Georgios Siolas

Exploiting Wikipedia Knowledge for Conceptual Hierarchical Clustering of Documents

In this paper, we propose a novel method for conceptual hierarchical clustering of documents using knowledge extracted from Wikipedia. The proposed method overcomes the classic bag-of-words models disadvantages through the exploitation of Wikipedia textual content and link structure. A robust and compact document representation is built in real-time using the Wikipedia application programmers interface, without the need to store locally any Wikipedia information. The clustering process is hierarchical and extends the idea of frequent items by using Wikipedia article titles for selecting cluster labels that are descriptive and important for the examined corpus. Experiments show that the proposed technique greatly improves over the baseline approach, both in terms of F-measure and entropy on the one hand and computational cost on the other.

Social things - The SandS instantiation

At a time when socialism as an economic option is variously questioned, very few people are against social instances of our life such as entertainment, customer assistance, and so on. This happens with the management of many things accompanying our life as well. We can find both the reason and the evidence for the viability of this trend in one very basic fact: things are social because they work better. However, also in this sphere social politics are highly questionable. Here we introduce the perspective adopted in the European project SandS within a framework of Internet of Things. In this case things are agents interacting on the network within a service centric approach where a sound hierarchy dispatches instructions. It is a complete ecosystem where the social network develops a collective intelligence subtending new concrete functionalities that are centered on the user willing and fostered by his/her feedbacks. The central role of the user reflects on all aspects of the ecosystem, from the family of things which are socially governed: the household appliances (the white goods) that affect our everyday life, up to the employed hardware and software: strictly open source.

Social and Smart: Towards an Instance of Subconscious Social Intelligence

The Social and Smart (SandS) project aims to lay the foundations for a social network of home appliance users endowed with a layer of intelligent systems that must be able to produce new solutions to new problems on the basis of the knowledge accumulated by the social network players. The system is not a simple recollection of tested appliance use recipes, but it will have the ability generate new or refine existing recipes trying to satisfy user demands, and to perform fine tuning of recipes on the basis of user satisfaction by a hidden reinforcement learning process. This paper aims to advance on the specification of diverse aspects and roles of the system architecture, to get a clearer picture of module interactions and duties, along with data transfer and transformation paths.

A Hybrid Web-Based Measure for Computing Semantic Relatedness Between Words

In this paper, we build a hybrid Web-based metric for computing semantic relatedness between words. The method exploits page counts, titles, snippets and URLs returned by a Web search engine. Our technique uses traditional information retrieval methods and is enhanced by page-count-based similarity scores which are integrated with automatically extracted lexico-synantic patterns from titles, snippets and URLs for all kinds of semantically related words provided by WordNet (synonyms, hypernyms, meronyms, antonyms). A support vector machine is used to solve the arising regression problem of word relatedness and the proposed method is evaluated on standard benchmark datasets. The method achieves an overall correlation of 0.88, which is the highest among other metrics up to date.

DoSO: a document self-organizer

In this paper, we propose a Document Self Organizer (DoSO), an extension of the classic Self Organizing Map (SOM) model, in order to deal more efficiently with a document clustering task. Starting from a document representation model, based on important “concepts” exploiting Wikipedia knowledge, that we have previously developed in order to overcome some of the shortcomings of the Bag-of-Words (BOW) model, we demonstrate how SOM’s performance can be boosted by using the most important concepts of the document collection to explicitly initialize the neurons. We also show how a hierarchical approach can be utilized in the SOM model and how this can lead to a more comprehensive final clustering result with hierarchical descriptive labels attached to neurons and clusters. Experiments show that the proposed model (DoSO) yields promising results both in terms of extrinsic and SOM evaluation measures.

Mining Interesting Topics in Twitter Communities

We present a methodology for identifying user communities on Twitter, by defining a number of similarity metrics based on their shared content, following relationships and interactions. We then introduce a novel method based on latent Dirichlet allocation to extract user clusters discussing interesting local topics and propose a methodology to eliminate trivial topics. In order to evaluate the methodology, we experiment with a real-world dataset created using the Twitter Searching API.

Scaled On-line Unsupervised Learning Algorithm for a SOM-HMM Hybrid

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a scaled on-line gradient descent unsupervised learning algorithm.

Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models

Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea.

Accuracy Versus Novelty and Diversity in Recommender Systems: A Nonuniform Random Walk Approach

In this chapter, we focus on recommender systems that are enhanced with social information in the form of trust statements between their users. The trust information may be processed in a number of ways, including the random walks in the social graph, where every step in the walk is chosen almost uniformly at random from the available choices. Although this strategy yields satisfactory results in terms of the novelty and the diversity of the produced recommendations, it exhibits poor accuracy because it does not fully exploit the similarity information among users and items. Our work tries to model user-to-user and user-to-item relation as a probability distribution using a novel approach based on Rejection Sampling in order to decide its next step (biased random walk). Some initial results on reference datasets indicate that a satisfying trade-off among accuracy, novelty, and diversity is achieved.

Semantic Question Answering Using Wikipedia Categories Clustering

We describe a system that performs semantic Question Answering based on the combination of classic Information Retrieval methods with semantic ones. First, we use a search engine to gather web pages and then apply a noun phrase extractor to extract all the candidate answer entities from them. Candidate entities are ranked using a linear combination of two IR measures to pick the most relevant ones. For each one of the top ranked candidate entities we find the corresponding Wikipedia page. We then propose a novel way to exploit Semantic Information contained in the structure of Wikipedia. A vector is built for every entity from Wikipedia category names by splitting and lemmatizing the words that form them. These vectors maintain Semantic Information in the sense that we are given the ability to measure semantic closeness between the entities. Based on this, we apply an intelligent clustering method to the candidate entities and show that candidate entities in the biggest cluster are the most semantically related to the ideal answers to the query. Results on the topics of the TREC 2009 Related Entity Finding task dataset show promising performance.