Serena Villata

Computational Logic in Multi-Agent Systems: 15th International Workshop, CLIMA XV, Prague, Czech Republic, August 18-19, 2014, Proceedings

This book constitutes the proceedings of the 15th International Workshop on Computational Logic in Multi-Agent Systems, CLIMA XV, held in Prague, Czech Republic, in August 2014.The 12 regular papers were carefully reviewed and selected from 20 submissions. The purpose of the CLIMA workshops is to provide a forum for discussing techniques, based on computational logic, for representing, programming and reasoning about agents and multi-agent systems in a formal way. This edition will feature two special sessions: logics for agreement technologies and logics for games, strategic reasoning, and social choice.

Enriching a Small Artwork Collection Through Semantic Linking

Cultural heritage institutions have recently started to explore the added value of sharing their data, opening to initiatives that are using the Linked Open Data cloud to integrate and enrich metadata of their cultural heritage collections. However, each museum and each collection shows peculiarities, which make it difficult to generalize this process and offer one-size-fits-all solutions. In this paper, we report on the integration, enrichment and interlinking activities of metadata from a small collection of verbo-visual artworks in the context of the Verbo-Visual-Virtual project. We investigate how to exploit Semantic Web technologies and languages combined with natural language processing methods to transform and boost the access to documents providing cultural information, i.e., artist descriptions, collection notices, information about technique. We also discuss the open challenges raised by working with a small collection including little-known artists and information gaps, for which additional data can be hardly retrieved from the Web.

A Multi-context Framework for Modeling an Agent-based Recommender System

In this paper, we propose a multi-agent recommender system based on the Belief-Desire-Intention (BDI) n nmodel applied to multi-context systems. First, we extend the BDI model with additional contexts to deal n nwith sociality and information uncertainty. Second, we propose an ontological representation of planning n nand intention contexts in order to reason about plans and intentions. Moreover, we show a simple real-world n nscenario in healthcare in order to illustrate the overall reasoning process of our model.

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The research results are applied to support and foster interactions in online communities and manage their resources.

Combining Natural Language Processing Approaches for Rule Extraction from Legal Documents

Legal texts express conditions in natural language describing what is permitted, forbidden or mandatory in the context they regulate. Despite the numerous approaches tackling the problem of moving from a natural language legal text to the respective set of machine-readable conditions, results are still unsatisfiable and it remains a major open challenge. In this paper, we propose a preliminary approach which combines different Natural Language Processing techniques towards the extraction of rules from legal documents. More precisely, we combine the linguistic information provided by WordNet together with a syntax-based extraction of rules from legal texts, and a logic-based extraction of dependencies between chunks of such texts. Such a combined approach leads to a powerful solution towards the extraction of machine-readable rules from legal documents. We evaluate the proposed approach over the Australian “Telecommunications consumer protections code”.

Information and friend segregation for online social networks: a user study

Online social networks (OSNs) captured the attention of the masses by offering attractive means of sharing personal information and developing social relationships. People expose personal information about their lives on OSNs. This may result in undesirable consequences of users’ personal information leakage to an unwanted audience and raises privacy concerns. The issue of privacy has received a significant attention in both the research literature and the mainstream media. In this paper, we present results of an empirical study that measure users’ attitude towards interpersonal privacy concerns in OSNs. The results demonstrated a serious mismatch between privacy concerns of users and their information sharing behavior. In addition, it indicated that strangers are part of user social circles, this makes privacy protection more complicated, and introduce an insider threat, whereas all existing privacy tools allow users to manage the outsider threat. Information and friend segregation strategies are suggested on the basis of user information disclosure and interaction pattern. We conclude that sensitivity of information and frequency of interaction, both, play a vital role in information and friend segregation.

An Agent-Based Architecture for Personalized Recommendations

This paper proposes a design framework for a personalized multi-agent recommender system. More precisely, the proposed framework is a multi-context based recommender system that takes into account user preferences to generate a plan satisfying those preferences. Agents in this framework have a Belief-Desire-Intention (BDI) component based on the well-known BDI architecture. These BDI agents are empowered with cognitive capabilities in order to interact with others agents. They are also able to adapt to the environment changes and to the information coming from other agents. The architecture includes also a planning module based on ontologies in order to represent and reason about plans and intentions. The applicability of the proposed model is shown through a simulation in the NetLogo environment.

Natural Language Argumentation for Text Exploration

Argumentation mining aims at automatically extracting natural language arguments from textual documents. In the last years, it has become a hot topic due to its potential in processing information originating from the Web in innovative ways. In this paper, we propose to apply the argument mining pipeline to the text exploration task. First, starting from the arguments put forward in online debates, we introduce bipolar entailment graphs to predict the relation among the textual arguments, i.e., entailment or non entailment relation. Second, we exploit the well know formalism called abstract dialectical frameworks to define acceptance conditions answering the needs of the text exploration task. The evaluation of the proposed approach shows its feasibility.

Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments (Dagstuhl Seminar 16161).

This report documents the program and the outcomes of Dagstuhl Seminar 16161 Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments, 17--22 April 2016. The seminar brought together leading researchers from computational linguistics, argumentation theory and cognitive psychology communities to discuss the obtained results and the future challenges of the recently born Argument Mining research area. 40 participants from 14 different countries took part in 7 sessions that included 30 talks, two tutorials, and a hands-on unshared task.