Abstract: Text categorization techniques are widely used to many Information Retrieval (IR) applications. In this paper, we proposed a simple but efficient method that can automatically find the relationship between any pair of terms and documents, also an indexing matrix is established for text categorization. We call this method Indexing Matrix Categorization Machine (IMCM). Several experiments are conducted to show the efficiency and robust of our algorithm.
Abstract: One of object oriented software developing problem
is the difficulty of searching the appropriate and suitable objects for
starting the system. In this work, ontologies appear in the part of
supporting the object discovering in the initial of object oriented
software developing. There are many researches try to demonstrate
that there is a great potential between object model and ontologies.
Constructing ontology from object model is called ontology
engineering can be done; On the other hand, this research is aiming to
support the idea of building object model from ontology is also
promising and practical. Ontology classes are available online in any
specific areas, which can be searched by semantic search engine.
There are also many helping tools to do so; one of them which are
used in this research is Protégé ontology editor and Visual Paradigm.
To put them together give a great outcome. This research will be
shown how it works efficiently with the real case study by using
ontology classes in travel/tourism domain area. It needs to combine
classes, properties, and relationships from more than two ontologies
in order to generate the object model. In this paper presents a simple
methodology framework which explains the process of discovering
objects. The results show that this framework has great value while
there is possible for expansion. Reusing of existing ontologies offers
a much cheaper alternative than building new ones from scratch.
More ontologies are becoming available on the web, and online
ontologies libraries for storing and indexing ontologies are increasing
in number and demand. Semantic and Ontologies search engines have
also started to appear, to facilitate search and retrieval of online
ontologies.
Abstract: The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for crossdomain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.
Abstract: Considering the theory of attribute grammars, we use
logical formulas instead of traditional functional semantic rules.
Following the decoration of a derivation tree, a suitable algorithm
should maintain the consistency of the formulas together with the
evaluation of the attributes. This may be a Prolog-like resolution, but
this paper examines a somewhat different strategy, based on
production specialization, local consistency and propagation: given a
derivation tree, it is interactively decorated, i.e. incrementally
checked and evaluated. The non-directed dependencies are
dynamically directed during attribute evaluation.
Abstract: Data mining and knowledge engineering have become a tough task due to the availability of large amount of data in the web nowadays. Validity and reliability of data also become a main debate in knowledge acquisition. Besides, acquiring knowledge from different languages has become another concern. There are many language translators and corpora developed but the function of these translators and corpora are usually limited to certain languages and domains. Furthermore, search results from engines with traditional 'keyword' approach are no longer satisfying. More intelligent knowledge engineering agents are needed. To address to these problems, a system known as Multilingual Word Semantic Network is proposed. This system adapted semantic network to organize words according to concepts and relations. The system also uses open source as the development philosophy to enable the native language speakers and experts to contribute their knowledge to the system. The contributed words are then defined and linked using lexical and semantic relations. Thus, related words and derivatives can be identified and linked. From the outcome of the system implementation, it contributes to the development of semantic web and knowledge engineering.
Abstract: Databases have become ubiquitous. Almost all IT applications are storing into and retrieving information from databases. Retrieving information from the database requires knowledge of technical languages such as Structured Query Language (SQL). However majority of the users who interact with the databases do not have a technical background and are intimidated by the idea of using languages such as SQL. This has led to the development of a few Natural Language Database Interfaces (NLDBIs). A NLDBI allows the user to query the database in a natural language. This paper highlights on architecture of new NLDBI system, its implementation and discusses on results obtained. In most of the typical NLDBI systems the natural language statement is converted into an internal representation based on the syntactic and semantic knowledge of the natural language. This representation is then converted into queries using a representation converter. A natural language query is translated to an equivalent SQL query after processing through various stages. The work has been experimented on primitive database queries with certain constraints.
Abstract: This paper makes a contribution to the on-going
debate on conceptualization and lexicalization of cutting and
breaking (C&B) verbs by discussing data from Telugu, a language of
India belonging to the Dravidian family. Five Telugu native speakers-
verbalizations of agentive actions depicted in 43 short video-clips
were analyzed. It was noted that verbalization of C&B events in
Telugu requires formal units such as simple lexical verbs, explicator
compound verbs, and other complex verb forms. The properties of
the objects involved, the kind of instruments used, and the manner of
action had differential influence on the lexicalization patterns.
Further, it was noted that all the complex verb forms encode 'result'
and 'cause' sub-events in that order. Due to the polysemy associated
with some of the verb forms, our data does not support the
straightforward bipartition of this semantic domain.
Abstract: The data exchanged on the Web are of different nature
from those treated by the classical database management systems;
these data are called semi-structured data since they do not have a
regular and static structure like data found in a relational database;
their schema is dynamic and may contain missing data or types.
Therefore, the needs for developing further techniques and
algorithms to exploit and integrate such data, and extract relevant
information for the user have been raised. In this paper we present
the system OSIX (Osiris based System for Integration of XML
Sources). This system has a Data Warehouse model designed for the
integration of semi-structured data and more precisely for the
integration of XML documents. The architecture of OSIX relies on
the Osiris system, a DL-based model designed for the representation
and management of databases and knowledge bases. Osiris is a viewbased
data model whose indexing system supports semantic query
optimization. We show that the problem of query processing on a
XML source is optimized by the indexing approach proposed by
Osiris.
Abstract: Business process modeling has become an accepted
means for designing and describing business operations. Thereby,
consistency of business process models, i.e., the absence of modeling
faults, is of upmost importance to organizations. This paper presents
a concept and subsequent implementation for detecting faults in
business process models and for computing a measure of their
consistency. It incorporates not only syntactic consistency but also
semantic consistency, i.e., consistency regarding the meaning of
model elements from a business perspective.
Abstract: With the advent of emerging personal computing paradigms such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide range of mobile devices. Since these devices do not have the same rendering capabilities, Web contents need to be adapted for transparent access from a variety of client agents. Such content adaptation is exploited for either an individual element or a set of consecutive elements in a Web document and results in better rendering and faster delivery to the client device. Nevertheless, Web content adaptation sets new challenges for semantic markup. This paper presents an advanced components platform, called SMC, enabling the development of mobility applications and services according to a channel model based on the principles of Services Oriented Architecture (SOA). It then goes on to describe the potential for integration with the Semantic Web through a novel framework of external semantic annotation that prescribes a scheme for representing semantic markup files and a way of associating Web documents with these external annotations. The role of semantic annotation in this framework is to describe the contents of individual documents themselves, assuring the preservation of the semantics during the process of adapting content rendering. Semantic Web content adaptation is a way of adding value to Web contents and facilitates repurposing of Web contents (enhanced browsing, Web Services location and access, etc).
Abstract: The ability of UML to handle the modeling process of complex industrial software applications has increased its popularity to the extent of becoming the de-facto language in serving the design purpose. Although, its rich graphical notation naturally oriented towards the object-oriented concept, facilitates the understandability, it hardly successes to report all domainspecific aspects in a satisfactory way. OCL, as the standard language for expressing additional constraints on UML models, has great potential to help improve expressiveness. Unfortunately, it suffers from a weak formalism due to its poor semantic resulting in many obstacles towards the build of tools support and thus its application in the industry field. For this reason, many researches were established to formalize OCL expressions using a more rigorous approach. Our contribution join this work in a complementary way since it focuses specifically on OCL predefined properties which constitute an important part in the construction of OCL expressions. Using formal methods, we mainly succeed in expressing rigorously OCL predefined functions.
Abstract: Documents clustering become an essential technology
with the popularity of the Internet. That also means that fast and
high-quality document clustering technique play core topics. Text
clustering or shortly clustering is about discovering semantically
related groups in an unstructured collection of documents. Clustering
has been very popular for a long time because it provides unique
ways of digesting and generalizing large amounts of information.
One of the issues of clustering is to extract proper feature (concept)
of a problem domain. The existing clustering technology mainly
focuses on term weight calculation. To achieve more accurate
document clustering, more informative features including concept
weight are important. Feature Selection is important for clustering
process because some of the irrelevant or redundant feature may
misguide the clustering results. To counteract this issue, the proposed
system presents the concept weight for text clustering system
developed based on a k-means algorithm in accordance with the
principles of ontology so that the important of words of a cluster can
be identified by the weight values. To a certain extent, it has resolved
the semantic problem in specific areas.
Abstract: In this paper we present semantic assistant agent
(SAA), an open source digital library agent which takes user query
for finding information in the digital library and takes resources-
metadata and stores it semantically. SAA uses Semantic Web to
improve browsing and searching for resources in digital library. All
metadata stored in the library are available in RDF format for
querying and processing by SemanSreach which is a part of SAA
architecture. The architecture includes a generic RDF-based model
that represents relationships among objects and their components.
Queries against these relationships are supported by an RDF triple
store.
Abstract: This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.
Abstract: More and more home videos are being generated with the ever growing popularity of digital cameras and camcorders. For many home videos, a photo rendering, whether capturing a moment or a scene within the video, provides a complementary representation to the video. In this paper, a video motion mining framework for creative rendering is presented. The user-s capture intent is derived by analyzing video motions, and respective metadata is generated for each capture type. The metadata can be used in a number of applications, such as creating video thumbnail, generating panorama posters, and producing slideshows of video.
Abstract: Parsing is important in Linguistics and Natural
Language Processing to understand the syntax and semantics of a
natural language grammar. Parsing natural language text is
challenging because of the problems like ambiguity and inefficiency.
Also the interpretation of natural language text depends on context
based techniques. A probabilistic component is essential to resolve
ambiguity in both syntax and semantics thereby increasing accuracy
and efficiency of the parser. Tamil language has some inherent
features which are more challenging. In order to obtain the solutions,
lexicalized and statistical approach is to be applied in the parsing
with the aid of a language model. Statistical models mainly focus on
semantics of the language which are suitable for large vocabulary
tasks where as structural methods focus on syntax which models
small vocabulary tasks. A statistical language model based on Trigram
for Tamil language with medium vocabulary of 5000 words has
been built. Though statistical parsing gives better performance
through tri-gram probabilities and large vocabulary size, it has some
disadvantages like focus on semantics rather than syntax, lack of
support in free ordering of words and long term relationship. To
overcome the disadvantages a structural component is to be
incorporated in statistical language models which leads to the
implementation of hybrid language models. This paper has attempted
to build phrase structured hybrid language model which resolves
above mentioned disadvantages. In the development of hybrid
language model, new part of speech tag set for Tamil language has
been developed with more than 500 tags which have the wider
coverage. A phrase structured Treebank has been developed with 326
Tamil sentences which covers more than 5000 words. A hybrid
language model has been trained with the phrase structured Treebank
using immediate head parsing technique. Lexicalized and statistical
parser which employs this hybrid language model and immediate
head parsing technique gives better results than pure grammar and
trigram based model.
Abstract: Nowadays, organizing a repository of documents and
resources for learning on a special field as Information Technology
(IT), together with search techniques based on domain knowledge or
document-s content is an urgent need in practice of teaching, learning
and researching. There have been several works related to methods of
organization and search by content. However, the results are still
limited and insufficient to meet user-s demand for semantic
document retrieval. This paper presents a solution for the
organization of a repository that supports semantic representation and
processing in search. The proposed solution is a model which
integrates components such as an ontology describing domain
knowledge, a database of document repository, semantic
representation for documents and a file system; with problems,
semantic processing techniques and advanced search techniques
based on measuring semantic similarity. The solution is applied to
build a IT learning materials management system of a university with
semantic search function serving students, teachers, and manager as
well. The application has been implemented, tested at the University
of Information Technology, Ho Chi Minh City, Vietnam and has
achieved good results.
Abstract: In modern distributed software systems, the issue of communication among composing parts represents a critical point, but the idea of extending conventional programming languages with general purpose communication constructs seems difficult to realize. As a consequence, there is a (growing) gap between the abstraction level required by distributed applications and the concepts provided by platforms that enable communication. This work intends to discuss how the Model Driven Software Development approach can be considered as a mature technology to generate in automatic way the schematic part of applications related to communication, by providing at the same time high level specialized languages useful in all the phases of software production. To achieve the goal, a stack of languages (meta-meta¬models) has been introduced in order to describe – at different levels of abstraction – the collaborative behavior of generic entities in terms of communication actions related to a taxonomy of messages. Finally, the generation of platforms for communication is viewed as a form of specification of language semantics, that provides executable models of applications together with model-checking supports and effective runtime environments.