2,408 research outputs found
Semantic Query Optimisation with Ontology Simulation
Semantic Web is, without a doubt, gaining momentum in both industry and
academia. The word "Semantic" refers to "meaning" - a semantic web is a web of
meaning. In this fast changing and result oriented practical world, gone are
the days where an individual had to struggle for finding information on the
Internet where knowledge management was the major issue. The semantic web has a
vision of linking, integrating and analysing data from various data sources and
forming a new information stream, hence a web of databases connected with each
other and machines interacting with other machines to yield results which are
user oriented and accurate. With the emergence of Semantic Web framework the
na\"ive approach of searching information on the syntactic web is clich\'e.
This paper proposes an optimised semantic searching of keywords exemplified by
simulation an ontology of Indian universities with a proposed algorithm which
ramifies the effective semantic retrieval of information which is easy to
access and time saving
Web Service Discovery in a Semantically Extended UDDI Registry: the Case of FUSION
Service-oriented computing is being adopted at an unprecedented rate, making the effectiveness of automated service discovery an increasingly important challenge. UDDI has emerged as a de facto industry standard and fundamental building block within SOA infrastructures. Nevertheless, conventional UDDI registries lack means to provide unambiguous, semantically rich representations of Web service capabilities, and the logic inference power required for facilitating automated service discovery. To overcome this important limitation, a number of approaches have been proposed towards augmenting Web service discovery with semantics. This paper discusses the benefits of semantically extending Web service descriptions and UDDI registries, and presents an overview of the approach put forward in project FUSION, towards semantically-enhanced publication and discovery of services based on SAWSDL
Development of Use Cases, Part I
For determining requirements and constructs appropriate for a Web query language, or in fact
any language, use cases are of essence. The W3C has published two sets of use cases for XML
and RDF query languages. In this article, solutions for these use cases are presented using
Xcerpt. a novel Web and Semantic Web query language that combines access to standard Web
data such as XML documents with access to Semantic Web metadata
such as RDF resource
descriptions with reasoning abilities and rules familiar from logicprogramming.
To the
best knowledge of the authors, this is the first in depth study of how to solve use cases for
accessing XML and RDF in a single language: Integrated access to data and metadata
has been
recognized by industry and academia as one of the key challenges in data processing for the
next decade. This article is a contribution towards addressing this challenge by demonstrating
along practical and recognized use cases the usefulness of reasoning abilities, rules, and
semistructured
query languages for accessing both data (XML) and metadata
(RDF)
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
Representation Independent Analytics Over Structured Data
Database analytics algorithms leverage quantifiable structural properties of
the data to predict interesting concepts and relationships. The same
information, however, can be represented using many different structures and
the structural properties observed over particular representations do not
necessarily hold for alternative structures. Thus, there is no guarantee that
current database analytics algorithms will still provide the correct insights,
no matter what structures are chosen to organize the database. Because these
algorithms tend to be highly effective over some choices of structure, such as
that of the databases used to validate them, but not so effective with others,
database analytics has largely remained the province of experts who can find
the desired forms for these algorithms. We argue that in order to make database
analytics usable, we should use or develop algorithms that are effective over a
wide range of choices of structural organizations. We introduce the notion of
representation independence, study its fundamental properties for a wide range
of data analytics algorithms, and empirically analyze the amount of
representation independence of some popular database analytics algorithms. Our
results indicate that most algorithms are not generally representation
independent and find the characteristics of more representation independent
heuristics under certain representational shifts
Towards the Automatic Classification of Documents in User-generated Classifications
There is a huge amount of information scattered on the World Wide Web. As the information flow occurs at a high speed in the WWW, there is a need to organize it in the right manner so that a user can access it very easily. Previously the organization of information was generally done manually, by matching the document contents to some pre-defined categories. There are two approaches for this text-based categorization: manual and automatic. In the manual approach, a human expert performs the classification task, and in the second case supervised classifiers are used to automatically classify resources. In a supervised classification, manual interaction is required to create some training data before the automatic classification task takes place. In our new approach, we intend to propose automatic classification of documents through semantic keywords and building the formulas generation by these keywords. Thus we can reduce this human participation by combining the knowledge of a given classification and the knowledge extracted from the data. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia, is the automatic classification of documents into user-generated classifications. The key benefits foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering, semantic index managing
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