Fund Finder: A case study of database-to-ontology mapping

The mapping between databases and ontologies is a basic problem when trying to "upgrade" deep web content to the semantic web. Our approach suggests the declarative definition of mappings as a way to achieve domain independency and reusability. A specific language (expressive enough to cover some real world mapping situations like lightly structured databases or not 1st normal form ones) is defined for this purpose. Along with this mapping description language, the ODEMapster processor is in charge of carrying out the effective instance data migration. We illustrate this by testing both the mappings definition and processor on a case study

Methodological considerations concerning manual annotation of musical audio in function of algorithm development

In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of high-level content that users can deal with in Music Information Retrieval (MIR) contexts. The notion of musical content, and therefore the notion of annotation, is ill-defined, however, both in the syntactic and semantic sense. As a consequence, annotation has been approached from a variety of perspectives (but mainly linguistic-symbolic oriented), and a general methodology is lacking. This paper is a step towards the definition of a general framework for manual annotation of musical audio in function of a computational approach to musical audio-mining that is based on algorithms that learn from annotated data. 1

R2O, an extensible and semantically based database-to-ontology mapping language

We present R2O, an extensible and declarative language to describe mappings between relational DB schemas and ontologies implemented in RDF(S) or OWL. R2O provides an extensible set of primitives with welldefined semantics. This language has been conceived expressive enough to cope with complex mapping cases arisen from situations of low similarity between the ontology and the DB models

Lexical Knowledge Extraction: an Effective Approach to Schema and Ontology Matching

This paper’s aim is to examine what role Lexical Knowledge Extraction plays in data integration as well as ontology engineering.Data integration is the problem of combining data residing at distributed heterogeneous sources, and providing the user with a unified view of these data; a common and important scenario in data integration are structured or semi-structure data sources described by a schema.Ontology engineering is a subfield of knowledge engineering that studies the methodologies for building and maintaining ontologies. Ontology engineering offers a direction towards solving the interoperability problems brought about by semantic obstacles, such as the obstacles related to the definitions of business terms and software classes. In these contexts where users are confronted with heterogeneous information it is crucial the support of matching techniques. Matching techniques aim at finding correspondences between semantically related entities of different schemata/ontologies.Several matching techniques have been proposed in the literature based on different approaches, often derived from other fields, such as text similarity, graph comparison and machine learning.This paper proposes a matching technique based on Lexical Knowledge Extraction: first, an Automatic Lexical Annotation of schemata/ontologies is performed, then lexical relationships are extracted based on such annotations.Lexical Annotation is a piece of information added in a document (book, online record, video, or other data), that refers to a semantic resource such as WordNet. Each annotation has the property to own one or more lexical descriptions. Lexical annotation is performed by the Probabilistic Word Sense Disambiguation (PWSD) method that combines several disambiguation algorithms.Our hypothesis is that performing lexical annotation of elements (e.g. classes and properties/attributes) of schemata/ontologies makes the system able to automatically extract the lexical knowledge that is implicit in a schema/ontology and then to derive lexical relationships between the elements of a schema/ontology or among elements of different schemata/ontologies.The effectiveness of the method presented in this paper has been proven within the data integration system MOMIS

An incremental method for meaning elicitation of a domain ontology

Internet has opened the access to an overwhelming amount of data, requiring the development of new applications to automatically recognize, process and manage informationavailable in web sites or web-based applications. The standardSemantic Web architecture exploits ontologies to give a shared(and known) meaning to each web source elements.In this context, we developed MELIS (Meaning Elicitation and Lexical Integration System). MELIS couples the lexical annotation module of the MOMIS system with some components from CTXMATCH2.0, a tool for eliciting meaning from severaltypes of schemas and match them. MELIS uses the MOMIS WNEditor and CTXMATCH2.0 to support two main tasks in theMOMIS ontology generation methodology: the source annotationprocess, i.e. the operation of associating an element of a lexicaldatabase to each source element, and the extraction of lexicalrelationships among elements of different data sources

KC Two-Way Clustering Algorithms For Multi-Child Semantic Maps In Image Mining

Image mining is now a thriving and expanding field of computer science research. Image mining is linked to the advancement of data mining in image preparation. Image mining is used to extract hidden information and in other situations where the photos do not clearly describe the situation. Image mining combines machine learning, data handling, application autonomy, and image preparation concepts. Semantic maps are used to visualize image data stored in image databases. We recommend using Multi-Child Semantic Maps to build semantic maps which fully display the image. In this study, we propose two path clustering on Multi-Child Semantic Maps (MCSM) using the K-C Means Clustering Algorithm, also known as the MCSMK-C algorithm. This algorithm causes image clustering and instructs the mining system to look at the image's top area. When mining, the MCSMK-C algorithm considers the X and Y coordinates. The system looks for groups by examining each object's territory in the database, and it saves a region if it contains more objects than the required number

Melis: an incremental method for the lexical annotation of domain ontologies

In this paper, we present MELIS (Meaning Elicitation and Lexical Integration System), a method and a software tool for enabling an incremental process of automatic annotation of local schemas (e.g. relational database schemas, directory trees) with lexical information. The distinguishing and original feature of MELIS is the incremental process: the higher the number of schemas which are processed, the more background/domain knowledge is cumulated in the system (a portion of domain ontology is learned at every step), the better the performance of the systems on annotating new schemas.MELIS has been tested as component of MOMIS-Ontology Builder, a framework able to create a domain ontology representing a set of selected data sources, described with a standard W3C language wherein concepts and attributes are annotated according to the lexical reference database.We describe the MELIS component within the MOMIS-Ontology Builder framework and provide some experimental results of ME LIS as a standalone tool and as a component integrated in MOMIS

Application of Integrated Interface Schema (LIS) Over Multiple WDBS To Enhance Data Unit Annotation

An annotation wrapper for the search site is automatically build and can be used to interpret new result pages from the same web database.  A growing number of databases have become web accessible through HTML form based search interfaces. The data units revisit from the underlying database are regularly encoded into the result pages dynamically for human browsing. In this paper we present an automatic annotation approach that first line up the data units on a result page into different groups such that the data in the same group have the same semantic. Then for each group we annotate it from dissimilar aspects and cumulative the different annotations to expect a final annotation label for it. Our experiments specify that the proposed approach is superior and effectual