3rd International KEYSTONE Conference
IKC 2017
Gdańsk Poland, 11-12 September 2017
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Regular Papers
Charlie Abela and Joel Azzopardi.
Analysing and Visualising Parliamentary Questions: a Linked Data approach
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Abstract:
In many national parliaments, Members can exercise a basic Parliamentary function of holding the Executive to account by submitting Questions to Government Ministers. In certain parliaments Members also have the faculty of either requesting a written answer or an oral one. Parliamentary Questions (PQs) often generate significant media attention and public interest, and are considered to be a very useful tool for parliamentarians to scrutinise the Government's operative and financial administration. Interesting insights about individual Members of Parliament (MPs) as well as about the Parliament as a collective institution can be gleaned by analysing PQs. In this paper we present a linked data approach to PQs that is complemented with visualisations intended to increase the accessibility, by citizens, to this rich repository of parliamentary data. We use PQ data from the Maltese Parliament ranging over the last four legislatures and present an application called PQViz that exploits graph analytics to expose interesting insights from this data.
David Abian, Francesco Guerra, Jorge Martinez-Romanos and Raquel Trillo-Lado.
Wikidata and DBpedia: A Comparative Study
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Abstract:
DBpedia and Wikidata are two online projects focused on offering structured data from Wikipedia in order to ease its explotation on the Linked Data Web. In this paper, a comparison of these two widely-used structured data sources is presented. This comparison considers the most relevant data quality dimensions in the state of the art of the scientific research. As fundamental differences between both projects, we can highlight that Wikidata has an open centrallized nature, whereas DBpedia is more popular in the Semantic Web and the Linked Open Data communities and depends on the different linguistic editions of Wikipedia.
Ana Mestrovic.
Collaboration Networks Analysis: Combining Structural and Keyword-based Approaches
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Abstract:
This paper proposes a framework for the analysis of the characteristics of collaboration networks. The framework uses social network analysis metrics which are especially applicable to directed and weighted collaboration networks. By using the proposed framework it is possible to investigate the global structure of the collaboration networks, such as density, centralisation, assortativity and the dynamics of network growth. Furthermore, the framework proposes appropriate network centrality measures (degree and its variations for directed and weighted networks) for ranking the nodes. In addition the proposed framework combines a keyword-based approach and Louvain algorithm for the community detection task. Next, the paper describes a case study in which the proposed framework is applied to the collaboration networks emerged from STSMs on the KEYSTONE COST Action.
David Alvarez, Josée R.R. Viqueira and Alberto Bugarin. .
Towards Keyword-Based Search over Environmental Data Sources
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Abstract:
This paper describes the problem of keyword-based search over environmental data sources. Based on a number of assumptions that simplify this general problem, a prototype of a search engine for environmental data was designed, implemented and evaluated. This first solution serves as a proof of concept that illustrates its applicability in different domains, for both expert and non-expert users. The requirements analysis undertaken and the subsequent design and implementation helped in the identification of a number of new research challenges.
Colin Layfield and Dragan Ivanovic.
Multi-Lingual LSA with Serbian and Croatian: An Investigative Case Study
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Abstract:
One of the challenges in information retrieval is attempting to search a corpus of documents that may contain multiple languages. This exploratory study expands upon earlier research employing Latent Semantic Analysis (so called Multi-Lingual Latent Semantic Indexing, or ML-LSI/LSA). We experiment using this approach, and a new one, in a multi-lingual context utilising two similar languages, namely Serbian and Croatian. Traditionally, with a LSA approach, a parallel corpus would be needed in order to train the system by combining identical documents in two languages into one document. We repeat that approach and also experiment with creating a semantic space using the parallel corpus on its own without merging the documents together to test the hypothesis that, with very similar languages, the merging of documents may not be required for good results.
Nicholas Mamo and Joel Azzopardi.
FIRE: Finding Important News REports
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Abstract:
Every day, an immeasurable number of news items are published. Social media greatly contributes to the dissemination of information, making it difficult to stay on top of what is happening. Twitter stands out among popular social networks due to its large user base and the immediateness with which news is spread.
In this paper, we present a solution named Finding Important News REports (FIRE) that exploits the information available on Twitter to identify and track breaking news, and the defining articles that discuss them. The methods used in FIRE present context-specific problems when dealing with the micro-messages of Twitter, and thus they are the subject of research.
FIRE demonstrates how Twitter’s conversation habits do nothing to shackle the detection of important news. To the contrary, the developed system is able to extract newsworthy stories that are important to the general population, and do so before Twitter itself. Moreover, the results emphasize the need for reliable and efficient spam and noise filtering tools.
Slobodan Beliga, Olivera Kitanovic, Ranka Stankovic and Sanda Martincic-Ipsic.
Keyword Extraction from Parallel Abstracts of Scientific Publications
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Abstract:
In this paper, we study the keyword extraction from parallel abstracts of scientific publication in the Serbian and English languages. The keywords are extracted by a selectivity-based keyword extraction method. The method is based on the structural and statistical properties of text represented as a complex network. The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still there are drawbacks – the method can extract only the words that appear in the text.
Jakub Jagoda and Tomasz Boinski.
Assessing Word Difficulty for Quiz-like Game
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Abstract:
Mappings verification is a laborious task. Our research aims at providing a framework for manual verification of mappings using crowdsourcing approach. For this purpose we plan on implementing a quiz like game. For this purpose the mappings have to be evaluated in terms of difficulty to better present texts in respect of game levels. In this paper we present an algorithm for assessing word difficulty. Three approaches are presented and experimental results are shown. Plans for future works are also provided.
Fatma Slaimi, Sana Sellami and Omar Boucelma.
From a Web Services Catalog to a Linked Ecosystem of Services
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Abstract:
In this paper, we present a Linked ecosystem of Web services where both Web services, mashups and users are represented as a multigraph structure. For illustration and experimental purposes, and instance of the graph has been constructed, in gathering different data from ProgrammableWeb. The graph is stored in a Neo4J graph database and serves as a repository for real collection of data for achieving services/mashups discovery, selection and recommendation.
Peter Butka, Thomas Low, Michael Kotzyba, Stefan Haun and Andreas Nuernberger.
Exploration of Web Search Results based on the Formal Concept Analysis
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Abstract:
In this paper, we present an approach to support exploratory search by structuring search results based on concept lattices, which are created on the fly using advanced methods from the area of Formal Concept Analysis (FCA). The main aim of the approach is to organize query based search engine results (e.g. web documents) as a hierarchy of clusters that are composed of documents with similar attributes. The concept lattice provides a structured view on the query-related domains and hence can improve the understanding of document properties and shared features. Additionally, we applied a fuzzy extension of FCA in order to support the usage of different types of attributes within the analyzed query results set. The approach has been integrated into an interactive web search interface. It provides a smooth integration of keyword-based web search and interactive visualization of concept lattice and its concepts in order to support complex search tasks.
Ioannis Bourlakos, Manolis Wallace, Angeliki Antoniou, Costas Vassilakis, George Lepouras and Anna Vassiliki Karapanagiotou.
Formalization and visualization of the narrative
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Abstract:
There is a wide range of metadata standards for the documentation of museum related information, such as CIDOC-CRM; these standards focus on the description of distinct exhibits. In contract, there is a lack of standards for the digitization and documentation of the routes followed and information provided by museum guides. In this work we propose the notion of ``narrative'', which can be used to model a guided museum visit. We provide a formalization for the narrative so that it can be digitally encoded, and thus preserved, shared, re-used, further developed and exploited, and also propose an intuitive visualization approach.
Vagan Terziyan, Mariia Golovianko and Michael Cochez.
TB-Structure: Collective Intelligence for Exploratory Keyword Search
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Abstract:
This paper represents a search optimization technique dealing with large-scale content and the open-world assumption. The aim is to increase search effectiveness predicting implicit seeker’s intents at early stages of the search process. It`s done by uncovering behavioral patterns in large sets of previously collected data describing collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s intents.
Andrea Cali and Martin Ugarte.
Foundations of Accessing the Deep Web with keywords
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Abstract:
The Deep Web is constituted by data that are generated dynamically as the result of interactions with Web pages. The problem of accessing Deep Web data presents many challenges: it has been shown that answering even simple queries on such data requires the execution of recursive query plans. Unfortunately, there is a gap between the theoretical understanding of this problem and the practical approaches to it. The main reason behind this is that the computational complexity of the problem is studied by considering a database as part of the input. Therefore, it is assumed that the user has full access to the database, but wants to obtain only answers that he could retrieve by following the access patterns. On the contrary, in practice users do not have full access to the database, and have to explore it by posing a (possibly large) number of searches on the Web. In this paper we embark on the task of closing the above gap and of giving a precise definition that reflects the practical nature of accessing Deep Web data sources. We formalise the associated decision problem, and show that assuming full access to the underlying database can make query answering exponentially faster.
Andrzej Sobecki and Marcin Kepa.
Methodology of selecting the Hadoop ecosystem conguration in order to improve the performance of a plagiarism detection system
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Abstract:
The plagiarism detection problem involves nding patterns in unstructured text documents. Similarity of documents in this approach means that the documents contain some identical phrases with dened minimal length.The typical methods used to nd similar documents in digital libraries are not suitable for this task (plagiarism detection) be- cause found documents may contain similar content and we have not any warranty that they contain any of identical phrases. The article describes an example method of searching for similar documents contains identical phrases in big documents repositories, and presents a problem of select- ing storage and computing platform suitable for presented method using in plagiarism detection systems. In the article we present comparison of the mentioned above method implementations using two computing platforms: KASKADA and Hadoop with dierent congurations in or- der to test and compare their performance and scalability. The method using the default tools available on the Hadoop platform i.e. HDFS and Apache Spark oers worse performance than the method implemented on the KASKADA platform using the NFS (Network File System) and the processing model Master/Slave. The advantage of the Hadoop plat- form increases with the use of additional data structures (hash-map) and tools oered on this platform, i.e. HBase (NoSQL). The tools integrated with the Hadoop platform provide a possibility of creating ecient and a scalable method for nding similar documents in big repositories. The KASKADA platform oers ecient tools for analysing data in real-time processes i.e. when there is no need to compare the input data to a large collection of information (patterns) and to use the advanced data structures. The Contribution of this article is the comparison of the two computing and storage platforms in order to achieve better performance of the method used in the plagiarism detection system to nd similar documents containing identical phrases.
Short Papers
Claudia Ifrim, Iulian Iuga, Florin Pop, Manolis Wallace and Vassilis Poulopoulos.
Data Reduction Techniques applied on Automatic Identification System Data
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Abstract:
In recent years, the constant incrase of waterway traffic generates a high volume of AIS data that require a big effort to be processed and analyzed in near real-time. In this paper, we analyze an AIS data set and we propose a data reduction technique that can be applied on AIS data without losing any important information in order to reduce it to a manageable size data set that can be furthure used for analysis or can be easly used for AIS data visualization applications.
Joel Azzopardi.
Item-based vs User-based Collaborative Recommendation Predictions
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Abstract:
The use of personalised recommendation systems to push interesting items to users have become a necessity in the digital world that contains overwhelming amounts of information. One of the most effective ways to achieve this is by considering the opinions of other similar users -- i.e. through collaborative techniques. In this paper, we compare the performance of item-based and user-based recommendation algorithms as well as propose an ensemble that combines both systems. We investigate the effect of applying LSA, as well as varying the neighbourhood size on the different algorithms. Finally, we experiment with the inclusion of content-type information in our recommender systems. We find that the most effective system is the ensemble system that uses LSA.
Paolo Nesi, Laura Po, José R. Ríos Viqueira and Raquel Trillo Lado.
An Integrated Smart City Platform
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Abstract:
Smart Cities aim to create a higher quality of life for their citizens, improve business services and promote tourism experience. Fostering smart city innovation at local and regional level requires a set of mature technologies to discover, integrate and harmonize multiple data sources and the exposure of effective applications for end-users (citizens, administrators, tourists...). In this context, Semantic Web technologies and Linked Open Data principles provide a means for sharing knowledge about cities as physical, economical, social, and technical systems, enabling the development of smart city services. Despite the tremendous effort these communities have done so far, there exists a lack of comprehensive and effective platforms that handle the entire process of identification, ingestion, consumption and publication of data for Smart Cities. In this paper, a complete open-source platform to boost the integration, semantic enrichment, publication and exploitation of public data to foster smart cities in local and national administrations is proposed. Starting from mature software solutions, we propose a platform to facilitate the harmonization of datasets (open and private, static and dynamic on real time) of the same domain generated by different authorities. The platform provides a unified dataset oriented to smart cities that can be exploited to offer services to the citizens in a uniform way, to easily release open data, and to monitor services status of the city in real time by means of a suite of web applications.
Mariia Golovianko, Svitlana Gryshko and Vagan Terziyan.
From Deep Learning to Deep University: Cognitive Development of Intelligent Systems
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Abstract:
Search is not only an instrument to find intended information. Ability to search is a basic cognitive skill helping people to explore the world. It is largely based on personal intuition and creativity. However, due to the emerged big data challenge, people require new forms of training to develop or improve this ability. Current developments within Cognitive Computing and Deep Learning enable artificial systems to learn and gain human-like cognitive abilities. This means that the skill how to search efficiently and creatively within huge data spaces becomes one of the most important ones for the cognitive systems aiming at autonomy. This skill cannot be pre-programmed, it requires learning. We offer to use the collective search expertise to train creative association-driven navigation across heterogeneous information spaces. We argue that artificial cognitive systems, as well as humans, need special environments, like universities, to train skills of autonomy and creativity.
Chaya Liebeskind and Karine Nahon.
Challenges in Applying Machine Learning Methods: Studying Political Interactions on Social Networks
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Abstract:
This document discusses the potential role of Machine Learning (ML) methods in social science research, in general, and specifically in studies of political behavior of users in social networks (SN). This paper explores challenges which occurred in a set of studies which we conducted regarding classification of comments to posts of politicians and suggests ways of addressing these challenges. These challenges apply to a larger set of online political behavior studies.
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