Konstan, J. & Riedl, J. (2012),
'Recommender systems: from algorithms to user experience', User Modeling and User-Adapted Interaction
22
(1-2)
, 101-123
.
[BibTeX]
[Endnote]
Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
Garcia-Silva, A.; Corcho, O.; Alani, H. & Gomez-Perez, A. (2011),
'Review of the state of the art: Discovering and Associating Semantics to Tags in Folksonomies', Knowledge Engineering Review
26
(4)
.
[BibTeX]
[Endnote]
This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches.
Ricci, F.; Rokach, L.; Shapira, B. & Kantor, P. B., ed.
(2011),
Recommender Systems Handbook
, Springer
.
[BibTeX]
[Endnote]
Herrera, F.; Carmona, C.; González, P. & del Jesus, M. (2010),
'An overview on subgroup discovery: foundations and applications', Knowledge and Information Systems
, 1-31
.
[BibTeX]
[Endnote]
Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.
LIBRARYä, U. (2010),
'2Bibliometrics - an introduction'.
[BibTeX]
[Endnote]
Bibliometrics:
an overview
• Research impact can be measured in many ways: quantitative approaches
include publication counts, amount of research income, no of PhD students,
size of research group, no of PI projects, views and downloads of online
outputs, number of patents and licenses obtained, and others.
• Use of bibliometrics and citation analysis is only one of these quantitative
indicators.
• The ability to apply it and its importance in the overall assessment of research
varies from field to field
• Attempts at quantitative measures can be contrasted with the main alternative
assessment approach - qualitative peer-review in various forms
• The balance between use of bibliometrics and peer-review in assessing
academic performance at both the individual and unit levels is currently a “hot
topic” being played out locally, nationally and internationally
• This section provides an introductory overview of the field - others look in
more depth at: the key uses of bibliometrics for journal ranking and individual
assessment; the main metrics available; the main data sources and packaged
toolkits available
Brewster, C.; Jupp, S.; Luciano, J.; Shotton, D.; Stevens, R. D. & Zhang, Z. (2009),
'Issues in learning an ontology from text', BMC Bioinformatics
10 Suppl 5
.
[BibTeX]
[Endnote]
BACKGROUND: Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. RESULTS: Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/iffer/animalbehaviour/. CONCLUSION: We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.
Dong, X.; Chen, X.; Guan, Y.; Yu, Z. & Li, S. (2009),
An Overview of Learning to Rank for Information Retrieval., in
Mark Burgin; Masud H. Chowdhury; Chan H. Ham; Simone A. Ludwig; Weilian Su & Sumanth Yenduri, ed.,
'CSIE (3)'
, IEEE Computer Society,
, pp. 600-606
.
[BibTeX]
[Endnote]
Limpens, F.; Gandon, F. & Buffa, M. (2009),
'Linking Folksonomies and Ontologies for Supporting Knowledge Sharing: a State of the Art'
, Technical report, INRIA, Institut National de Recherche en Informatique et Automatique
.
[BibTeX]
[Endnote]
Social tagging systems have recently become very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations: tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This report compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.
Limpens, F.; Gandon, F. & Buffa, M. (2008),
'Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey', Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on
, 13-18
.
[BibTeX]
[Endnote]
Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.
Zhou, L. (2007),
'Ontology learning: state of the art and open issues', Information Technology and Management
8
(3)
, 241--252
.
[BibTeX]
[Endnote]
Abstract&&Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.
Cimiano, P.; Völker, J. & Studer, R. (2006),
'Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text', Information, Wissenschaft und Praxis
57
(6-7)
, 315-320
.
[BibTeX]
[Endnote]
Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general idea is that data and services are semantically described with respect to ontologies,which are formal specifications of a domain of interest, and can thus be shared and reused in a way such that the shared meaning specified by the ontology remains formally the same across different parties and applications. As the cost of creating ontologies is relatively high, different proposals have emerged for learning ontologies from structured and unstructured resources. In this article we examine the maturity of techniques for ontology learning from textual resources, addressing the question whether the state-of-the-art is mature enough to produce ontologies ‘on demand’.
Biemann, C. (2005),
'Ontology Learning from Text: A Survey of Methods.', LDV Forum
20
(2)
, 75-93
.
[BibTeX]
[Endnote]
Ganter, B.; Stumme, G. & Wille, R., ed.
(2005),
Formal Concept Analysis: Foundations and Applications
, Vol. 3626
, Springer
, Berlin/Heidelberg
.
[BibTeX]
[Endnote]
Gómez-Pérez, A. & Manzano-Macho, D. (2004),
'An overview of methods and tools for ontology learning from texts', The knowledge engineering review
19
(03)
, 187--212
.
[BibTeX]
[Endnote]
Ontology learning aims at reducing the time and efforts in the ontology development process. In recent years, several methods and tools have been proposed to speed up this process using different sources of information and different techniques. In this paper, we have reviewed 13 methods and 14 tools for semi-automatically building ontologies from texts and their relationships with the techniques each method follows. The methods have been grouped according to the main techniques followed and three groups have been identified: one based on linguistics, one on statistics, and one on machine learning. Regarding the tools, the criterion for grouping them, which has been the main aim of the tool, is to distinguish what elements of the ontology can be learned with each tool. According to this, we have identified three kinds of tools: tools for learning relations, tools for learning new concepts, and assisting tools for building up taxonomies.
Shamsfard, M. & Barforoush, A. A. (2003),
'The state of the art in ontology learning: a framework for comparison', The Knowledge Engineering Review
18
(04)
, 293--316
.
[BibTeX]
[Endnote]
In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some distinguishing factors and have many features in common. This paper presents the state of the art in ontology learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework answer to questions about what to learn, from where to learn and how to learn. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulted ontology and also the evaluation process. To extract the framework over 50 OL systems or modules from the recent workshops, conferences and published journals are studied and seven prominent of them with most differences are selected to be compared according to our framework. In this paper after a brief description of the seven selected systems we will describe the framework dimensions. Then we will place the representative ontology learning systems into our framework. At last we will describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features (dimensions’ values) to create or use an OL system for their own domain or application.
Staab, S. & Studer, R., ed.
(2003),
Handbook on Ontologies
, Springer
, Berlin, DE
.
[BibTeX]
[Endnote]
Fensel, D.; Wahlster, W.; Lieberman, H. & Hendler, J., ed.
(2002),
Spinning the Semantic Web: Bringing the World Wide Web
to Its Full Potential
, MIT Press
.
[BibTeX]
[Endnote]
Omelayenko, B. (2001),
Learning of Ontologies for the Web: the Analysis of Existent Approaches, in
'Proceedings of the International Workshop on Web Dynamics, held in conj. with the 8th International Conference on Database Theory (ICDT’01), London, UK'
.
[BibTeX]
[Endnote]
The next generation of the Web, called Semantic Web, has to improve the Web with semantic (ontological) page annotations to enable knowledge-level querying and searches. Manual construction of these ontologies will require tremendous efforts that force future integration of machine learning with knowledge acquisition to enable highly automated ontology learning. In the paper we present the state of the-art in the field of ontology learning from the Web to see how it can contribute to the task of semantic Web querying. We consider three components of the query processing system: natural language ontologies, domain ontologies and ontology instances. We discuss the requirements for machine learning algorithms to be applied for the learning of the ontologies of each type from the Web documents, and survey the existent ontology learning and other closely related approaches.
Jain, A. K.; Murty, M. N. & Flynn, P. J. (1999),
'Data Clustering: A Review', ACM Comput. Surv.
31
(3)
, 264--323
.
[BibTeX]
[Endnote]
Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
Jain, A. K. & Dubes, R. C.
(1988),
Algorithms for clustering data
, Prentice-Hall, Inc.
, Upper Saddle River, NJ, USA
.
[BibTeX]
[Endnote]