Alfredo Milani

Set Similarity Measures for Images Based on Collective Knowledge

This work introduces a new class of group similarity where different measures are parameterized with respect to a basic similarity defined on the elements of the sets. Group similarity measures are of great interest for many application domains, since they can be used to evaluate similarity of objects in term of the similarity of the associated sets, for example in multimedia collaborative repositories where images, videos and other multimedia are annotated with meaningful tags whose semantics reflects the collective knowledge of a community of users. The group similarity classes are formally defined and their properties are described and discussed. Experimental results, obtained in the domain of images semantic similarity by using search engine based tag similarity, show the adequacy of the proposed approach in order to reflect the collective notion of semantic similarity.

Community of scientist optimization: An autonomy oriented approach to distributed optimization

A novel optimization paradigm, called Community of Scientists Optimization (CoSO), is presented in this paper. The approach is inspired to the behaviour of a community of scientists interacting, pursuing for research results and foraging the funds needed to held their research activities. The CoSO metaphor can be applied to general optimization domains, where optimal solutions emerge from the collective behaviour of a distributed community of interacting autonomous entities. The CoSO framework presents analogies and remarkable differences with other evolutionary optimization approaches: swarm behaviour, foraging and selection mechanism based on research funds competition, dynamically evolving multicapacity communication channels realized by journals and evolving population size regulated by research management strategies. Experiments and comparisons on benchmark problems show the effectiveness of the approach for numerical optimization. CoSO, with the design of appropriate foraging and competition strategies, also represents a great potential as a general meta-heuristic for applications in non-numerical and agent-based domains.

Heuristics for Semantic Path Search in Wikipedia

In this paper an approach based on Heuristic Semantic Walk (HSW) is presented, where semantic proximity measures among concepts are used as heuristics in order to guide the concept chain search in the collaborative network of Wikipedia, encoding problem-specific knowledge in a problem-independent way. Collaborative information and multimedia repositories over the Web represent a domain of increasing relevance, since users cooperatively add to the objects tags, label, comments and hyperlinks, which reflect their semantic relationships, with or without an underlying structure. As in the case of the so called Big Data, methods for path finding in collaborative web repositories require solving major issues such as large dimensions, high connectivity degree and dynamical evolution of online networks, which make the classical approach ineffective. Experiments held on a range of different semantic measures show that HSW lead to better results than state of the art search methods, and points out the relevant features of suitable proximity measures for the Wikipedia concept network. The extracted semantic paths have many relevant applications such as query expansion, synthesis of explanatory arguments, and simulation of user navigation.

PLANNING IN REACTIVE ENVIRONMENTS

The diffusion of domotic and ambient intelligence systems have introduced a new vision in which autonomous deliberative agents operate in environments where reactive responses of devices can be cooperatively exploited to fulfill the agents goals. In this article a model for automated planning in reactive environments, based on numerical planning, is introduced. A planner system, based on mixed integer linear programming techniques, which implements the model, is also presented. The planner is able to reason about the dynamic features of the environment and to produce solution plans, which take into account reactive devices and their causal relations with agents goals by exploitation and avoidance techniques, to reach a given goal state. The introduction of reactive domains in planning poses some issues concerning reasoning patterns which are briefly depicted. Experiments of planning in reactive domains are also discussed.

Multi-path traces in semantic graphs for latent knowledge elicitation

In this work an online collaborative semantic network is explored. The method used is based on multi-path traces for extracting latent contextual knowledge, which explores an unknown Semantic proximity measures based on search engines are uses as heuristics to navigate the collaborative network, in order to find multiple random paths representing traces between seed concepts. The exploration is driven by an online randomized walk informed by those heuristics, where the multiple traces model reinforces the most relevant explanatory paths using a pheromone-like approach to elicit latent contexts. Experiments have been held on Wikipedia and on the Word Similarity 353 dataset to evaluate the effectiveness of the method. The general methodology can be easily extended to other online collaborative graphs and to non-textual domains.

A Semantic Comparison of Clustering Algorithms for the Evaluation of Web-Based Similarity Measures

The Internet explosion and the massive diffusion of mobile devices lead to the creation of a worldwide collaborative system, daily used by millions of users through search engines and application interfaces. New paradigms permit to calculate the similarity of terms using only the statistical information returned by a query, or from additional features; also old algorithms and measures have been applied to new domains and scopes, to efficiently find words clusters from the Web. The problem of evaluating such techniques and algorithms in new domains emerges, and highlights a still open field of experimentation.

Leveraging Zero Tail in Neighbourhood for Link Prediction

For link prediction, Common Neighbours (CN) ranking measures allow to discover quality links between nodes in a social network, assessing the likelihood of a new link based on the neighbours frontier of the already existing nodes. A zero rank value is often given to a large number of pairs of nodes, which have no common neighbours, that instead can be potentially good candidates for a quality assessment. With the aim of improving the quality of the ranking for link prediction, in this work we propose a general technique to evaluate the likelihood of a linkage, iteratively applying a given ranking measure to the Quasi-Common Neighbours (QCN) of the node pair, i.e. iteratively considering paths between nodes, which include more than one traversing step. Experiments held on a number of datasets already accepted in literature show that QCNAA, our QCN measure derived from the well know Adamic-Adar (AA), effectively improves the quality of link prediction methods, keeping the prediction capability of the original AA measure. This approach, being general and usable with any CN measure, has many different applications, e.g. trust management, terrorism prevention, disambiguation in co-authorship networks.

Clustering Facebook for Biased Context Extraction

Facebook comments and shared posts often convey human biases, which play a pivotal role in information spreading and content consumption, where short information can be quickly consumed, and later ruminated. Such bias is nevertheless at the basis of human-generated content, and being able to extract contexts that does not amplify but represent such a bias can be relevant to data mining and artificial intelligence, because it is what shapes the opinion of users through social media. Starting from the observation that a separation in topic clusters, i.e. sub-contexts, spontaneously occur if evaluated by human common sense, especially in particular domains e.g. politics, technology, this work introduces a process for automated context extraction by means of a class of path-based semantic similarity measures which, using third party knowledge e.g. WordNet, Wikipedia, can create a bag of words relating to relevant concepts present in Facebook comments to topic-related posts, thus reflecting the collective knowledge of a community of users. It is thus easy to create human-readable views e.g. word clouds, or structured information to be readable by machines for further learning or content explanation, e.g. augmenting information with time stamps of posts and comments. Experimental evidence, obtained by the domain of information security and technology over a sample of 9M3k page users, where previous comments serve as a use case for forthcoming users, shows that a simple clustering on frequency-based bag of words can identify the main context words contained in Facebook comments identifiable by human common sense. Group similarity measures are also of great interest for many application domains, since they can be used to evaluate similarity of objects in term of the similarity of the associated sets, can then be calculated on the extracted context words to reflect the collective notion of semantic similarity, providing additional insights on which to reason, e.g. in terms of cognitive factors and behavioral patterns.

SEMO: a semantic model for emotion recognition in web objects

In this work, we present SEMO, a Semantic Model for Emotion Recognition, which enables users to detect and quantify the emotional load related to basic emotions hidden in short, emotionally rich sentences (e.g. news titles, tweets, captions). The idea of assessing the semantic similarity of concepts by looking at the occurrences and co-occurrences of terms describing them in pages indexed by a search engine can be directly extended to emotions, and to the words expressing them in different languages. The emotional content associated to a particular emotion for a term can thus be estimated using web-based similarity measures, e.g. Confidence, PMI, NGD and PMING, aggregating the distance computed by a model of emotions, e.g. Ekman, Plutchik and Lovheim. Emotions are ranked based on their similarity to the analyzed text, describing each sentence through a vector of values of emotion load, which form the Vector Space Model for the chosen emotion model and similarity measures. The model is tested comparing experimental results to a ground truth in literature. SEMO takes care of both the phases of data collection and data analysis, to produce knowledge to be used in application domains such as social robots, recommender systems, and human-machine interactive systems.

A New Precedence-Based Ant Colony Optimization for Permutation Problems

In this paper we introduce ACOP, a novel ACO algorithm for solving permutation based optimization problems. The main novelty is in how ACOP ants construct a permutation by navigating the space of partial orders and considering precedence relations as solution components. Indeed, a permutation is built up by iteratively adding precedence relations to a partial order of items until it becomes a total order, thus the corresponding permutation is obtained. The pheromone model and the heuristic function assign desirability values to precedence relations. An ACOP implementation for the Linear Ordering Problem (LOP) is proposed. Experiments have been held on a large set of widely adopted LOP benchmark instances. The experimental results show that the approach is very competitive and it clearly outperforms previous ACO proposals for LOP.