A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment

A Service Ecosystem is a biological view of the business and software environment, which is comprised of a Service Use Ecosystem and a Service Supply Ecosystem. Service matchmakers play an important role in ensuring the connectivity between the two ecosystems. Current matchmakers attempt to employ ontologies to disambiguate service consumers’ service queries by semantically classifying service entities and providing a series of human computer interactions to service consumers. However, the lack of relevant service domain knowledge and the wrong service queries could prevent the semantic service matchmakers from seeking the service concepts that can be used to correctly represent service requests. To resolve this issue, in this paper, we propose the framework of a service concept recommendation system, which is built upon a semantic similarity model.This system can be employed to seek the concepts used to correctly represent service consumers’ requests, when a semantic service matchmaker finds that the service concepts that are eventually retrieved cannot match the service requests. Whilst many similar semantic similarity models have been developed to date, most of them focus on distance-based measures for the semantic network environment and ignore content-based measures for the ontology environment. For the ontology environment in which concepts are defined with sufficient datatype properties, object properties, and restrictions etc., the content of concepts should be regarded as an important factor in concept similarity measures. Hence, we present a novel semantic similarity model for the service ontology environment. The technical details and evaluation details of the framework are discussed in this paper

A customized semantic service retrieval methodology for the digital ecosystems environment

With the emergence of the Web and its pervasive intrusion on individuals, organizations, businesses etc., people now realize that they are living in a digital environment analogous to the ecological ecosystem. Consequently, no individual or organization can ignore the huge impact of the Web on social well-being, growth and prosperity, or the changes that it has brought about to the world economy, transforming it from a self-contained, isolated, and static environment to an open, connected, dynamic environment. Recently, the European Union initiated a research vision in relation to this ubiquitous digital environment, known as Digital (Business) Ecosystems. In the Digital Ecosystems environment, there exist ubiquitous and heterogeneous species, and ubiquitous, heterogeneous, context-dependent and dynamic services provided or requested by species. Nevertheless, existing commercial search engines lack sufficient semantic supports, which cannot be employed to disambiguate user queries and cannot provide trustworthy and reliable service retrieval. Furthermore, current semantic service retrieval research focuses on service retrieval in the Web service field, which cannot provide requested service retrieval functions that take into account the features of Digital Ecosystem services. Hence, in this thesis, we propose a customized semantic service retrieval methodology, enabling trustworthy and reliable service retrieval in the Digital Ecosystems environment, by considering the heterogeneous, context-dependent and dynamic nature of services and the heterogeneous and dynamic nature of service providers and service requesters in Digital Ecosystems.The customized semantic service retrieval methodology comprises: 1) a service information discovery, annotation and classification methodology; 2) a service retrieval methodology; 3) a service concept recommendation methodology; 4) a quality of service (QoS) evaluation and service ranking methodology; and 5) a service domain knowledge updating, and service-provider-based Service Description Entity (SDE) metadata publishing, maintenance and classification methodology.The service information discovery, annotation and classification methodology is designed for discovering ubiquitous service information from the Web, annotating the discovered service information with ontology mark-up languages, and classifying the annotated service information by means of specific service domain knowledge, taking into account the heterogeneous and context-dependent nature of Digital Ecosystem services and the heterogeneous nature of service providers. The methodology is realized by the prototype of a Semantic Crawler, the aim of which is to discover service advertisements and service provider profiles from webpages, and annotating the information with service domain ontologies.The service retrieval methodology enables service requesters to precisely retrieve the annotated service information, taking into account the heterogeneous nature of Digital Ecosystem service requesters. The methodology is presented by the prototype of a Service Search Engine. Since service requesters can be divided according to the group which has relevant knowledge with regard to their service requests, and the group which does not have relevant knowledge with regard to their service requests, we respectively provide two different service retrieval modules. The module for the first group enables service requesters to directly retrieve service information by querying its attributes. The module for the second group enables service requesters to interact with the search engine to denote their queries by means of service domain knowledge, and then retrieve service information based on the denoted queries.The service concept recommendation methodology concerns the issue of incomplete or incorrect queries. The methodology enables the search engine to recommend relevant concepts to service requesters, once they find that the service concepts eventually selected cannot be used to denote their service requests. We premise that there is some extent of overlap between the selected concepts and the concepts denoting service requests, as a result of the impact of service requesters’ understandings of service requests on the selected concepts by a series of human-computer interactions. Therefore, a semantic similarity model is designed that seeks semantically similar concepts based on selected concepts.The QoS evaluation and service ranking methodology is proposed to allow service requesters to evaluate the trustworthiness of a service advertisement and rank retrieved service advertisements based on their QoS values, taking into account the contextdependent nature of services in Digital Ecosystems. The core of this methodology is an extended CCCI (Correlation of Interaction, Correlation of Criterion, Clarity of Criterion, and Importance of Criterion) metrics, which allows a service requester to evaluate the performance of a service provider in a service transaction based on QoS evaluation criteria in a specific service domain. The evaluation result is then incorporated with the previous results to produce the eventual QoS value of the service advertisement in a service domain. Service requesters can rank service advertisements by considering their QoS values under each criterion in a service domain.The methodology for service domain knowledge updating, service-provider-based SDE metadata publishing, maintenance, and classification is initiated to allow: 1) knowledge users to update service domain ontologies employed in the service retrieval methodology, taking into account the dynamic nature of services in Digital Ecosystems; and 2) service providers to update their service profiles and manually annotate their published service advertisements by means of service domain knowledge, taking into account the dynamic nature of service providers in Digital Ecosystems. The methodology for service domain knowledge updating is realized by a voting system for any proposals for changes in service domain knowledge, and by assigning different weights to the votes of domain experts and normal users.In order to validate the customized semantic service retrieval methodology, we build a prototype – a Customized Semantic Service Search Engine. Based on the prototype, we test the mathematical algorithms involved in the methodology by a simulation approach and validate the proposed functions of the methodology by a functional testing approach

A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system

© 2015 Elsevier B.V.All rights reserved. Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services

Bridging the Gap at Ecosystem Level : Enhancing Business Model Innovation in Internet of Things-Enabled Platform Ecosystems

Digitaalinen murros haastaa yrityksiä ja yhteisöjä tarjoamaan innovatiivisia palveluita asiakkailleen ja lisäämään omaa kannattavuuttaan uusia liiketoimintamalleja luomalla. Esineiden internet (IoT) on tunnistettu potentiaaliseksi uudenlaisen arvon mahdollistajaksi. Odotetuista hyödyistä huolimatta onnistuneesti toteutettuja IoT:llä varustettuja alustaekosysteemejä on toistaiseksi vähän. IoT-tutkimus on pääosin keskittynyt teknologisten edistysaskeleiden ottamiseen, kun taas liiketoimintamallien innovaatioiden merkitys on suurelta osin sivuutettu. On kuitenkin muistettava, että teknologian onnistunut käyttöönotto on suurelta osin kiinni hyvin määritellystä liiketoimintamallista ja sen arvolupauksen onnistuneisuudesta Sosiaalisen vaihdannan teoria (SET) on olennainen IoT:llä varustettujen alustaekosysteemien kontekstissa. Sen mukaan toimijoiden tulee kokea arvon vaihtaminen oikeudenmukaiseksi eli kokea saamansa arvo riittäväksi tekemiinsä panostuksiin nähden. Tätä teoreettista viitekehystä hyödynnettiin tässä tutkimuksessa selvitettäessä, miten digitaalisen murroksen aikoina liiketoimintamallien innovointia (BMI) voitaisiin parantaa IoT:llä varustetuissa alustaekosysteemeissä. Siten toimijoiden pysyvyyttä voitaisiin parantaa ja verkostojen ulkoisvaikutuksia lisätä. Tämän tutkimuksen tulokset lisäävät teoreettista ymmärrystä arvon vaihtamisesta IoT:tä hyödyntävien alustaekosysteemien kontekstissa. Tutkimuksessa tunnistettiin sosiaalisen arvon dimensiolle kaksi erilaista tulkintaa. Tutkimuksen perusteella voidaan myös todeta, etteivät teoriat – saati käytännön tekijät – huomioi ehdollista arvoa alustakontekstissa. Tutkimus tunnisti monitieteellisesti IoT:n ja alustaekosysteemien liiketoimintamalli-innovaatioiden luomiseen tarvittavat osat. Lisäksi tutkimuksessa luotiin uusi malli BMI:lle, joka yhdistää kaksi uutta työkalua eli ekosysteemin arvotaseen ja alustakanvaasin. Käyttämällä mallia iteratiivisesti strategisena työkaluna luodaan arvokasta näkemystä ekosysteemin toimijoille, minkä avulla he voivat luoda yhdessä yhteisen arvolupauksen ja mahdollistaa positiiviset verkostovaikutukset. Lisäksi tämä tutkimus edistää tutkimusmenetelmiä esittämällä uuden tavan tarkentaa konseptien ominaisuuksia kirjallisuuskatsauksen avulla. Parannettu menetelmä on yhdistelmä lumipallomenetelmää, Porter sanarunkohaku-algoritmia ja temaattista analyysiä. Näitä hyödyntämällä voidaan luoda kattava ja strukturoitu synteesi oleellisesta kirjallisuudesta ja edistää monivivahteisempaa ja syvempää ymmärrystä tutkimusaiheesta. Menetelmää voidaan hyödyntää myös muilla tutkimusalueilla täsmällisten kirjallisuuskatsausten tekemiseen. Tämä tutkimus avaa väylän arvolupausten arvioinnin tutkimiseen IoT:llä varustetuissa alustaekosysteemeissä. Lisää tutkimusta kuitenkin tarvitaan ennen kuin liiketoimintamahdollisuudet realisoituvat odotetusti. Ehdotettua mallia tulee tutkia vielä useammilla ja pidempikestoisilla tapaustutkimuksilla. Lisäksi monialainen tutkimus voisi tunnistaa yhtäläisyyksiä ja eroavaisuuksia IoT:llä varustettujen alustaekosysteemien haasteissa ja mahdollisuuksissa. Lisäksi tulisi tutkia, miten uudet ja tulevat teknologiat vaikuttavat arvolupauksen muodostamiseen ja arvon tuottamiseen. Tämän tutkimuksen tuloksissa korostetaan ekosysteemissä toimimisen vaatimaa kulttuurimuutosta. Perinteisesti yritykset ovat keskittyneet oman voittonsa maksimoimiseen, mutta ekosysteemeissä tulisi keskittyä koko ekosysteemin kokonaisarvon maksimoimiseen. Tämän kulttuurimuutoksen tarvetta ja sitä, miten muutos voitaisiin saada aikaan, tulisi tutkia lisää. Yhteenvetona voidaankin todeta, että tämä tutkimus edistää niin liiketoimintamallien innovoinnin teoriaa kuin käytäntöjäkin IoT:llä varustetuissa alustaekosysteemeissä. Se tarjoaa BMI-mallin, joka rakentuu ekosysteemin arvotaseen ympärille. Se mahdollistaa ketterän mallin, jolla IoT:llä varustetun alustaekosysteemin toimijat voivat iteratiivisesti luoda ja kehittää arvolupaustaan. Tämä tutkimus myös kirkastaa käsittelemiään konsepteja ja tarjoaa tuoreen lähestymistavan kirjallisuuskatsauksen tekemiseen. Tämä tutkimus voi auttaa yrityksiä ja yhteisöjä ymmärtämään liiketoimintamallien innovoinnin merkityksen ja näin johtaa ne luomaan kestävämpiä ja kannattavampia ekosysteemejä.Digital transformation is challenging businesses and societies to offer innovative services to customers and to increase profitability through the development of new business models. The Internet of Things (IoT) has been identified as a potent enabler for novel services and businesses. However, despite the potential benefits, successful implementation of IoT-enabled platform ecosystems remains scarce. Research on IoT has mainly focused on technological advancements, while the importance of business model innovation has been largely overlooked. The research in the field of IoT has predominantly focused on technological advancements, disregarding the critical aspect of business model innovation. However, successful implementation of technology largely relies on a well-defined business model that delivers outstanding value propositions. Social Exchange Theory (SET) is a theoretical framework that is pertinent in the context of IoT-enabled platform ecosystems. According to SET, actors in value exchange should find the distribution of value equitable vis-à-vis the effort invested in value creation. Therefore, in the present research, SET is adopted as a conceptual framework to explore how ecosystem-level business model innovation (BMI) in IoT-enabled platform ecosystems could be enhanced to increase actor retention, and to internalize network externalities to increase the positive network effects during times of digital transformation. The contribution of this research extends beyond the theoretical development of value exchange in the context of IoT-enabled platform ecosystems. This research identifies two different views of social value and recognizes that in the ecosystem context, conditional value is often overlooked in theoretical discussion and neglected by practitioners. This research also contributes to BMI theories in the IoT-enabled platform ecosystem context by identifying, in an interdisciplinary manner, the required building blocks, i.e., characteristics of a platform ecosystem BMI, and IoT. Further, a model for BMI is created, which combines two novel frameworks, namely, the Ecosystem Value Balance and the Platform Canvas. This provides ecosystem actors with valuable insights to co-create a joint value proposition and enable positive network effects by utilizing the model iteratively as a strategic tool. In addition, this research advances research methodologies by presenting a novel approach to clarifying concepts through literature reviews. The method involves a combination of snowballing, Porter stemming, and thematic analysis, which enables a comprehensive and structured synthesis of relevant literature and promotes a more nuanced and deeper understanding of the research topic. This approach can be applied in other research fields, too, to achieve more rigorous and accurate literature reviews. Although this research opens up avenues for researching value proposition evaluation in IoT-enabled ecosystems, more attention to the business opportunities that can be realized is necessary. The proposed model needs validation with more and longer-term cases, and a cross-industry study could explore potential similarities and differences in the challenges and opportunities of IoT platform ecosystems. Moreover, further research is required to validate the proposed model, explore potential similarities and differences in IoT platform ecosystems, and investigate the role of emerging technologies in shaping the value proposition and value creation processes. Further, the research emphasizes the need for cultural change in companies operating in ecosystems, as traditionally companies have focused on maximizing their profits instead of maximizing the overall value for the whole ecosystem. In conclusion, this research contributes to the theory and practice of business model innovation in IoT-enabled platform ecosystems by offering a BMI model which relies on value balance in ecosystem contexts and proposes a model for IoT platform ecosystem actors to co-create joint value propositions. It also clarifies related concepts and offers a novel approach to literature reviews. This research can help businesses and societies to understand the importance of business model innovation and to create a more sustainable and profitable ecosystem

Could Alexa Increase Your Social Worth?

People have historically used personal introductions to build social capital, which is the foundation of career networking and is perhaps the most effective way to advance a career (Lin, 2001). With societal changes, such as the pandemic (Venkatesh & Edirappuli, 2020), and the increasing capabilities of Artificial Intelligence (AI), new approaches may emerge that impact societal relationships. Social capital theory highlights the need for reciprocal agreements to establish the trust between parties (Gouldner, 1960). My theoretical prediction and focus of this research include two principles: The impact of reciprocity in evaluating trust of the source of the introduction and the acceptability of AI in interpersonal relationships. I test this relationship through the creation of plausible vignettes that the participants may have encountered in business. The results show that a higher trust of AI and could replace one side of the relationship, thus reducing the dependency on or eliminating reciprocal behavior

A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment

A Service Ecosystem is a biological view of the business and software environment, which is comprised of a Service Use Ecosystem and a Service Supply Ecosystem. Service matchmakers play an important role in ensuring the connectivity between the two ecosystems. Current matchmakers attempt to employ ontologies to disambiguate service consumers' service queries by semantically classifying service entities and providing a series of human computer interactions to service consumers. However, the lack of relevant service domain knowledge and the wrong service queries could prevent the semantic service matchmakers from seeking the service concepts that can be used to correctly represent service requests. To resolve this issue, in this paper, we propose the framework of a service concept recommendation system, which is built upon a semantic similarity model. This system can be employed to seek the concepts used to correctly represent service consumers' requests, when a semantic service matchmaker finds that the service concepts that are eventually retrieved cannot match the service requests. Whilst many similar semantic similarity models have been developed to date, most of them focus on distance-based measures for the semantic network environment and ignore content-based measures for the ontology environment. For the ontology environment in which concepts are defined with sufficient datatype properties, object properties, and restrictions etc., the content of concepts should be regarded as an important factor in concept similarity measures. Hence, we present a novel semantic similarity model for the service ontology environment. The technical details and evaluation details of the framework are discussed in this paper. © 2010 Elsevier Ltd. All rights reserved

A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment

A Service Ecosystem is a biological view of the business and software environment, which is comprised of a Service Use Ecosystem and a Service Supply Ecosystem. Service matchmakers play an important role in ensuring the connectivity between the two ecosystems. Current matchmakers attempt to employ ontologies to disambiguate service consumers' service queries by semantically classifying service entities and providing a series of human computer interactions to service consumers. However, the lack of relevant service domain knowledge and the wrong service queries could prevent the semantic service matchmakers from seeking the service concepts that can be used to correctly represent service requests. To resolve this issue, in this paper, we propose the framework of a service concept recommendation system, which is built upon a semantic similarity model. This system can be employed to seek the concepts used to correctly represent service consumers' requests, when a semantic service matchmaker finds that the service concepts that are eventually retrieved cannot match the service requests. Whilst many similar semantic similarity models have been developed to date, most of them focus on distancebased measures for the semantic network environment and ignore content-based measures for the ontology environment. For the ontology environment in which concepts are defined with sufficient datatype properties, object properties, and restrictions etc., the content of concepts should be regarded as an important factor in concept similarity measures. Hence, we present a novel semantic similarity model for the service ontology environment. The technical details and evaluation details of the framework are discussed in this paper

Trust and transparency in an age of surveillance

Investigating the theoretical and empirical relationships between transparency and trust in the context of surveillance, this volume argues that neither transparency nor trust provides a simple and self-evident path for mitigating the negative political and social consequences of state surveillance practices. Dominant in both the scholarly literature and public debate is the conviction that transparency can promote better-informed decisions, provide greater oversight, and restore trust damaged by the secrecy of surveillance. The contributions to this volume challenge this conventional wisdom by considering how relations of trust and policies of transparency are modulated by underlying power asymmetries, sociohistorical legacies, economic structures, and institutional constraints. They study trust and transparency as embedded in specific sociopolitical contexts to show how, under certain conditions, transparency can become a tool of social control that erodes trust, while mistrust - rather than trust - can sometimes offer the most promising approach to safeguarding rights and freedom in an age of surveillance. The first book addressing the interrelationship of trust, transparency, and surveillance practices, this volume will be of interest to scholars and students of surveillance studies as well as appeal to an interdisciplinary audience given the contributions from political science, sociology, philosophy, law, and civil society

Trust and Transparency in an Age of Surveillance

Investigating the theoretical and empirical relationships between transparency and trust in the context of surveillance, this volume argues that neither transparency nor trust provides a simple and self-evident path for mitigating the negative political and social consequences of state surveillance practices. Dominant in both the scholarly literature and public debate is the conviction that transparency can promote better-informed decisions, provide greater oversight, and restore trust damaged by the secrecy of surveillance. The contributions to this volume challenge this conventional wisdom by considering how relations of trust and policies of transparency are modulated by underlying power asymmetries, sociohistorical legacies, economic structures, and institutional constraints. They study trust and transparency as embedded in specific sociopolitical contexts to show how, under certain conditions, transparency can become a tool of social control that erodes trust, while mistrust—rather than trust—can sometimes offer the most promising approach to safeguarding rights and freedom in an age of surveillance. The first book addressing the interrelationship of trust, transparency, and surveillance practices, this volume will be of interest to scholars and students of surveillance studies as well as appeal to an interdisciplinary audience given the contributions from political science, sociology, philosophy, law, and civil society