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International Semantic Web Conference, , Web semantics: science, services and agents on the World Wide Web 36, , The evolved ontology may be used by the alignment system as the new ontology and may be further evolved upon exposure to new data sets. Additionally, the ontology may be saved, output, etc. As illustrated, various reference numerals indicating components or method steps of other exemplary embodiments illustrated in other figures are provided. In some embodiments, the referenced components or method steps may be relevant to one or more steps shown in FIG.
At step , a new data set is received at an alignment system. At steps and , the schema or data model schema is extracted from the new data set by the alignment system and assertions are made using an intermediate ontology. See, for example, Table 1 and FIG. In some embodiments, the new data set may have an explicit schema.
For example, the schema may be extracted based on metadata associated with the new data set that explicitly defines the structure of the data in the new data set, meaning of terms, etc. In some embodiments, the new data set may have an implicit schema. In the former case an explicit schema file can be analyzed, while in the latter i. For example, the schema may be inferred based on the organization of data in the new data set e.
The intermediate schema ontology may be aligned to the current ontology e. According to one embodiment, this process may progress as follows: 1 identify similar table names, 2 identify similar table column names, 3 identify similar primary keys, and 4 identify similar foreign keys. At step , the current ontology is evolved based on the intermediate schema ontology using the equivalence principles described previously. Evolving the current ontology may include adding one or more new statements or facts to the current ontology based on information contained in the intermediate schema ontology, where there are no semantic equivalents among the compared schema assertions.
Where there are semantic equivalents, then new assertions may be made relating those elements from the new schemas to the current aligned schema ontology. Further, additional knowledge may be inferred from the knowledge incorporated from the intermediate schema ontology, and additional statements may be added to the evolved ontology. At step , the evolved ontology may be saved e. In some embodiments, the evolved ontology may be expressed in the form of a set of OWL-2 statements. In some embodiments, the intermediate schema ontology e.
In some embodiments, alignment agents may incorporate a learning capability that may improve their ability to align elements of the schemas in ontology format. Referring again to FIG. For example, alignment may be improved inter-schema e. The evolutionary agents may learn to perform alignment using one or more of the following guideline patterns. Agents are trained on a corpus consisting of many different successful alignment schemas and ontologies where predefined alignment instance patterns have been identified.
In some embodiments, an external machine readable dictionary e. The evolutionary agents are supplied training corpus with the external dictionary to evolve their semantic matching capabilities for each synset e. As each agent learns each synset from multiple dictionary sources, it is able to recognize with increasing accuracy sets of words that have a reasonable semantic distance to its synset. There may be cases in which a word has a relationship to more than one synset. For purposes of semantic equivalence analysis, the agents may use another set of training examples that are tagged with semantic relationships to machine readable dictionaries and synsets, and then, in some embodiments, utilize a statistical algorithm to weight the strength of the occurrence of the semantic relationships to each synset for each word.
From this learning method, as more tagged training data is provided, the agents improve their ability to identify the various semantic relationships between elements of schema assertion statements and between schema assertion statements and statements of the aligned schema ontology. The disclosure is described above with reference to drawings. These drawings illustrate certain details of specific embodiments that implement the systems and methods and programs of the present disclosure.
However, describing the disclosure with drawings should not be construed as imposing on the disclosure any limitations that may be present in the drawings. The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing its operations. The embodiments of the present disclosure may be implemented using an existing computer processor, or by a special purpose computer processor incorporated for this or another purpose or by a hardwired system. No claim element herein is to be construed under the provisions of 35 U. As noted above, embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon.
Such machine-readable media can be any available media which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machine to perform a certain function or group of functions. Embodiments of the disclosure are described in the general context of method steps which may be implemented in one embodiment by a program product including machine-executable instructions, such as program code, for example, in the form of program modules executed by machines in networked environments.
Generally, program modules include routines, programs, objects, components, data structures, etc.
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Machine-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represent examples of corresponding acts for implementing the functions described in such steps. Embodiments of the present disclosure may be practiced in a networked environment using logical connections to one or more remote computers having processors.
Logical connections may include a local area network LAN and a wide area network WAN that are presented here by way of example and not limitation. Such networking environments are commonplace in office-wide or enterprise-wide computer networks, intranets and the Internet and may use a wide variety of different communication protocols.
Those skilled in the art will appreciate that such network computing environments will typically encompass many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, servers, minicomputers, mainframe computers, and the like.
Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked either by hardwired links, wireless links, or by a combination of hardwired or wireless links through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. An exemplary system for implementing the overall system or portions of the disclosure might include a general purpose computing device in the form of a computer, including a processing unit, a system memory, and a system bus that couples various system components including the system memory to the processing unit.
The computer may also include a magnetic hard disk drive for reading from and writing to a magnetic hard disk, a magnetic disk drive for reading from or writing to a removable magnetic disk, and an optical disk drive for reading from or writing to a removable optical disk such as a CD ROM or other optical media. The drives and their associated machine-readable media provide nonvolatile storage of machine-executable instructions, data structures, program modules, and other data for the computer.
It should be noted that although the flowcharts provided herein show a specific order of method steps, it is understood that the order of these steps may differ from what is depicted. Also two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. It is understood that all such variations are within the scope of the disclosure.
Likewise, software and web implementations of the present disclosure could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various database searching steps, correlation steps, comparison steps and decision steps. The foregoing description of embodiments of the disclosure have been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the disclosure.
The embodiments were chosen and described in order to explain the principals of the disclosure and its practical application to enable one skilled in the art to utilize the disclosure in various embodiments and with various modifications as are suited to the particular use contemplated. Year of fee payment : 4. Methods, systems, and computer-readable media relating to adapting existing data based on one or more new sets of data are provided. An exemplary method comprises extracting a data model from a new data set. SUMMARY One exemplary embodiment of the present disclosure relates to a method of adapting existing data based on one or more new sets of data.
Ebook Type Inference On Noisy Rdf Data
According to some exemplary embodiments, this evolution of the schema aligned ontology may occur by adding schema assertion statements obtained from the aggregated and inter-schema aligned ontology using the following alignment concepts: 1. There are two types of ontology definition statements: a.
Ontology defining statements where S and O are classes, and P is an object property between classes, or a data property between a class and a data type i. Ontology assertions defining relations between individuals of classes using object properties, or relations between a class and a data value using a data property i. Alignment may occur in one or both of the following places: a. The intermediate schema ontology used to represent each database may be formed as a hierarchy of class definitions see, e. When the same intermediate schema ontology is used to represent the structure and elements of each database to be aligned, there are no class level equivalence statements to be asserted between them; they are already equivalent.
Instead, the allowed alignment equivalence assertions are for individual schema assertions describing each database and its constituent elements. Alignment occurs between individual schema assertions between two schemas for their respective databases.
Ebook Type Inference On Noisy Rdf Data 2013
For a particular database description, a set of individual instances may be asserted for the respective classes in the intermediate schema ontology. The intermediate schema ontology taxonomic dependency structure is described, according to one exemplary embodiment, as follows: 1. This is a terminal class with no dependent classes.
Class has predefined individuals with one selected through property hasAttrType for Named individual in AttrDef. There are no dependent classes. The following axiomatic guidelines provide the basis for creating alignment assertions, according to an exemplary embodiment: 1. Each individual asserted in the aligned inter-schema assertions is analyzed for equivalence with classes in the evolutionary aligned schema ontology , and if there is an equivalence an assertion is made to relate the individual with the class: ClassAssertion EvolOntClass, InterSchemalndividual.
If there is no equivalence with a class, then the name of the individual is used as a definition for a new class in the evolutionary aligned schema ontology: Declaration InterSchemaIndividual. The evolutionary aligned schema ontology may have other classes used from related ontologies see, e. The actual alignment assertions relating elements of the individual schema assertions are statements using the following guidelines: a.
Identifying semantic matches of the elements of the two statements being compared, e. Statements with no semantic matches i. If no semantic matches occur between a statements elements and all other statements of the aligned ontology, than it is added to the aligned ontology without any additional equivalence statements c. Semantic matches may include hypernym e. In some embodiments, a reference dictionary may be used to discover these relations between the names of the compared classes. Class Equivalence Statement i. EquivalentClasses C1.
Individual Equivalence Statement i.
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In ontologies, individual membership of a class may be stated as a class assertion 1. ClassAssertion C, a where C is a class and a is an individual asserted to be a member of this class ii. Samelndividual a1,.
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Object Property Equivalence Statement i. Data Property Equivalence Statement i.
EquivalentDataProperties R1,. Explicit equivalent terms: Student, Hall 2. The actual alignment assertions relating elements of the individual schema assertions may be statements using the following guidelines: a. Semantic matches may include hypernym and hyponym relationships between classes relating to a more general semantic concepts or to a more specific semantic concepts, respectively. In ontologies individual membership of a class may be stated as a class assertion ClassAssertion C a where C is a class and a is an individual asserted to be a member of this class ii.
SameIndividual a1,. What is claimed is: 1. A method of incorporating one or more sets of new data and adapting existing data based on the new data, comprising: extracting, using an extraction module, a data model from a new data set, wherein the extraction module comprises instructions stored on at least one computer-readable medium that are executable by a processor;. The method of claim 1 , wherein the one or more new sets of data comprise a plurality of new data sets, and wherein: extracting a data model from a new data set comprises extracting a plurality of data models, wherein each of the plurality of data models corresponds to one of the plurality of new data sets;.
The method of claim 2 , wherein aligning the separate generated schema ontology assertions to generate a set of interschema aligned ontology solutions comprises: creating one or more new sets of ontology equivalence assertions where elements of two or more of the generated schema ontology assertions are determined equivalent; and.
The method of claim 2 , wherein aligning the generated schema ontology assertions to generate a set of interschema aligned ontology assertions comprises: determining the existence of one or more semantically equivalent elements contained within the schema ontology assertions;. The method of claim 2 , wherein aligning the set of interschema aligned ontology assertions with the at least one existing aligned ontology comprises: determining the existence of one or more semantically equivalent elements contained within schema ontology assertions of the set of interschema aligned ontology assertions and the at least one existing aligned ontology; and.
The method of claim 5 , wherein the existence of one or more semantically equivalent elements contained within the schema ontology assertions is determined based on a computer-readable dictionary comprising a plurality of sets of semantically equivalent terms. Submit Search. Successfully reported this slideshow. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime. Upcoming SlideShare. Like this presentation? Why not share! Embed Size px. Start on. Show related SlideShares at end.