Abstract: Phenomenological analysis is not based on natural language, but ideal language which is able to be a carrier of ideal meanings – eidos representing typical structures or essences. For this purpose, it’s necessary to release from the spatio-temporal definiteness of a subject and then state its noetic essence (eidos) by means of free fantasy generation. Herewith, as if a totally new objectness is created - the universal, confirming the thesis that thinking process takes place in generalizations passing by numerous means through the specific to the general and from the general through the specific to the singular.
Abstract: Lexical invariants, being a sort of stereotypes within the frames of ordinary consciousness, are created by the members of a language community as a result of uniform division of reality. The invariant meaning is formed in person’s mind gradually in the course of different actualizations of secondary meanings in various contexts. We understand lexical the invariant as abstract language essence containing a set of semantic components. In one of its configurations it is the basis or all or a number of the meanings making up the semantic structure of the word.
Abstract: Systems running these days are huge, complex and exist in many versions. Controlling these versions and tracking their changes became a very hard process as some versions are created using meaningless names or specifications. Many versions of a system are created with no clear difference between them. This leads to mismatching between a user’s request and the version he gets. In this paper, we present a system versions meta-modeling approach that produces versions based on system’s features. This model reduced the number of steps needed to configure a release and gave each version its unique specifications. This approach is applicable for systems that use features in its specification.
Abstract: This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to identify itemsets whose utility satisfies a given threshold. Although, the approach of mining HUIs in a distributed environment and mining of the same from XML data have not explored yet. In this work, a novel approach is proposed to mine HUIs from the XML based data in a distributed environment. This work utilizes Service Oriented Computing (SOC) paradigm which provides Knowledge as a Service (KaaS). The interesting patterns are provided via the web services with the help of knowledge server to answer the queries of the consumers. The performance of the approach is evaluated on various databases using execution time and memory consumption.
Abstract: The intense use of the web has made it a very changing environment, its content is in permanent evolution to adapt to the demands. The standards have accompanied this evolution by passing from standards that regroup data with their presentations without any structuring such as HTML, to standards that separate both and give more importance to the structural aspect of the content such as XML standard and its derivatives. Currently, with the appearance of the Semantic Web, ontologies become increasingly present on the web and standards that allow their representations as OWL and RDF/RDFS begin to gain momentum. This paper provided an automatic method that converts XML schema document to ontologies represented in OWL.
Abstract: The continuous growth in the size of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills and more sophisticated tools to help the Web user to find the desired information. In order to make Web more user friendly, it is necessary to provide personalized services and recommendations to the Web user. For discovering interesting and frequent navigation patterns from Web server logs many Web usage mining techniques have been applied. The recommendation accuracy of usage based techniques can be improved by integrating Web site content and site structure in the personalization process.
Herein, we propose semantically enriched Web Usage Mining method for Personalization (SWUMP), an extension to solely usage based technique. This approach is a combination of the fields of Web Usage Mining and Semantic Web. In the proposed method, we envisage enriching the undirected graph derived from usage data with rich semantic information extracted from the Web pages and the Web site structure. The experimental results show that the SWUMP generates accurate recommendations and is able to achieve 10-20% better accuracy than the solely usage based model. The SWUMP addresses the new item problem inherent to solely usage based techniques.
Abstract: This paper proposes an approach for translating an existing relational database (RDB) schema into ORDB. The transition is done with methods that can extract various functions from a RDB which is based on aggregations, associations between the various tables, and the reflexive relationships. These methods can extract even the inheritance knowing that no process of reverse engineering can know that it is an Inheritance; therefore, our approach exceeded all of the previous studies made for the transition from RDB to ORDB. In summation, the creation of the New Data Model (NDM) that stocks the RDB in a form of a structured table, and from the NDM we create our navigational model in order to simplify the implementation object from which we develop our different types. Through these types we precede to the last step, the creation of tables.
The step mentioned above does not require any human interference. All this is done automatically, and a prototype has already been created which proves the effectiveness of this approach.
Abstract: Events discrimination and decision maker in sport field are the subject of many interesting studies in computer vision and artificial intelligence. A large volume of research has been conducted for automatic semantic event detection and summarization of sports videos. Indeed the results of these researches have a very significant contribution, as well to television broadcasts as to the football teams, since the result of sporting event can be reflected on the economic field. In this paper, we propose a novel fuzzy sequential technique which lead to discriminate events and specify the technico-tactics on going the game, nor the fuzzy system or the sequential one, may be able to respond to the asked question, in fact fuzzy process is not sufficient, it does not respect the chronological order according the time of various events, similarly the sequential process needs flexibility about the parameters used in this study, it may affect a membership degree of each parameter on the one hand and respect the sequencing of events for each frame on the other hand. Indeed this technique describes special events such as dribbling, headings, short sprints, rapid acceleration or deceleration, turning, jumping, kicking, ball occupation, and tackling according velocity vectors of the two players and the ball direction.
Abstract: Due the proliferation of smartphones in everyday use, several different outdoor navigation systems have become available. Since these smartphones are able to connect to the Internet, the users can obtain location-based information during the navigation as well. The users could interactively get to know the specifics of a particular area (for instance, ancient cultural area, Statue Park, cemetery) with the help of thus obtained information. In this paper, we present an Augmented Reality system which uses Semantic Web technologies and is based on the interaction between the user and the smartphone. The system allows navigating through a specific area and provides information and details about the sight an interactive manner.
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 cross-domain 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: Often the users of a semantic search application are facing the problem that they do not find appropriate terms for their search. This holds especially if the data to be searched is from a technical field in which the user does not have expertise. In order to support the user finding the results he seeks, we developed a domain-specific ontology and implemented it into a search application. The ontology serves as a knowledge base, suggesting technical terms to the user which he can add to his query. In this paper, we present the search application and the underlying ontology as well as the project EnArgus in which the application was developed.
Abstract: Improvements in the data fusion and data analysis phase of research are imperative due to the exponential growth of sensed data. Currently, there are developments in the Semantic Sensor Web community to explore efficient methods for reuse, correlation and integration of web-based data sets and live data streams. This paper describes the integration of remotely sensed data with web-available static data for use in observational hypothesis testing and the analysis phase of research. The Semantic Reef system combines semantic technologies (e.g., well-defined ontologies and logic systems) with scientific workflows to enable hypothesis-based research. A framework is presented for how the data fusion concepts from the Semantic Reef architecture map to the Smart Environment Monitoring and Analysis Technologies (SEMAT) intelligent sensor network initiative. The data collected via SEMAT and the inferred knowledge from the Semantic Reef system are ingested to the Tropical Data Hub for data discovery, reuse, curation and publication.
Abstract: Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.
Abstract: Ontologies play an important role in semantic web
applications and are often developed by different groups and
continues to evolve over time. The knowledge in ontologies changes
very rapidly that make the applications outdated if they continue to
use old versions or unstable if they jump to new versions. Temporal
frames using frame versioning and slot versioning are used to take
care of dynamic nature of the ontologies. The paper proposes new
tags and restructured OWL format enabling the applications to work
with the old or new version of ontologies. Gene Ontology, a very
dynamic ontology, has been used as a case study to explain the OWL
Ontology with Temporal Tags.
Abstract: To achieve competitive advantage nowadays, most of
the industrial companies are considering that success is sustained to
great product development. That is to manage the product throughout
its entire lifetime ranging from design, manufacture, operation and
destruction. Achieving this goal requires a tight collaboration
between partners from a wide variety of domains, resulting in various
product data types and formats, as well as different software tools. So
far, the lack of a meaningful unified representation for product data
semantics has slowed down efficient product development. This
paper proposes an ontology based approach to enable such semantic
interoperability. Generic and extendible product ontology is
described, gathering main concepts pertaining to the mechanical field
and the relations that hold among them. The ontology is not
exhaustive; nevertheless, it shows that such a unified representation
is possible and easily exploitable. This is illustrated thru a case study
with an example product and some semantic requests to which the
ontology responds quite easily. The study proves the efficiency of
ontologies as a support to product data exchange and information
sharing, especially in product development environments where
collaboration is not just a choice but a mandatory prerequisite.
Abstract: With a growing number of digital libraries and other
open education repositories being made available throughout the
world, effective search and retrieval tools are necessary to access the
desired materials that surpass the effectiveness of traditional, allinclusive
search engines. This paper discusses the design and use of
Folksemantic, a platform that integrates OpenCourseWare search,
Open Educational Resource recommendations, and social network
functionality into a single open source project. The paper describes
how the system was originally envisioned, its goals for users, and
data that provides insight into how it is actually being used. Data
sources include website click-through data, query logs, web server
log files and user account data. Based on a descriptive analysis of its
current use, modifications to the platform's design are recommended
to better address goals of the system, along with recommendations
for additional phases of research.
Abstract: FAQ system can make user find answer to the problem that puzzles them. But now the research on Chinese FAQ system is still on the theoretical stage. This paper presents an approach to semantic inference for FAQ mining. To enhance the efficiency, a small pool of the candidate question-answering pairs retrieved from the system for the follow-up work according to the concept of the agriculture domain extracted from user input .Input queries or questions are converted into four parts, the question word segment (QWS), the verb segment (VS), the concept of agricultural areas segment (CS), the auxiliary segment (AS). A semantic matching method is presented to estimate the similarity between the semantic segments of the query and the questions in the pool of the candidate. A thesaurus constructed from the HowNet, a Chinese knowledge base, is adopted for word similarity measure in the matcher. The questions are classified into eleven intension categories using predefined question stemming keywords. For FAQ mining, given a query, the question part and answer part in an FAQ question-answer pair is matched with the input query, respectively. Finally, the probabilities estimated from these two parts are integrated and used to choose the most likely answer for the input query. These approaches are experimented on an agriculture FAQ system. Experimental results indicate that the proposed approach outperformed the FAQ-Finder system in agriculture FAQ retrieval.
Abstract: Abai Kunanbayev (1845-1904) was a great Kazakh
poet, composer and philosopher. Abai's main contribution to Kazakh
culture and folklore lies in his poetry, which expresses great
nationalism and grew out of Kazakh folk culture. Before him, most
Kazakh poetry was oral, echoing the nomadic habits of the people of
the Kazakh steppes. We want to introduce to abroad our country, its
history, tradition and culture. We can introduce it only through
translations. Only by reading the Kazakh works can foreign people
know who are kazakhs, the style of their life, their thoughts and so
on. All information comes only through translation. The main
requirement to a good translation is that it should be natural or that it
should read as smoothly as the original. Literary translation should
be adequate, should follow the original to the fullest. Translators
have to be loyal to original text, they shouldn-t give the way to
liberty.
Abstract: The most important property of the Gene Ontology is
the terms. These control vocabularies are defined to provide
consistent descriptions of gene products that are shareable and
computationally accessible by humans, software agent, or other
machine-readable meta-data. Each term is associated with
information such as definition, synonyms, database references, amino
acid sequences, and relationships to other terms. This information has
made the Gene Ontology broadly applied in microarray and
proteomic analysis. However, the process of searching the terms is
still carried out using traditional approach which is based on keyword
matching. The weaknesses of this approach are: ignoring semantic
relationships between terms, and highly depending on a specialist to
find similar terms. Therefore, this study combines semantic similarity
measure and genetic algorithm to perform a better retrieval process
for searching semantically similar terms. The semantic similarity
measure is used to compute similitude strength between two terms.
Then, the genetic algorithm is employed to perform batch retrievals
and to handle the situation of the large search space of the Gene
Ontology graph. The computational results are presented to show the
effectiveness of the proposed algorithm.
Abstract: Natural language processing systems pose a unique
challenge for software architectural design as system complexity has
increased continually and systems cannot be easily constructed from
loosely coupled modules. Lexical, syntactic, semantic, and pragmatic
aspects of linguistic information are tightly coupled in a manner that
requires separation of concerns in a special way in design,
implementation and maintenance. An aspect oriented software
architecture is proposed in this paper after critically reviewing
relevant architectural issues. For the purpose of this paper, the
syntactic aspect is characterized by an augmented context-free
grammar. The semantic aspect is composed of multiple perspectives
including denotational, operational, axiomatic and case frame
approaches. Case frame semantics matured in India from deep
thematic analysis. It is argued that lexical, syntactic, semantic and
pragmatic aspects work together in a mutually dependent way and
their synergy is best represented in the aspect oriented approach. The
software architecture is presented with an augmented Unified
Modeling Language.