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In our work we investigate the relationship between semantic textual similarity and credibility of the individual sentence. For this purpose we performed an experiment to create a corpus of sentences with known credibility value. Then we calculated semantic similarity of each pair of sentences and tried to assess the credibility of a sentence using similar one. Performing comparison, we use idea to...
This paper presents an investigation into the Website Boundary Detection (WBD) problem in the dynamic context. In the dynamic context (as opposed to the static context) the web data to be considered is not fully available prior to the start of the website boundary detection process. The dynamic approaches presented in this paper are all probabilistic and based on the concept of random walks, three...
This paper presents a highly parallel solution for cross-document co reference resolution, which can deal with billions of documents that exist in the current web. At the core of our solution lies a novel algorithm for community detection in large scale graphs. We operate on graphs which we construct by representing documents' keywords as nodes and the colocation of those keywords in a document as...
Keeping up with rapid advances in research in various fields of Engineering and Technology is a challenging task. Decision makers including academics, program managers, venture capital investors, industry leaders and funding agencies not only need to be abreast of latest developments but also be able to assess the effect of growth in certain areas on their core business. Though analyst agencies like...
Being transmitted as part of numerous Internet services, geo location data is increasingly bringing hints of people's real-world activities into Internet traffic. This paper focuses on the discovery of key properties that motivate personal activities - locational interests. We propose and design GeoEcho, a mobile traffic analysis system that extracts and analyses a wealth of latitude-longitude geotag...
Relating, connecting and navigating between concepts represent a major challenge for machine intelligence. On the other hand, collaborative repositories provide a large base of knowledge already filtered, structured, linked and meaningful from a human semantic point of view. Although these repositories are machine accessible, they have no formal explicit semantic tagging to help for automatic navigation...
This study describes and evaluates a novel trust model for a range of collaborative applications. The model assumes that humans routinely choose to trust their peers by relying on few recurrent presumptions, which are domain independent and which form a recognisable trust expertise. We refer to these presumptions as trust schemes, a specialised version of Walton's argumentation schemes. Evidence is...
In recent years, the development of legal ontologies has increased significantly with the diversity of their applications known as complicated due to the complexity of their domain. In the preliminary part of this paper, we introduce the major steps in the learning process, then we present some works interested in Arabic ontology learning. The rest of the paper serves to propose our approach for ontology...
With increasing amount of information (video, text) being available today, it has become non-trivial to develop techniques to categorize documents into contextually meaningful classes. The information as available in the documents is composed of sequence of events termed as patterns. It is evident to know the important trends as observed from patterns that are emerging over a specific time period...
The use of word senses in place of surface word forms has been shown to improve performance on many computational tasks, including intelligent web search. In this paper we propose a novel approach to automatic discovery of word senses from raw text, a task referred to as Word Sense Induction (WSI). Almost all the WSI approaches proposed in the literature dealt with monolingual data and only very few...
Existing rating and reviewing schemes typically come in the in the flavor of a single rating and/or a textual review. While a single judgment evaluating the overall quality of a product is of limited significance, textual customer reviews typically deliver more informative feedback at the attribute level. However, reading and comparing them to extract relevant information is time-consuming and mentally-demanding...
In the absence of legal authorities and enforcement mechanisms in open e-marketplaces, it is extremely challenging for a user to validate the quality of opinions (i.e. Ratings and reviews) of products or services provided by other users (referred as advisers). Rationally, advisers tend to be reluctant to share their truthful experience with others. In this paper, we propose an adaptive incentive mechanism,...
Analyzing human behaviors during emergency situations contributes to build efficient emergency management plans. Indeed, research shows that emotions have a major influence on human behavior particularly to respond to highly emotive events such as those happening in emergency situations. Therefore, it is recognized that they are necessary to produce human-like behaviors in artificial agents. In this...
This paper tackles the issue of ambient systems adaptation to users' needs while the environment and users' preferences evolve continuously. We propose the adaptive multi-agent system Amadeus whose goal is to learn from users' actions and contexts how to perform actions on behalf of the users in similar contexts. However, considering the possible changes of users preferences, a previously learnt behaviour...
We propose extensions of the classical JSM-method and the Naive Bayesian classifier for the case of triadic relational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification...
Information overload is an increasing challenge for the enterprise knowledge worker. Traditional information retrieval, i.e. Search-based approaches for knowledge management in the enterprise are under strain because users do not have the time to search, often they are not even aware that material relevant to they current needs exists. Neither do they have the time to track the various external news...
Unlike traditional recommender systems, which make recommendations only by using the relation between users and items, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process. One problem of context-aware approaches is that it is required techniques to extract such additional information in an automatic manner. In this...
Dynamic collaborative environments enabled with multiple computational devices, software and files have always had difficulty sharing these resources in a practical way. Traditionally, people involved in environments like those are forced to waste time and energy trying to locate a resource they do not even know if it exists inside the organization. Those resources can be devices, which make collaborators...
In recent years, the study of norm-regulated multi-agent systems (MAS) has attracted a lot of attention. The Kanger-Lindahl theory of normative positions has great potential of serving as the logical foundation for normative systems for MAS, but its generality, which allows for great freedom when interpreting the theory, may also become a challenge for practical applications. As an important step...
In heterogeneous and open multi-agent systems the selection of future partners have been an issue that reputation systems are trying to solve. However, nowadays reputation systems propose the selection of such partners based solely on the reputation the partners have as service providers. In this paper we argue that an agent that intends to participate in future interactions may concern not only about...
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