79,681 research outputs found
Enhancing Undergraduate AI Courses through Machine Learning Projects
It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
Collaborative editing of knowledge resources for cross-lingual text mining
The need to smoothly deal with textual documents expressed in different languages is increasingly becoming a relevant issue in modern text mining environments. Recently the research on this field has been considerably fostered by the necessity for Web users to easily search and browse the growing amount of heterogeneous multilingual contents available on-line as well as by the related spread of the Semantic Web. A common approach to cross-lingual text mining relies on the exploitation of sets of properly structured multilingual knowledge resources. The involvement of huge communities of users spread over different locations represents a valuable aid to create, enrich, and refine these knowledge resources. Collaborative editing Web environments are usually exploited to this purpose.
This thesis analyzes the features of several knowledge editing tools, both semantic wikis and ontology editors, and discusses the main challenges related to the design and development of this kind of tools. Subsequently, it presents the design, implementation, and evaluation of the Wikyoto Knowledge Editor, called also Wikyoto. Wikyoto is the collaborative editing Web environment that enables Web users lacking any knowledge engineering background to edit the multilingual network of knowledge resources exploited by KYOTO, a cross-lingual text mining system developed in the context of the KYOTO European Project.
To experiment real benefits from social editing of knowledge resources, it is important to provide common Web users with simplified and intuitive interfaces and interaction patterns. Users need to be motivated and properly driven so as to supply information useful for cross-lingual text mining. In addition, the management and coordination of their concurrent editing actions involve relevant technical issues.
In the design of Wikyoto, all these requirements have been considered together with the structure and the set of knowledge resources exploited by KYOTO. Wikyoto aims at enabling common Web users to formalize cross-lingual knowledge by exploiting simplified language-driven interactions. At the same time, Wikyoto generates the set of complex knowledge structures needed by computers to mine information from textual contents. The learning curve of Wikyoto has been kept as shallow as possible by hiding the complexity of the knowledge structures to the users. This goal has been pursued by both enhancing the simplicity and interactivity of knowledge editing patterns and by using natural language interviews to carry out the most complex knowledge editing tasks. In this context, TMEKO, a methodology useful to support users to easily formalize cross-lingual information by natural language interviews has been defined. The collaborative creation of knowledge resources has been evaluated in Wikyoto
Computer-based library or computer-based learning?
Traditionally, libraries have played the role of repository of published information resources and, more recently,
gateway to online subscription databases. The library online catalog and digital library interface serve an
intermediary function to help users locate information resources available through the library. With competition from Web search engines and Web portals of various kinds available for free, the library has to step up to play a more active role as guide and coach to help users make use of information resources for learning or to accomplish particular tasks. It is no longer sufficient for computer-based library systems to provide just search and access functions. They must provide the functionality and environment to support learning and become computer-based learning systems. This paper examines the kind of learning support that can be incorporated in library online catalogs and digital libraries, including 1) enhanced support for information browsing and synthesis through linking by shared meta-data, references and concepts; 2) visualization of related information; 3) adoption of Library 2.0 and social technologies; 4) adoption of Library 3.0 technologies including intelligent processing and text mining
An integrated ranking algorithm for efficient information computing in social networks
Social networks have ensured the expanding disproportion between the face of
WWW stored traditionally in search engine repositories and the actual ever
changing face of Web. Exponential growth of web users and the ease with which
they can upload contents on web highlights the need of content controls on
material published on the web. As definition of search is changing,
socially-enhanced interactive search methodologies are the need of the hour.
Ranking is pivotal for efficient web search as the search performance mainly
depends upon the ranking results. In this paper new integrated ranking model
based on fused rank of web object based on popularity factor earned over only
valid interlinks from multiple social forums is proposed. This model identifies
relationships between web objects in separate social networks based on the
object inheritance graph. Experimental study indicates the effectiveness of
proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC),
Vol.3, No.1, March 201
Web Site Personalization based on Link Analysis and Navigational Patterns
The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users’ needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next” pages to users based on their current visit and the past users’ navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important – in terms of PageRank authority score – pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web site’s navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational sub-graphs for online web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches
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