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A great share of current sentiment analysis techniques is based on special purpose lexicons providing information about the semantic orientation (e.g. positive, negative, neutral) of its entries. Due to the high labor costs of manually assembling such resources, recent work has focused on automatically inducing the polarity of given terms. We follow this line of work while focusing on the domain of...
Reuse is an important mechanism for improving the efficiency of software development. For Internet-scale software produced through service composition, the simple reuse granularity at service is often inefficient due to the large number of available services. This paper proposes a novel architecture which enables efficient reuse of process fragments. In the proposed architecture, services are organized...
In the article there is presented comparison of overlapping clustering methods for data mining of DBLP datasets. For the analysis, the DBLP data sets were pre-processed, while each journal has been assigned attributes, defined by its topics. The data collection can be described as vague and uncertain; obtained clusters and applied queries do not necessarily have crisp boundaries. The authors presented...
Clustering- an important data mining task, which groups the data on the basis of similarities among the data, can be divided into two broad categories, partitional clustering and hierarchal. We combine these two methods and propose a novel clustering algorithm called Hierarchical Particle Swarm Optimization (HPSO) data clustering. The proposed algorithm exploits the swarm intelligence of cooperating...
Summary form only given. Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This talk focuses on...
Computational trust systems are getting popular in several domains such as social networks, grid computing and business-to-business systems. However, the estimation of the trustworthiness of agents is not trivial in scenarios where the existing trust evidences are scarce. We propose an online, situation-aware trust model that uses the information gain metric to dynamically extract tendencies of failure...
The article discusses the potential methods and benefits of the analysis of social networks hidden in the enterprise and personal email archives. A proof-of concept prototype was developed. Social network extraction and the spreading activation algorithm are discussed and evaluated.
Due to the complexity of topical opinion retrieval systems, standard measures, such as MAP or precision, do not fully succeed in assessing their performances. In this paper we introduce an evaluation framework based on artificially defined opinion classifiers. Using a Monte Carlo sampling, we perturb a relevance ranking by the outcomes of these classifiers and analyse how the opinion retrieval performance...
Research on opinion detection has shown that a large number of opinion-labeled data are necessary for capturing subtle opinions. However, opinion-labeled data, especially at the sub-document level, are often limited. This paper describes the application of Semi-Supervised Learning (SSL) to automatically produce more labeled data and explores the potential of SSL to improve transfer of labeled data...
LBD tools enable the establishment of relationships between concepts appearing in scientific articles in the biomedical field and the generation of new hypotheses via the examination of these existing relationships. In this paper, we study the effectiveness of generally accepted grouping and eliminating logics used in LBD tools. This work is performed in the context of Lit2Info, a system that we have...
Opinion mining is of great significance in the analysis of user generated content. While there is some progress in supervised classification of opinion, the unsupervised learning of product features has drawn less attention. Unlike previous approaches based on basic syntactic pattern, our product feature mining utilizes syntactic dependency knowledge in a novel way by discriminating nominal and non-nominal...
In W3C's Rule Interchange Format (RIF), F-Logic rules have received considerable attention as a major logical rule formalism, while combinations of rules with Description Logic (DL) ontologies in RIF, let alone with F-Logic rules, are far less developed. To mend this, we first present F-Logic# knowledge bases, a framework based on the semantics of the well-investigated dl-programs, that provides a...
In this paper, we present a Hierarchical Fuzzy Clustering algorithm which uses domain knowledge to automatically determine the number of clusters and their initial values. The algorithm is applied on a collection of web pages and the results are compared with existing algorithms in the literature.
The following topics are dealt with: Web intelligence; World Wide Web; Web information retrieval; information filtering; ontology engineering; semantic Web; Web mining; social networks; ubiquitous intelligence; and Web agents.
Unlike existing studies dealing with the selection of Bitmap Join Indexes for star join queries optimization, this paper presents three original features. The first one consists in addressing the problem with ant based approach that is more robust than the simple heuristic algorithms, which are usually used in the related works. The second interesting novelty resides in the metric used to prune the...
In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a ``stakeholder'' is a participant in an event described in a news article who should have some relationships with other participants in the article...
Recent years have seen a huge increase in the amount of publicly-available information relevant to drug discovery, including online databases of compound and bioassay information; scholarly publications linking compounds with genes, targets and diseases; and predictive models that can suggest new links between compounds, genes, targets and diseases. However, there is a lack of tools and methods to...
Representing meaning is a major challenge facing Web 3.0. However, it is extremely difficult to excavate the meaning of a target concept from textual data as there is no one-to-one correspondence between the textual unit in which the target concept is embedded and the conceptual content that we would like to excavate. In this paper, we propose one possible approach for addressing this challenge, by...
In this article we describe the implementation of a diversified investment strategy using 25 intelligent agents. Each agent utilizes several data mining models and other artificial intelligence techniques to autonomously day trade an American stock. The agents were individually tested with out-of-sample data corresponding to the period between February of 2006 and June of 2010, and most achieved an...
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