology, thereby lays the groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries mainta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Unification
Semantic unification is the process of unifying lexically different concept representations that are judged to have the same semantic content (i.e., meaning). In business processes, the conceptual semantic unification is defined as "the mapping of two expressions onto an expression in an exchange format which is equivalent to the given expression". Semantic unification has since been applied to the fields of business processes and workflow management. In the early 1990s Charles Petri at Stanford University introduced the term "semantic unification" for business models, later references could be found in and later formalized in Fawsy Bendeck's dissertation. Petri introduced the term 'pragmatic semantic unification" to refer to the approaches in which the results are tested against a running application using the semantic mappings. In this pragmatic approach, the accuracy of the mapping is not as important as its usability. In general, semantic unification as used in business ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Translation
Semantic translation is the process of using semantic information to aid in the translation of data in one representation or data model to another representation or data model. Semantic translation takes advantage of semantics that associate meaning with individual data elements in one dictionary to create an equivalent meaning in a second system. An example of semantic translation is the conversion of XML data from one data model to a second data model using formal ontologies for each system such as the Web Ontology Language (OWL). This is frequently required by intelligent agents that wish to perform searches on remote computer systems that use different data models to store their data elements. The process of allowing a single user to search multiple systems with a single search request is also known as federated search. Semantic translation should be differentiated from data mapping tools that do simple one-to-one translation of data from one system to another without actu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Technology
The ultimate goal of semantic technology is to help machines understand data. To enable the encoding of semantics with the data, well-known technologies are RDF (Resource Description Framework) and OWL (Web Ontology Language). These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources. Overview In software, semantic technology encodes meanings separately from data and content files, and separately from application code. This enables machines as well as people to understand, share and reason with them at execution time. With semantic technologies, adding, changing and implementing new relationships or interconnecting programs in a different way can be just as simple as changing the external model that these programs share. Wit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology Matching
Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy. Computer science For computer scientists, concepts are expressed as labels for data. Historically, the need for ontology alignment arose out of the need to integrate heterogeneous databases, ones developed independently and thus each having their own data vocabulary. In the Semantic Web context involving many actors providing their own ontologies, ontology matching has taken a critical place for helping heterogeneous resources to interoperate. Ontology alignment tools find classes of data that are semantically equivalent, for example, "truck" and "lorry". The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007),Jérôme Euzenat and Pavel Shvaiko. 2013Ontology matching, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology Engineering
In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building Ontology (information science), ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities of a given domain of interest. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of Ontology, philosophical ontology. Core ideas and objectives of ontology engineering are also central in Conceptual model (computer science), conceptual modeling. Automated processing of information not interpretable by software ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology Alignment
Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy. Computer science For computer scientists, concepts are expressed as labels for data. Historically, the need for ontology alignment arose out of the need to integrate heterogeneous databases, ones developed independently and thus each having their own data vocabulary. In the Semantic Web context involving many actors providing their own ontologies, ontology matching has taken a critical place for helping heterogeneous resources to interoperate. Ontology alignment tools find classes of data that are semantically equivalent, for example, "truck" and "lorry". The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007),Jérôme Euzenat and Pavel Shvaiko. 2013Ontology matching ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology-based Data Integration
Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology‑based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process. Background Data from multiple sources are characterized by multiple types of heterogeneity. The following hierarchy is often used: * Syntactic heterogeneity: is a result of differences in representation format of data * Schematic or structural heterogeneity: the native model or structure to store data differ in data sources leading to structural heterogeneity. Schematic heterogeneity that particularly appears in structured databases is also an aspect of structural heterogeneity. * Semantic heterogeneity: differences in interpretation of the 'meaning' of data are source of sem ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
