Research and Applications in Web Intelligence, Mining, and Semantics [Editorial] Rajendra Akerkar
Nick Bassiliades
John Davies
Information Technology Group Vestlandsforsking Sogndal, Norway
Department of Informatics Aristotle University of Thessaloniki Greece
British Telecommunicatons, plc. United Kingdom
[email protected]
[email protected]
[email protected]
Vadim Ermolayev Department of IT Zaporozhye National University Ukraine
[email protected] ABSTRACT
Keywords
The Web has an enormous influence on our everyday life. Thus, more efficient intelligent approaches and technologies are needed to realize the Web’s full potential. Intelligence can be achieved by making the Web aware of the semantics of its own structures and content and by applying intelligent techniques to effectively access web resources. The Semantic Web was one of the significant steps towards bringing Intelligence to the Web. Based on this starting point, the Web Intelligence, Mining, and Semantics (WIMS) community works toward researching and implementing the next generation of the intelligent Web for humans and machines. In this editorial, opening the volume of the proceedings of WIMS’14, we review the topics of interest for the WIMS community, analyze the response of this year’s authors to these topics, and present the program of the conference. We hope that this material will be useful for a reader as a key for the structure and content of these proceedings.
Web intelligence, web semantics, web mining, scalable web, data architectures, information extraction, knowledge extraction, reasoning, application, case study, evaluation, validation, methodology
Categories and Subject Descriptors H [Information Systems]; I.2 [Artificial Intelligence]; I.7 [Document and Text Processing]; A.0 [General]: Conference proceedings
General Terms Algorithms, Management, Measurement, Performance, Design, Reliability, Experimentation, Human Factors, Languages, Theory, Verification
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[email protected]. WIMS '14, June 02 - 04 2014, Thessaloniki, Greece Copyright 2014 ACM 978-1-4503-2538-7/14/06…$15.00. http://dx.doi.org/10.1145/2611040.2611045
1. INTRODUCTION The World Wide Web (WWW) has grown quickly in the past two decades from a small research community to the biggest and most popular infrastructure for communication, information dissemination, search, social interaction and commerce. Every day, the WWW grows by nearly a million electronic pages, adding to the hundreds of millions previously on-line. WWW serves as a platform for exchanging various kinds of information, ranging from research publications, and educational content, to multimedia content and software. The incessant growth in the size and use of the WWW imposes the demand for novel methods to process these immense volumes of data. Because of its rapid and chaotic growth, the resulting network of information lacks organization and structure. The content is published in various diverse formats. Due to these and many other complicating factors, users are feeling sometimes perplexed, lost in the information overload that continues to increase. Issues that have to be dealt with are the detection of relevant information, involving the searching and indexing of Web content, the creation of some metadata and knowledge out of the information which is available on the Web, as well as the addressing of the individual user needs and interests, by personalizing the provided information and services. Web intelligence (WI), as a research direction, has a broad agenda dealing with the issues that arise around the WWW phenomenon [1]. It is a converging research area bringing together several research communities, such as Databases (DB), Information Retrieval (IR), Semantic Technologies (ST), Artificial Intelligence (AI) to mention a few. To facilitate this convergence, the WIMS community started the conference series in 2011 on web intelligence, web mining and web semantics. The WIMS conference is an international forum for researchers and practitioners to present the state-of-the-art in the building of an intelligent Web, to examine performance characteristics of
various approaches in web-based intelligent information technology, and to cross-fertilize ideas about the development of Web-based intelligent information management among different domains. For the Web to reach its full potential and gain intelligence, we need to enhance its services, make it more comprehensible, and increase its usability. As researchers continue to develop intelligent tools and techniques for web mining and web semantics, we believe these technologies will play ever more vital role in meeting the challenges of developing the intelligent Web. In this editorial to the proceedings volume of WIMS’14, we discuss the challenges of WIMS and report on the community effort resulting in the contributions to this volume. The next section offers an overview of the research and application fields, topics, and themes that form the agenda for the WIMS community. Section 3 reports the response of the WIMS’14 authors in these areas and to the related problems. Finally, the composition of the contributions to the WIMS’14 conference sessions is presented to provide a reader with a key to this volume structure and content.
2.
THE WIMS LANDSCAPE
This section offers an overview of the research and application fields, topics, and themes that form the agenda for the WIMS community.
2.1 Scalable Web, Data Architectures, and Infrastructures The WIMS conference series is interested in novel data architectures and infrastructures especially suited to meet the challenges in the Web, namely the massive volumes of data, their dynamic nature, their explicit or implicit semantics, their integrity and provenance. Coupled with the above is the emergence of new Web-based information systems (e.g. health and bio- information systems) and novel, smart user interfaces for analyzing and visualizing these massive data making sense out of them. Several challenges need to be addressed with these systems, such as scalability issues, data synchronization issues, security and privacy, indexing and information extraction from the deep Web, crawling, caching, querying and question answering. New research areas, such as big data computing, and nature-inspired models and approaches in web and data processing infrastructures, are also emerging as future new hot topics to cope with the large volume of web data. Finally, the introduction of sensors and novel equipment that on the one hand interact with the real world and on the other hand they live as web entities, brings into life the Sensing Web, also known as the Web of Things.
2.2 Web Intelligence Web intelligence is about applying artificial intelligence and information technology techniques on the Web in order to create novel, adaptive and smarter web-based products, services and frameworks. Research about web intelligence covers many fields such as data mining, information retrieval, semantic web, etc. Especially for WIMS‘14 the focus is on either using some new AI techniques for providing general purpose web intelligence, such as semantic agent systems or nature-inspired models, or using AI techniques for more specialized web research and application areas, for example visualizing web data, such as social network data or linked open data, analyzing big data, such as data originating from ubiquitous intelligence environments, sensor networks, and the Internet of Things, data from social media, etc., enhancing web services, grids, and middleware, smart advertising
through social networks, “feeling” the sentiment / opinion of the crowds from the Social Web, etc. In another approach to webbased intelligence, the Web offers ways to incentivize people to participate, as in a social game, in complicated intelligent computations which machines are still unable to perform, offering their intelligence to a machine. This is usually called crowdsourcing or human-based computation. Finally, all the above open up new possibilities to build novel user interaction and communication paradigms, possibly based on natural language or semantically enhanced interfaces.
2.3 Web Mining, Information and Knowledge Extraction The facet of web mining and extracting information and knowledge from the Web within the WIMS landscape covers several interrelated and vibrant research directions providing enabling technologies for web intelligence. In particular, the methods and technologies for mining content spanning across different modalities (multimedia) and being very dynamic in its nature or constrained in access time (data streams) gain increased attention in the research communities and more demand in industries. An important complicating factor for these technologies is that the content to be mined is sometimes at big data scale, with all the associated complications for processing complexity, scalability, coverage, quality, etc. Provided that such enabling technologies appear, the capability for building the knowledge layer for the Web, and more specifically for linked data on the Web, increases drastically – pushing forward the development of the intelligent Web. Contextualization and clustering are the approaches in web mining and information extraction leading to the techniques that help making the data mix separable – discovering the gems of information in the burden of noise; helping solving the challenge of polysemy and choosing the right pragmatic scent of sense for this or that context. Clustering is also a very powerful technique for enabling the analytics – for example a search for trends and tendencies based on the features made visible in separated clusters of information. Further, contextualization proves to be valuable not only for semantic disambiguation, but also for making the vast volumes of information smaller and better structured, hence facilitating to better scalable processing techniques. It is well known that the knowledge acquisition bottleneck is one of the blocking factors for making the concept of the Semantic Web a usable industrial strength reality. The technologies allowing for at least semi-automated building of ontologies from the data present and evolving on the Web are therefore a key for making the Web more intelligent and usable for machines. A vibrant field of research developing such technologies is knowledge extraction and ontology learning from the Web. Further, much semantic information resides in the relationships between data. So, mining these relationships provides a valuable source for building substantially more comprehensive knowledge models of web resources. Even further, linked data clusters and infrastructures stem out of these efforts – enabling more powerful data provision and analytics. Again, the complications arise when real world data is addressed because it is big and evolving data [2]. These challenges are addressed under the theme of information extraction and knowledge discovery from big data. The volume and diversity of data increases even more as the deep web data appears on the agenda. Performance, scalability, recognition, information and knowledge fusion problems with respect to the deep web data and knowledge are more complex
and demand novel approaches and techniques based on the use of semantics and machine intelligence.
2.4 Web Semantics and Reasoning The concept of the Semantic Network Model was formed in the early 1960s as a formalism to represent semantically structured knowledge. In the Web context, these ideas can be adapted to extend the network of hyperlinked human-readable web pages by inserting machine-readable metadata about pages and how they are related to each other, enabling automated agents to access the Web more intelligently and perform tasks on behalf of users [3]. The term "Semantic Web" was coined by Tim Berners-Lee to mean "a web of data that can be processed directly and indirectly by machines" [4]. The technologies proposed by the World Wide Web Consortium (W3C), which oversees the development of proposed semantic web standards, are used in the contexts dealing with information that encompasses a limited and defined domain, and where sharing data is a common necessity, such as scientific research or data exchange among businesses. In addition, other technologies with similar goals have emerged, such as microformats. The WIMS conference recognizing the widespread research lines developed for over than 10 years in the Semantic Web research community, as well as the emergence of web semantics within large commercial or community-driven projects, included several topics for web semantics and the associated required reasoning on them. Knowledge representation methodologies for the Web include mainly ontologies and metadata. Ontologies are usually based on some variation of description logics, OWL, the official W3C ontology language, being one of them, whereas metadata are usually based on the semantic network model of RDF. Linked semantic data or linked open data are actually RDF data hosted on some database server, either supporting natively the RDF data model or exposing an RDF-ish view of relational data, usually via a public SPARQL endpoint, i.e. a RESTful service. Metadata are based on property vocabularies which are actually the ontologies that classify web resources to specific sets or classes of entities with specified properties. Ontologies are usually developed using well-structured engineering methodologies and they aim at being re-used by others on the Web. This can be usually achieved by separating the generic, high-level concepts and roles into an upper ontology, whereas the domain-specific concepts are defined in a domain- or application- specific ontology. Alternatively, ontologies could be developed in a bottom-up manner using the wisdom of the crowds, via crowd-sourcing or via clustering tags from the Social Web. No matter how an ontology has been developed it must be maintained throughout its lifecycle and may evolve. Furthermore, since ontologies are usually artefacts of different groups of people with the same or similar goals and backgrounds, they may cover similar concepts with different ontology structures, assumptions, constraints, etc. Since the semantic interoperability of the exchanged information over the web can only be achieved when all involved entities use the same concept to describe the same thing, different ontologies need to be aligned, merged or mapped to each other. This is usually achieved using machine learning or information retrieval techniques. Finally, ontologies can be used for semantically annotating content through the use of microformats, i.e. extensions of HTML that allow the annotation of web content with metadata using e.g. the RDF model, as in RDFa. In addition to ontologies, which are based on description
logics, a subset of first-order logic, rules in the form of Horn logic, another subset of first-order logic, are also a significant knowledge representation formalism for reasoning about metadata on the Semantic Web. Rules are offered in a great variety of languages, syntaxes, semantics, and systems, so that the landscape of interoperation and exchange of information among heterogeneous systems is even more confusing. Rule markup languages and associated systems try to bridge the gap between rule systems by offering a common language and a framework of semantics to organize the various rule languages in order to be safely exchanged by systems on the Web. All the above knowledge representation efforts are coupled with the research on building expressive yet scalable reasoning systems, able to cope with the large volumes of data available on the Web. Furthermore, the Web is not a “clean room”; therefore, issues on incompleteness, vagueness, and/or uncertainty of data and knowledge should be tackled by reasoning systems.
2.5 WIMS Applications and Case Studies Research in web intelligence, mining, and semantics has been ongoing for more than two decades. WIMS technologies are being used in many 'vertical' industries such as in media, telecommunications, healthcare, governments, and others, as well as in different application areas such as in business intelligence, search, big data, recommendation systems, knowledge management, etc. These technologies are used to build applications that are prototyped, piloted, and/or put into production, and are starting to extend their influence from early adopters into the mainstream. Many technologies and tools in the WIMS area are often used to deliver infrastructural backend solutions, and so are not always easy to recognize. The recent drive by many governments and other organizations towards open data has led to an increase in application of linked open data technologies for information integration, adding to existing applications areas for WIMS technologies such as knowledge management, intelligent search and service-based computing. The objective of having this facet in the topical spectrum of WIMS, with a specific Applications and Case Studies track, is to provide the visibility of WIMS technologies used in industrial applications. It is aimed at showcasing implemented applications, learned best practices, and evaluations of WIMS techniques in education, enterprise, and other real-world deployments.
2.6 Evaluation and Validation Methodologies Since many new technologies, algorithms and methodologies have been driven by the emergence of the Web, and many new, vast amounts of information are available, new methodologies for evaluating and validating all the above need to be developed. Alternatively, traditional methodologies can be adapted to the nature of the Web, namely its extremely large data space, coupled with data uncertainty, unreliability and sometimes unavailability of the data sources. So, the above call for the establishment of new datasets and benchmarks for cross-evaluations of methodologies and competitions / comparisons between them. The huge data space calls for developing efficient evaluation and validation infrastructures, where large datasets and benchmarks can be hosted. Finally, traditional evaluation and validation metrics, e.g. fitness, quality, completeness, correctness, etc., need to be adapted to the new context of the Web.
2.7 The Challenges and Future of WIMS Research In this section we discuss the role and issues of semantics, data and social factors in future WIMS research. Nowadays, data
generated from different sources result in enormous volume, huge variety and speedy changes (velocity). To make efficient use of such data, rapid integration and processing of data from various heterogeneous sources is necessary. Providing interoperable information representation and extracting actionable knowledge from the deluge of human and machine data are the crucial issues. We refer to the innovative web intelligence capabilities needed to exploit all these types of data to enable advanced applications. The role of semantics for interoperability, integration, and improved querying has been investigated for several years. The “Semantic Web” drive brought focus to using semantics and metadata initially to web documents [5]. As the Web provided effective mechanisms to access and use new types of resources– well represented data, services, user generated content and other social data, sensor and devices data – techniques gradually moved from syntactic and structural to semantic ones. In addition to the web-based semantic systems built using semantic web languages, standards and typical semantic web technologies that employ formal representation of semantics, however, a larger number of systems are being built using informal and implicit forms of semantics. Engagement with the Web is changing: more people access content through applications compared to the Web browsers. Recently, lighter-weight semantic approaches have led to better developer and user engagements, and have become a lot more scalable. IBM Watson, Apple Siri and Google Knowledge Graph are the examples of using semantics at scale, but where the formal forms of semantic representation or RDF/SPARQL have not found a place. Therefore, in the near future, the Semantic Web may be thought of as something that popularized the core value proposition of semantics – better search, interoperability/ integration and analysis – to deal with and exploit a vast variety of things that the Web interconnects. Consequently WIMS research is increasingly merging with other powerful technologies that support semantics, mining and intelligence, including Machine Learning, Natural Language Processing, and Knowledge-based Systems, where contextual knowledge is applied.
3. RESPONSE TO THE WIMS RESEARCH TOPICS In order to meet the challenges of web-related research in the new information age, a new international conference series, namely the International Conference on Web Intelligence, Mining and Semantics (WIMS) has been initiated by Vestlandsforsking (Norway) in 2011. WIMS’14 is the 4th meeting in this series concerned with intelligent approaches to transform the World Wide Web into a global reasoning and semantics-driven computing machine. It is an international forum for researchers and practitioners to present the state-of-the-art in building Intelligent Web, to examine performance characteristics of various approaches in web-based intelligent information technology, and to cross-fertilize their ideas on the development of web-based intelligent information management solutions across different do-mains. By idea-sharing and discussions on the underlying foundations and the enabling technologies of web intelligence, we hope to stimulate future development of new models, new methodologies, and new tools for building a variety of embodiments of Intelligent Web Information Systems. The title “WIMS” of the conference was chosen to reflect the distinct feature that the conference is focused on intelligence and semantic aspects of the Web and also web information
Table 1: The coverage of the top-level WIMS Scope Topic Areas by the accepted papers (Research and Application tracks) WIMS Scope Topic Area
No Papers
Scalable Web and Data Architectures and Infrastructures (SWDAI) Web Intelligence (WI)
10
Web Mining, Information and Knowledge Extraction (WMIKE) Web Semantics and Reasoning (WSR)
14
WIMS Applications (APP)
14
Evaluation and Validation of WIMS Technologies and Applications (EVTA)
6
13
11
management and systems. The conference scope1 has been carefully thought of to address this important focus. It comprises the six top-level WIMS Scope Topic Areas presented in Table 1. Each of these topics is detailed by several second-level bullets covering the active and challenging themes of research and development within a theme. The importance of the WIMS topics has been proven by the submissions to the conference, and in particular the accepted papers. Overall, for the Research and Application tracks of WIMS’14 41 best papers have been accepted out of 90 submissions. Among those accepted papers 27 are full, 13 – short, and 1 – discussion paper. Our analysis revealed that the accepted papers roughly evenly covered the topics of the conference scope (Table 1 and Figure 1). Many of these, though emphasizing the focus on one of the topics, also build a bridge to another theme or showcase a technology in an application. The average number of scope topics dealt with by a paper is 1.66. 15 of 41 papers spanned across two and 6 across three topic areas. In many cases an additional area is APP which signifies about the effort spent by the authors for evaluating their results. This is why the number of the responses to the APP topic area is the highest. We were also curious about the set of terms best characterizing the papers of WIMS’14 included in this volume. For determining these, key term extraction2 was performed from the full texts of all accepted papers yielding the scored list of
Figure 1: Scope topic coverage proportions. 1
http://wims14.csd.auth.gr/?page_id=146
2
Extraction was performed using the TerMine service which is provided by the UK National Centre for Text Mining (NaCTeM, http://www.nactem.ac.uk/). NaCTeM is operated by the University of Manchester.
Table 2: Extracted key terms and their scores No 1 2 3 4 5 6 7 8 9 10 11 341 342
Key Term classification web service confidence classifier search engine social network semantic web identification candidate answer web page knowledge base ………… yellow marker youtube api
Score 133.00000 131.41176 130.00000 102.00000 81.72727 79.76923 74.38236 60.00000 58.23333 54.80953 53.92857 5.00000 5.00000
terms3. The scores were computed using the term recognition technique [6] which uses information about the frequencies of term occurrence. The resulting scored list of key terms has been further manually cleaned. Out of the cleaned list the upper part representing the “majority opinion”4 has been extracted and made public at the WIMS’14 web site. The excerpt from this resulting scored list is presented in Table 2. The tag cloud visualizing this scored list is pictured in Figure 2. The response to the Applications and Case Studies track of the WIMS conference this year has been substantially higher than previously. The submissions to the track showed the great variety, vibrancy, and breadth of the applications of web intelligence technologies in education, enterprise, and the real-world. Amongst the papers submitted, we had papers reporting their experiences of applying WIMS technologies to medical science, to academia, to the building of recommendation systems, to telecommunications, and to semantic search engines. Topically, the two of the three keynote talks at WIMS’14 were given by people representing different industrial sectors – telecommunications and media. These keynotes were focused on the industrial deployment and adoption of WIMS technologies yielding value to business and society. WIMS’14 also hosted two workshops that offered in-depth focused discussions of some specific aspects in research and development which are relevant to the WIMS topical landscape. The 4th Workshop on Applications of Software Agents (WASA 20145) dealt with software agent technologies that reached a level of maturity allowing the development of applications spanning from lab prototypes to mature real-life systems, in domains that were not possible previously. In particular, the workshop was interested in the precedents of the
3
4
The scored list of terms has been made public at http://wims14.csd.auth.gr/?page_id=822
The “majority opinion” has been computed as the broadest upper subset of terms for which the sum of their scores is higher than the sum of the scores of the rest of the terms. The technique is adopted from the OntoElect methodology [7]. 5 Figure 2: The tag cloud of WIMS’14 key terms. http://perun.pmf.uns.ac.rs/events/wasa2014/
synergy of software agent technologies with the methods and techniques in intelligent computing and artificial intelligence, proved their usefulness in applications. The WASA workshop contributed 8 accepted papers to our proceedings. The objective of the Modelling, Mining, Managing Smart City Data Flows Workshop (3M4City6) was to promote smart city research results in the context of modelling, mining, and managing data flows. As an integral part of the WIMS’14 conference, this workshop was dedicated to open discussions about the most important issues today in terms of smart city methodologies, implementations, and practices. 3M4city aimed at illustrating the theoretical context, existing state, current issues and trends, accompanied by innovative and forthcoming developments (norms, policies, and standards) in the smart city domain, mainly with regard to other city data flows (such as social networks, open data, etc.). More specifically, it did not just examine the smart city domain, but also looked at the integrated activities, such as social networking and innovative city solutions, towards adding value and beneficial impact. Theoretical concepts and modeling, empirical evidence and selected case studies from leading scholars and practitioners in the field, showing the “big picture” were showcased in this workshop. This workshop, though offering an in-depth focus on the domain of smart cities, was interdisciplinary in the sense that it brought together researchers and practitioners from several fields: Social Computing, Machine Learning, and Data Mining. This interdisciplinary approach provided a forum to think about the obstacles that hamper the leveraging of understanding and capturing of smart city trends with regard to the social network dynamics. The 3M4City workshop contributed 5 accepted papers to our proceedings.
4. PROGRAM AND SESSIONS AT WIMS’14 This year the conference has received three internationally renowned keynote speakers from both academy and industry. Grigoris Antoniou, the professor of Knowledge Representation and Semantic Technologies from the University of Huddersfield (United Kingdom), gave a talk on Large-Scale Reasoning with (Semantic) Data. In his talk professor Antoniou discussed scalable methods for non-monotonic rule-based reasoning over the semantic web data, using MapReduce. He pointed out that this work is motivated by the recent unparalleled explosion of available data coming from the Web, sensor readings, databases, ontologies, and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. Richard Benjamins, the director of Business Intelligence at Telefonica (Spain), presented on Big Data – from Hype to Reality. He focused on the potential business value to be created in this area by describing the opportunities and risks arising from the recent emergence of big data analytics technology for companies. Further, he discussed the role businesses can play in big data. Finally he explained Telefonica’s experience in applying big data technology, both internally for the enhancement of its own business processes and externally, where they are applying the technology to benefit their customers. 6
http://wims14.csd.auth.gr/?page_id=601
Table 3: Distribution of WIMS papers to sessions WIMS Scope Topic Areas SWDAI WI WMIKE WSR APP EVTA
Full Papers
Short Papers
No of Sessions
6 6 5 5 4 2
0 4 2 2 4 1
2 3 2 2 2 1
Jem Rayfield, the Head Architect at Financial Times (United Kingdom), gave the talk on the Semantic Technology for Online, Broadcast and Print Media. In this talk he described the Financial Times use of semantic technology to power its online and print product portfolio. He provided an insight into the Financial Times technical strategy which aims to deprecate a relational, taxonomical and search driven architecture towards a new semantic, search, and document centric architecture. An overview of the architectural approach used to deliver the BBC’s sport and olympics services has also been provided. The conference program has also benefitted from the tutorial given by Vasileios Verykios et al. representing four different academic institutions in Greece. The tutorial was focused on Knowledge Sanitization on the Web. It offered an informed presentation of recent approaches that deal with the sanitization of binary databases so that sensitive frequent itemsets are excluded from the unearthing achieved from the application of frequent itemset mining algorithms, like Apriori. In this tutorial the taxonomy of the works presented in the past few years in the area of frequent itemset hiding has been provided. This taxonomy consists of different categories, such as heuristic distortion-based approaches, heuristic blocking approaches, border-based approaches, database reconstruction approaches, inverse frequent itemset mining approaches, and linear programming-based approaches. The material has been illustrated by the representative examples of algorithms from each category to highlight their unique characteristics. The presentations of the contributed papers have been structured around the top-level WIMS Scope Topic Areas and their coverage according to the accepted papers, as analyzed in Section 3 and Table 1. In order to organize the conference sessions each paper has been classified to the main topic that it focuses on, as summarized in Table 3. This resulted in a total of 12 sessions for both the Research and Applications tracks. Furthermore, the two workshops contributed in total 13 papers, which were presented in 5 sessions. Concerning the papers within sessions, Table 4 summarizes the more specific topics that presented papers focused on inside each group of sessions. These specific topics are taken from the sub-topics of the top-level WIMS Scope Topic Areas.
5. ACKNOWLEGMENTS WIMS’14 Conference is supported by the Aristotle University of Thessaloniki (A.U.Th.), Greece, and more specifically, by the Department of Informatics and its research group of Logic Programming & Intelligent Systems, and the Vestlandsforsking, Department of Information Technologies of Zaporizhzhya National University (Z.N.U.), Ukraine, British Telecommunications Plc. Furthermore, WIMS’14 was sponsored by the Research Committee of the A.U.Th., the Faculty of
Table 4: Distribution of papers to sub-topics within sessions Sessions
Sub-topics
SWDAI
User Interfaces and Visualization for the Web Crawling, Caching and Querying Web Data Indexing and Information Extraction from the Web Web Intelligence in Social Media Opinion Mining / Sentiment Analysis on the Social Web Named Entity Linking Semantic Agent Systems for Web intelligence Social Monetization and Computational Advertising Web Intelligence in Human Computation Web Intelligence for Services Contextualization and Clustering in Web Mining and Information Extraction Knowledge Extraction and Ontology Learning from the Web Mining and Information Extraction from the Web Text, Data Stream, Web and Multimedia Content Mining Knowledge Representation and Reasoning for the Web Ontologies and Linked Semantic Data and Semantic Annotation Intelligence and Semantics for Business Information Management and Integration Semantic Search Semantic Technologies in e-Learning Semantics-driven Information Retrieval Web Intelligence for Sensors and Situational Awareness Evaluation and Validation Metrics Evaluation and Validation Methodologies
WI
WMIKE
WSR
APP
EVTA
No of papers 3 2 1 4 2 1 1 1 1 1 3 2 1 1 3 3 1 1 2 2 1 1 2 1
Sciences the A.U.Th., and by OTS (Open Technology Services) S.A.
6. REFERENCES [1] R. Akerkar and P. Lingras. Building an Intelligent Web: Theory & Practice. Jones and Bartlett, Sudbury, MA, 2008. [2] R. Akerkar. Big Data Computing. Taylor & Francis, 2013. [3] N. Bassiliades. Agents and Knowledge Interoperability in the Semantic Web Era. In WIMS’12 Conference Proceedings, pages 46–58. ACM, Craiova, Romania, June 2012. [4] T. Berners-Lee, J. Hendler and O. Lassila. The Semantic Web. Scientific American 284(5):34-43, May 2001. [5] J. Davies, D. Fensel and F. van Harmelen. Towards the Semantic Web. Wiley, UK, 2000. [6] K. Frantzi, S. Ananiadou and H. Mima. Automatic Recognition of Multi-Word Terms. Int. J. of Digital Libraries, 3(2):117–132, 2000. [7] S. Tatarintseva, V. Ermolayev, B. Keller and W.-E. Matzke. Quantifying Ontology Fitness in OntoElect Using Saturationand Vote-Based Metrics. In V. Ermolayev et al. (Eds.) Information and Communication technologies in Education, Research, and Industrial Applications. Revised Selected Papers of ICTERI 2013, pages 136–162, CCIS Vol. 412, Springer-Verlag, Berlin-Heidelberg, 2013.
