Web Intelligence

As web sites proliferate, offering more of the same, why does a customer choose one web site over the others? Among other factors, website intelligence offers a viable answer to this question. However, past studies fall short of providing a comprehensive conceptualization or effective metric of website intelligence, limiting our ability to enhance the intelligent aspects of web sites. Integrating prior research findings, we propose website intelligence as a second order construct consisting of three sub-dimensions of content, presentation, and interaction, and develop new measures for these dimensions. Further, we theorize the website intelligence construct as a mediator of the system quality and information quality effects on user perceptions of usefulness and ease of use, and test the proposed model using PLS on data collected from an experiment. The newly developed measure exhibits strong psychometric properties. The results largely support the proposed mediating role of website intelligence.

Straipsnyje aptariamas komandomis grįsto mokymo(si) strategijos elementų pritaikymas virtualiai aplinkai, taikant Web 5.0 saityno technologijas. Komandomis grįsto mokymo(si) strategija pasirinkta dėl jos efektyvumo aukštajame moksle. Web 5.0 saityno edukacinės technologijos pasirinktos dėl neabejotinos jų ateities perspektyvos. Tiek minėta strategija, tiek saitynas pasirinkti, nes laiduoja ateityje būtinų kompetencijų ugdymą. Atliekant tyrimą sukurtas ir įvertintas mokymo(si) strategijos prototipas. Apžvelgtos Web 5.0 saityno technologijų, susijusių su dirbtiniu intelektu, galimybės plėtojant sukurtą prototipą. PAGRINDINIAI ŽODŽIAI: komandomis grįsto mokymo(si) strategija, aukštasis mokslas, Web 5.0 saityno technologijos.

This article proposes a system architecture of a case based web services reasoner, CWSR, looks at the correspondence relationship between the main activities of CBR and those of web services, and proposes a unified approach for case-based web service discovery, composition and recommendation. The proposed approach will facilitate the research and development of web services, e-services, intelligent systems and business intelligence.

Introduction: In the last couple of decades, Amharic-English translation has greatly improved from a rule-based approach to contemporary systems that apply neural networks. Even after these advancements, problems remain because of the Amharic language's resource-scarce nature, such as inadequate datasets, tools for working with the language, and the intricate semantics and grammar of Amharic as compared to English. This systematic review seeks to analyze the evolution of the Amharic-English machine translation, the prominent ongoing difficulties, the noteworthy research undertakings, and the prospects of the research focus. Methods: This review uses a systematic approach to study the literature on Amharic-English machine translation. Important documents were retrieved from academic websites, and those with relevance to the methodologies of machine translation, language resources development, and evaluation practices were chosen. Primarily, the focus was on both statistical and neural machine translation models, especially those with transformer structures. The initial attempts to translate English to Amharic and vice-versa relied on statistic machine translation (SMT), which set the stage for the evolution to neural machine translation (NMT). The use of transformer models has impacted the accuracy and fluidity of translations tremendously. Still, there is a lack of sufficient parallel corpora, effective methods for tokenization of Amharic, and other resources. Recently, the focus has been on creating new datasets, improving token-level engineering, and modifying NMT models for Amharic's complex morphological structure. Discussion: The complete solutions for enhancing Amharic-English translation remain elusive and include the lack of sufficient data, semantic correspondence, and grammatical consistency within and across translations. Pursuable avenues include augmentation of data, tokenization on the language level, and incorporation of linguistic elements into the parallel corpora. In addition, creating effective evaluation frameworks along with comprehensive linguistic data is important for assessing and improving translation tools. With these changes, cross-cultural interaction and increasing accessibility to modern technologies will be achieved.

Information marketplaces are places where users search and retrieve information goods. Intelligent Agents could represent the participating entities in such places, i.e, assume the role of buyers and sellers of information products. In this paper, we introduce a finite horizon bargaining model between buyers and sellers. We examine the seller's side and define a method for the 'bargaining' deadline calculation based on Fuzzy-Logic (FL). Such deadline indicates the time for which it is profitable for a seller to participate in the bargaining procedure, i.e., the time threshold for his offers. We represent the seller's knowledge / policy adopting the Fuzzy Set Theory and provide a fuzzy inference engine for reasoning about the bargaining deadline. The result of the reasoning process defines the degree of patience of the seller agent, thus, affecting the time for which that seller participates in the bargaining game.

This paper describes iShakti, a real-world, Intelligent, Interactive and Adaptive Web application. At present, iShakti is deployed across 1000 rural kiosks in India, covering 5000 villages and reaching 1 million people. Further scale up is underway, expected to cover tens of thousands of villages within the next 2 years. iShakti is a 'virtual information and marketing channel', deploying leading-edge technology in a developing-world environment. It allows rich interactions with people in previously 'mediadark' regions, with easy access to high-value community development services coupled with engaging and scalable market and brand development activities. The impact is already being felt -iShakti is giving some of the most deprived and disempowered people more choice and control over their lives, and providing significant independent revenue for the iShakti entrepreneurs. Computational Intelligence is both in the design as well as the personalisation and synchronisation algorithms. The project was nominated as a finalist of the Stockholm Challenge (Economic Development category), an international award for ICT projects in 'underserved' regions of the world [1].

Domain ontology can help in information retrieval from documents. But ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.

In this paper we describe an information extraction and text mining system which identifies key information components from text documents. The information components are centered on domain entities and their relationships. The components mined from a repository are chained using an n-gram-based algorithm. The information chains provide a comprehensive view of the collection and can be also used for inferential reasoning.

In this paper we have proposed a system that performs both ontology-based text information extraction and ontology update using the extracted information. The system employs text-mining techniques to mine information from text documents guided by an underlying ontology. It also enhances the existing ontology with new concepts and their descriptors which may be precise and/ or imprecise, mined from the text. All extracted information related to concepts and concept descriptors are also stored in a structured knowledge base.

Recommender systems firstly appeared few years ago back in the early 1990's and tend to be on very high demand. Nowadays, they mostly can be found on every website, because every website has something, a similar item, to recommend. Since today, state-of-the-art algorithms have also applied to recommender systems in the real-world applications. In this research methodology, I further propose a state-of-the-art algorithm which extend KNN so that better recommendations are made through that. The particular algorithm implements the distance radius a greedily reduced content is recommended to the end user. Experimental results have shown that the research methodology outperforms simpler forms of the same algorithms and baseline algorithms as well.

On the market there are many commercial web classification services and a few publicly available web directory services. Unfortunately they mostly focus on English-speaking web sites, making them unsuitable for other languages in terms of classification reliability and coverage. This paper covers the design and implementation of a web-based classification tool for TLDs (Top Level Domain). Each domain is classified by analysing the main domain web site, and organised it in categories according to its content. The tool has been successfully validated by classifying all the registered .it Internet domains, whose results are presented in this paper.

Methodology, in intelligence, consists of the methods used to make decisions about threats, especially in the intelligence analysis discipline. The enormous amount of information collected by intelligence agencies often puts them in the inability to analyze them all. The US intelligence community collects over one billion daily information. The nature and characteristics of the information gathered as well as their credibility also have an impact on the intelligence analysis. Clark proposed a methodology for analyzing information by addressing the target-centric intelligence cycle as an alternative to the traditional information cycle.

Systems for Ambient Intelligence environments demand at some stage a service composition task, as a mean of adaptability to the context changes. However, in contrast to what ubiquitous and pervasive computing propose, users generally find themselves involved in the service composition task, by selecting or deciding what to compose and how. This paper proposes the use of Intelligent Agents for the automation of the composition task, providing transparency from the user point of view.

Recent advances in the standardization of knowledge representation languages to realize the Semantic Web as well as advances in natural language processing techniques have resulted in increased commercial efforts to create so called annotation services which aim to annotate web content with metadata. Description Logics-based metadata are symbolic descriptions of some content, e.g., web content, with semantics defined through so called ontologies. Ontologies play a central role for the definition of semantics in the context of the Semantic Web. Currently, annotation services can be exploited to build content-driven applications. However for such applications to offer more valuable functionality, current annotation services should provide for richer metadata. Rich metadata describe a deeper and more abstract level of information through complex relational structures. In this paper we describe use cases that require richer metadata than currently provided by annotation services and describe a contentdriven application that implements them. The use cases focus on geography-aware multimedia navigation, content activation and retrieval of semantically related multimedia. Furthermore a declarative process to extract richer metadata is introduced.

The paper describes how interpretations of multimedia documents can be formally derived using abduction over domain knowledge represented in an ontology. The approach uses an expressive ontology specification language, namely description logics in combination with logic programming rules, and formalizes the multimedia interpretation process using a combined abduction and deduction operation. We describe how the observables as well as the space of abducibles can be formally defined. The approach is evaluated using examples from text processing, but can also be applied to interpret content in other modalities. This paper has been partially supported by the projects BOEMIE and CASAM (FP6-027538 and FP7-217061, respectively).

The paper describes how interpretations of multimedia documents can be formally derived using abduction over domain knowledge represented in an ontology. The approach uses an expressive ontology specification language, namely description logics in combination with logic programming rules, and formalizes the multimedia interpretation process using a combined abduction and deduction operation. We describe how the observables as well as the space of abducibles can be formally defined. The approach is evaluated using examples from text processing, but can also be applied to interpret content in other modalities. This paper has been partially supported by the projects BOEMIE and CASAM (FP6-027538 and FP7-217061, respectively).

Recent advances in the standardization of knowledge representation languages to realize the Semantic Web as well as advances in natural language processing techniques have resulted in increased commercial efforts to create so called annotation services which aim to annotate web content with metadata. Description Logics-based metadata are symbolic descriptions of some content, e.g., web content, with semantics defined through so called ontologies. Ontologies play a central role for the definition of semantics in the context of the Semantic Web. Currently, annotation services can be exploited to build content-driven applications. However for such applications to offer more valuable functionality, current annotation services should provide for richer metadata. Rich metadata describe a deeper and more abstract level of information through complex relational structures. In this paper we describe use cases that require richer metadata than currently provided by annotation services and describe a contentdriven application that implements them. The use cases focus on geography-aware multimedia navigation, content activation and retrieval of semantically related multimedia. Furthermore a declarative process to extract richer metadata is introduced.

The automatic detection of emotions in Twitter posts is a challenging task due to the informal nature of the language used in this platform. In this paper, we propose a methodology for expanding the NRC word-emotion association lexicon for the language used in Twitter. We perform this expansion using multi-label classification of words and compare different wordlevel features extracted from unlabelled tweets such as unigrams, Brown clusters, POS tags, and word2vec embeddings. The results show that the expanded lexicon achieves major improvements over the original lexicon when classifying tweets into emotional categories. In contrast to previous work, our methodology does not depend on tweets annotated with emotional hashtags, thus enabling the identification of emotional words from any domainspecific collection using unlabelled tweets.

Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The founda...

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Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard.

The Information on web resources is increasing day by day because of increment of user’s interaction and contribution in web resources. It is becoming a challenge to collect relevant and useful information from this huge amount of data because most the data is not interrelated or organize. Semantic web allows collecting meaningful information from bulk amount of data, there for semantic web helped a lot to optimize the searching technique and facilitate the use to get useful information. The paper discussed some research performed on semantic web and some tools and platform developed by using semantic web approaches in past few years.

Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.

We present two secure real-time video conferencing solutions with JPEG coding using a cipher engine based on the Blowfish algorithm. One of these solutions is developed from Java media framework (JMF). In this case, our solution consists of encrypting frames coded with JPEG that are later encapsulated in packets of the RTP protocol (H. Schulzrinne et al., 1996) (real time transport protocol). Our cipher system is based on the Blowfish algorithm (B. Schneier, 1994) of the Cryptix open source cryptographic software library. Another solution is developed from VIC (video conferencing tool) (S. McCanne and V. Jacobson, 1995). In this case, our solution consists of encrypting the UDP packets that contain the JPEG coded frames incorporated in RTP packets. In this paper we analyse the solutions proposed and test the implementations developed in different data networks (LAN/WLAN, frame relay)

With the recent growth of the Social Web, an emerging challenge is how we can integrate information from the heterogeneity of current Social Web sites to improve semantic access to the information and knowledge across the entire World Wide Web, the Web. Interoperability across the Social Web sites make the simplest of inferences based on data from different sites challenging. Even if such data were interoperable across multiple Social Web sites, the ability of meaningful inferences of a collective intelligence [1] system depends on both its ability to marshal such semantic data, as well as its ability to accurately understand and precisely respond to queries from its users. This paper presents the architecture for Social Sifter, an agent-based, collective intelligence system for assimilating information and knowledge across the Social Web. A health recommender system prototype was developed using the Social Sifter architecture, which recommends treatments, prevention advice, therapi...

Product reviews written by on-line shoppers is a valuable source of information for potential new customers who desire to make an informed purchase decision. Manually processing quite a few dozens, or even hundreds, of reviews for a single product is tedious and time consuming. Although there exist mature and generic text summarization techniques, they are focused primarily on article type content and do not perform well on short and usually repetitive snippets of text found at on-line shops. In this paper, we propose MOpiS, a multiple opinion summarization algorithm that generates improved summaries of product reviews by taking into consideration metadata information that usually accompanies the on-line review text. We demonstrate the effectiveness of our approach with experimental results.

We use Conceptual Graphs (CGs) to model web content extraction rules (CG-Wrappers). The approach presented incorporates all major existing extraction techniques and allows the definition of synergies of cooperative wrappers for handling complex extraction task, without requiring programming.

Abstract. Modern successful on-line shops and product compari-son sites allow consumers to express their opinion on products and services they purchased. Although such information can be useful to other potential customers, reading and mentally processing quite a few ...

In this paper, some auction systems are applied to the parking reservation system which would exert an important role in the next generation traffic systems. As an introduction evaluation of the auction systems for the parking reservation, we compare the results in case of a simultaneous auction and sequential one, and examine the influences of the strategy. Although the limited numerical experiment under the assumption that the number of parking space is only three, time zone is three, and also the total number of bidders are 25, the following results are obtained; 1) electricity trading makes a profit to both the parking manager and users 2) the average of the parking revenues would be the highest when the reservation price is a little higher than the expect bidding price.

With the recent growth of the Social Web, an emerging challenge is how we can integrate information from the heterogeneity of current Social Web sites to improve semantic access to the information and knowledge across the entire World Wide Web, the Web. Interoperability across the Social Web sites make the simplest of inferences based on data from different sites challenging. Even if such data were interoperable across multiple Social Web sites, the ability of meaningful inferences of a collective intelligence [1] system depends on both its ability to marshal such semantic data, as well as its ability to accurately understand and precisely respond to queries from its users. This paper presents the architecture for Social Sifter, an agent-based, collective intelligence system for assimilating information and knowledge across the Social Web. A health recommender system prototype was developed using the Social Sifter architecture, which recommends treatments, prevention advice, therapies for ailments, and doctors and hospitals based on shared experiences available on the Social Web.

Advent of technologies like semantic web, multi-agent systems, web mining has changed the internet as knowledge provider. Web personalization offers a solution to the information overload problem in current web by providing users a personalized experience, considering their interest, behavior, context and emotions. Semantic web technology is based on use of software agents, ontologies and reasoning to add meaning to web information. An important technology for achieving personalization is the use of independent intelligent software agents. This work reviews, web personalization in the light of semantic web and software agent technology. A comparative study of recent works in the domain of web personalization has been carried out for this purpose. This review highlights ample scope for application of intelligent agents in the web personalization domain for solving many existing issues like personalized content management, user profile learning, modeling and adaptive interactions with...

Context-aware computing is widely accepted as a promising paradigm to enable seamless computing. Several middlewares and ontology-based models for describing context information have been developed in order to support contextaware applications. However, the context variability, which refers to the possibility to infer or interpret different context information from different perspectives, has been neglected in the existing context modeling approaches. This paper presents an approach for context-aware software development based on a flexible product line based context model which significantly enhances reusability of context information by providing context variability constructs to satisfy different application needs.

Interception of a data stream is central to any intelligent and dynamic processing of web information. It is perhaps as fundamental to Internet services' overall architecture as the design of disk scheduling to the conventional machine architecture. In this paper we discuss an IPv6 based indexing protocol that can facilitate random access into multilevel hierarchically encoded content streams and provide serious performance boost to web content processing.