Claudio Schifanella

Emerging topic detection on Twitter based on temporal and social terms evaluation

Twitter is a user-generated content system that allows its users to share short text messages, called tweets, for a variety of purposes, including daily conversations, URLs sharing and information news. Considering its world-wide distributed network of users of any age and social condition, it represents a low level news flashes portal that, in its impressive short response time, has the principal advantage. In this paper we recognize this primary role of Twitter and we propose a novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community. First, we extract the contents (set of terms) of the tweets and model the term life cycle according to a novel aging theory intended to mine the emerging ones. A term can be defined as emerging if it frequently occurs in the specified time interval and it was relatively rare in the past. Moreover, considering that the importance of a content also depends on its source, we analyze the social relationships in the network with the well-known Page Rank algorithm in order to determine the authority of the users. Finally, we leverage a navigable topic graph which connects the emerging terms with other semantically related keywords, allowing the detection of the emerging topics, under user-specified time constraints. We provide different case studies which show the validity of the proposed approach.

Verifying the conformance of web services to global interaction protocols: a first step

Global choreographies define the rules that peers should respect in their interaction, with the aim of guaranteeing interoperability. An abstract choreography can be seen as a protocol specification; it does not refer to specific peers and, especially in an open application domain, it might be necessary to retrieve a set of web services that fit in it. A crucial issue, that is raising attention, is verifying whether the business process of some peers, in particular the parts that encode the communicative behavior, will produce interactions which are conformant to the agreed protocol (legality issue). Such issue is tackled by the so called conformance test, which is a means for certifying the capability of interacting of the involved parts: two peers that are proved conformant to a same protocol will actually interoperate by producing a legal conversation. This work proposes an approach to the verification of a priori conformance of a business process to a protocol, which is based on the theory of formal languages and guarantees the interoperability of peers that are individually proved conformant.

CoSeNa: a context-based search and navigation system

Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, which supports navigation using domain-specific concepts as well as keywords that are characterizing the text corpus. The KbC graph is a weighted graph, created by tightly integrating keywords extracted from documents and concepts obtained from domain taxonomies. Documents in the corpus are associated to the nodes of the graph based on evidence supporting contextual relevance; thus, the KbC graph supports contextually informed access to these documents. In this paper, we also present CoSeNa (Context-based Search and Navigation) system that leverages the KbC model as the basis for document exploration and retrieval as well as contextually-informed media integration.

Verifying protocol conformance for logic-based communicating agents

Communication plays a fundamental role in multi-agents systems. One of the main issues in the design of agent interaction protocols is the verification that a given protocol implementation is “conformant” w.r.t. the abstract specification of it. In this work we tackle those aspects of the conformance verification issue, that regard the dependence/independence of conformance from the agent private state in the case of logic, individual agents, set in a multi-agent framework. We do this by working on a specific agent programming language, DyLOG, and by focussing on interaction protocol specifications described by AUML sequence diagrams. By showing how AUML sequence diagrams can be translated into regular grammars and, then, by interpreting the problem of conformance as a problem of language inclusion, we describe a method for automatically verifying a form of “structural” conformance; such a process is shown to be decidable and an upper bound of its complexity is given. We also give a set of properties that describes the influence of the agent private information on the conformance of its communication policies to protocol specifications.

Personalized emerging topic detection based on a term aging model

Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach.

Service selection by choreography-driven matching

The greater and greater quantity of services that are available over the web causes a growing attention to techniques that facilitate their reuse. A web service specification can be quite complex, including various operations and message exchange patterns. In this work, we focus on the problem of retrieving a web service, which can play a given choreography role, preserving at the same time a condition of interest (the goal for which the service is sought). We show that current semantic matchmaking techniques do not guarantee goal preservation. We also show an approach for overcoming these limits, which exploits the choreography definition. This work is based on an action-based representation of the operations of a service: each operation is described in terms of its preconditions and effects, without taking into account the ontology layer which is not functional to the aims of the work.

The d-index: Discovering dependences among scientific collaborators from their bibliographic data records

The evaluation of the work of a researcher and its impact on the research community has been deeply studied in literature through the definition of several measures, first among all the h-index and its variations. Although these measures represent valuable tools for analyzing researchers’ outputs, they usually assume the co-authorship to be a proportional collaboration between the parts, missing out their relationships and the relative scientific influences. In this work, we propose the d-index, a novel measure that estimates the dependence degree between authors on their research environment along their entire scientific publication history. We also present a web application that implements these ideas and provides a number of visualization tools for analyzing and comparing scientific dependences among all the scientists in the DBLP bibliographic database. Finally, relying on this web environment, we present case and user studies that highlight both the validity and the reliability of the proposed evaluation measure.

On context-aware co-clustering with metadata support

In traditional co-clustering, the only basis for the clustering task is a given relationship matrix, describing the strengths of the relationships between pairs of elements in the different domains. Relying on this single input matrix, co-clustering discovers relationships holding among groups of elements from the two input domains. In many real life applications, on the other hand, other background knowledge or metadata about one or more of the two input domain dimensions may be available and, if leveraged properly, such metadata might play a significant role in the effectiveness of the co-clustering process. How additional metadata affects co-clustering, however, depends on how the process is modified to be context-aware. In this paper, we propose, compare, and evaluate three alternative strategies (metadata-driven, metadata-constrained, and metadata-injected co-clustering) for embedding available contextual knowledge into the co-clustering process. Experimental results show that it is possible to leverage the available metadata in discovering contextually-relevant co-clusters, without significant overheads in terms of information theoretical co-cluster quality or execution cost.

Reasoning about agents' interaction protocols inside DCaseLP

Engineering systems of heterogeneous agents is a difficult task; one of the ways for achieving the successful industrial deployment of agent technology is the development of engineering tools that support the developer in all the steps of design and implementation. In this work we focus on the problem of supporting the design of agent interaction protocols by carrying out a methodological integration of the MAS prototyping environment DCaseLP with the agent programming language DyLOG for reasoning about action and change.

Sentiment Analysis in the Planet Art: A Case Study in the Social Semantic Web

Affective computing is receiving increasing attention in many sectors, ranging from advertisement to politics. Its application to the Planet Art, however, is quite at its beginning, especially for what concerns the visual arts. This work, set in a Social Semantic Web framework, explores the possibility of extracting rich, emotional semantic information from the tags freely associated to digitalized visual artworks, identifying the prevalent emotions that are captured by the tags. This is done by means of ArsEmotica, an application software that we developed and that combines an ontology of emotional concepts with available computational and sentiment lexicons. Besides having made possible the enrichment of the ontology with over four-hundred Italian terms, ArsEmotica is able to analyse the emotional semantics of a tagged artwork by working at different levels: not only it can compute a semantic value, captured by tags that can be directly associated to emotional concepts, but it can also compute the semantic value of tags that can be ascribed to emotional concepts only indirectly. The results of a user study, aimed at validating the outcomes of ArsEmotica, are reported and commented. They were obtained by involving the users of the same community which tagged the artworks. It is important to observe that the tagging activity was not performed with the aim of later applying some kind of Sentiment Analysis, but in a pure Web 2.0 approach, i.e. as a form of spontaneous annotation produced by the members of the community on one another’s artworks.