Abstract: In order to survive on the market, companies must
constantly develop improved and new products. These products are
designed to serve the needs of their customers in the best possible
way. The creation of new products is also called innovation and is
primarily driven by a company’s internal research and development
department. However, a new approach has been taking place for some
years now, involving external knowledge in the innovation process.
This approach is called open innovation and identifies customer
knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its
initial phase, the Ideation phase. For this purpose, the social media
posts are semantically structured with the help of an ontology and
the authors are evaluated using graph-theoretical metrics such as
density. For the structuring and evaluation of relevant social media
posts, we also use the findings of Natural Language Processing, e.
g. Named Entity Recognition, specific dictionaries, Triple Tagger
and Part-of-Speech-Tagger. The selection and evaluation of the tools
used are discussed in this paper. Using our ontology and metrics
to structure social media posts enables users to semantically search
these posts for new product ideas and thus gain an improved insight
into the external sources such as customer needs.
Abstract: This paper presents 15-year trends for scientific studies in a scientific database considering 3D and vehicle words. Two words are selected to find their associated publications in IEEE scholar database. Both of keywords are entered individually for the years 2002, 2012, and 2016 on the database to identify the preferred subjects of researchers in same years. We have classified closer research fields after searching and listing. Three years (2002, 2012, and 2016) have been investigated to figure out progress in specified time intervals. The first one is assumed as the initial progress in between 2002-2012, and the second one is in 2012-2016 that is fast development duration. We have found very interesting and beneficial results to understand the scholars’ research field preferences for a decade. This information will be highly desirable in smart city-based research purposes consisting of 3D and vehicle-related issues.
Abstract: There are real needs to integrate types of Open
Educational Resources (OER) with an intelligent system to extract
information and knowledge in the semantic searching level. The
needs came because most of current learning standard adopted web
based learning and the e-learning systems do not always serve all
educational goals. Semantic Web systems provide educators,
students, and researchers with intelligent queries based on a semantic
knowledge management learning system. An ontology-based learning
system is an advanced system, where ontology plays the core of the
semantic web in a smart learning environment. The objective of this
paper is to discuss the potentials of ontologies and mapping different
kinds of ontologies; heterogeneous or homogenous to manage and
control different types of Open Educational Resources. The important
contribution of this research is that it uses logical rules and
conceptual relations to map between ontologies of different
educational resources. We expect from this methodology to establish
an intelligent educational system supporting student tutoring, self and
lifelong learning system.
Abstract: Search is the most obvious application of information
retrieval. The variety of widely obtainable biomedical data is
enormous and is expanding fast. This expansion makes the existing
techniques are not enough to extract the most interesting patterns
from the collection as per the user requirement. Recent researches are
concentrating more on semantic based searching than the traditional
term based searches. Algorithms for semantic searches are
implemented based on the relations exist between the words of the
documents. Ontologies are used as domain knowledge for identifying
the semantic relations as well as to structure the data for effective
information retrieval. Annotation of data with concepts of ontology is
one of the wide-ranging practices for clustering the documents. In
this paper, indexing based on concept and annotation are proposed
for clustering the biomedical documents. Fuzzy c-means (FCM)
clustering algorithm is used to cluster the documents. The
performances of the proposed methods are analyzed with traditional
term based clustering for PubMed articles in five different diseases
communities. The experimental results show that the proposed
methods outperform the term based fuzzy clustering.
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: Chinese Idioms are a type of traditional Chinese idiomatic
expressions with specific meanings and stereotypes structure
which are widely used in classical Chinese and are still common in
vernacular written and spoken Chinese today. Currently, Chinese
Idioms are retrieved in glossary with key character or key word in
morphology or pronunciation index that can not meet the need of
searching semantically. OCIRS is proposed to search the desired
idiom in the case of users only knowing its meaning without any key
character or key word. The user-s request in a sentence or phrase will
be grammatically analyzed in advance by word segmentation, key
word extraction and semantic similarity computation, thus can be
mapped to the idiom domain ontology which is constructed to provide
ample semantic relations and to facilitate description logics-based
reasoning for idiom retrieval. The experimental evaluation shows that
OCIRS realizes the function of searching idioms via semantics, obtaining
preliminary achievement as requested by the users.
Abstract: This paper focuses on a novel method for semantic
searching and retrieval of information about learning materials.
Metametadata encapsulate metadata instances by using the properties
and attributes provided by ontologies rather than describing learning
objects. A novel metametadata taxonomy has been developed which
provides the basis for a semantic search engine to extract, match and
map queries to retrieve relevant results. The use of ontological views
is a foundation for viewing the pedagogical content of metadata
extracted from learning objects by using the pedagogical attributes
from the metametadata taxonomy. Using the ontological approach
and metametadata (based on the metametadata taxonomy) we present
a novel semantic searching mechanism.These three strands – the
taxonomy, the ontological views, and the search algorithm – are
incorporated into a novel architecture (OMESCOD) which has been
implemented.
Abstract: Semantic Web Technologies enable machines to
interpret data published in a machine-interpretable form on the web.
At the present time, only human beings are able to understand the
product information published online. The emerging semantic Web
technologies have the potential to deeply influence the further
development of the Internet Economy. In this paper we propose a
scenario based research approach to predict the effects of these new
technologies on electronic markets and business models of traders
and intermediaries and customers. Over 300 million searches are
conducted everyday on the Internet by people trying to find what
they need. A majority of these searches are in the domain of
consumer ecommerce, where a web user is looking for something to
buy. This represents a huge cost in terms of people hours and an
enormous drain of resources. Agent enabled semantic search will
have a dramatic impact on the precision of these searches. It will
reduce and possibly eliminate information asymmetry where a better
informed buyer gets the best value. By impacting this key
determinant of market prices semantic web will foster the evolution
of different business and economic models. We submit that there is a
need for developing these futuristic models based on our current
understanding of e-commerce models and nascent semantic web
technologies. We believe these business models will encourage
mainstream web developers and businesses to join the “semantic web
revolution."
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: 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.