AMBIT: Semantic Engine Foundations for Knowledge Management in Context-dependent Applications

Context-aware application and services proposing potentially useful information to users are more and more widespread; however, their actual usefulness is often limited by the “syntactical” notion of context they adopt. The recently started AMBIT project aims to provide a general software architecture for developing semantic-based context-aware tools in a number of vertical case study applications. In this paper, we focus on the knowledge management foundations we are laying for the Semantic Engine of the AMBIT architecture. The proposed semantic analysis and similarity techniques: (a) exploit the textual information deeply characterizing both users and the information to be retrieved; (b) overcome the limits of syntactic methods by leveraging on the strengths of both classic information retrieval and knowledge-based analysis and classification, ultimately proposing information relevant to the user interests. The experimental evaluation of a preliminary implementation in an actual “cultural territorial enhancement” scenario already shows promising results

A User-Aware and Semantic Approach for Enterprise Search

This article describes how in addition to general purposes search engines, specialized search engines have appeared and have gained their part of the market. An enterprise search engine enables the search inside the enterprise information, mainly web pages but also other kinds of documents; the search is performed by people inside the enterprise or by customers. This article proposes an enterprise search engine called AMBIT-SE that relies on two enhancements: first, it is user-aware in the sense that it takes into consideration the profile of the users that perform the query; second, it exploits semantic techniques to consider not only exact matches but also synonyms and related terms. It performs two main activities: (1) information processing to analyse the documents and build the user profile and (2) search and retrieval to search for information that matches user’s query and profile. An experimental evaluation of the proposed approach is performed on different real websites, showing its benefits over other well-established approaches

About Challenges in Data Analytics and Machine Learning for Social Good

The large number of new services and applications and, in general, all our everyday activities resolve in data mass production: all these data can become a golden source of information that might be used to improve our lives, wellness and working days. (Interpretable) Machine Learning approaches, the use of which is increasingly ubiquitous in various settings, are definitely one of the most effective tools for retrieving and obtaining essential information from data. However, many challenges arise in order to effectively exploit them. In this paper, we analyze key scenarios in which large amounts of data and machine learning techniques can be used for social good: social network analytics for enhancing cultural heritage dissemination; game analytics to foster Computational Thinking in education; medical analytics to improve the quality of life of the elderly and reduce health care expenses; exploration of work datafication potential in improving the management of human resources (HRM). For the first two of the previously mentioned scenarios, we present new results related to previously published research, framing these results in a more general discussion over challenges arising when adopting machine learning techniques for social good

SocialGQ: Towards semantically approximated and user-Aware querying of social-graph data

The proliferation of social and collaborative sites makes users increasingly active in the generation of socialgraph data; however, such sea of data often hinders them from finding the information they need. In this paper, we present SocialGQ ("Social-Graph Querying"), a novel approach for the effective and efficient querying of socialgraph data overcoming the limitations of typical search approaches proposed in the literature. SocialGQ allows users to compose complex queries in a simple way, and is able to retrieve useful knowledge (top-k answers) by jointly exploiting: (a) the structure of the graph, semantically approximating the user's requests with meaningful answers; (b) the unstructured textual resources of the graph; (c) its social and user-Aware dimension. An experimental evaluation comparing SocialGQ to leading approaches shows strong gains on a real social-graph data scenario

Effective Aggregation and Querying of Probabilistic RFID Data in a Location Tracking Context

RFID applications usually rely on RFID deployments to manage high-level events such as tracking the location that products visit for supply-chain management, localizing intruders for alerting services, and so on. However, transforming low-level streams into high-level events poses a number of challenges. In this paper, we deal with the well known issues of data redundancy and data-information mismatch: we propose an on-line summarization mechanism that is able to provide small space representation for massive RFID probabilistic data streams while preserving the meaningfulness of the information. We also show that common information needs, i.e. detecting complex events meaningful to applications, can be effectively answered by executing temporal probabilistic SQL queries directly on the summarized data. All the techniques presented in this paper are implemented in a complete framework and successfully evaluated in real-world location tracking scenarios

Exploiting Semantics for Filtering and Searching Knowledge in a Software Development Context

Software development is still considered a bottleneck for SMEs (Small and Medium Enterprises) in the advance of the Information Society. Usually, SMEs store and collect a large number of software textual documentation; these documents might be profitably used to facilitate them in using (and re-using) Software Engineering methods for systematically designing their applications, thus reducing software development cost. Specific and semantics textual filtering/search mechanisms, supporting the identification of adequate processes and practices for the enterprise needs, are fundamental in this context. To this aim, we present an automatic document retrieval method based on semantic similarity and Word Sense Disambiguation (WSD) techniques. The proposal leverages on the strengths of both classic information retrieval and knowledge-based techniques, exploiting syntactical and semantic information provided by general and specific domain knowledge sources. For any SME, it is as easily and generally applicable as are the search techniques offered by common enterprise Content Management Systems (CMSs). Our method was developed within the FACIT-SME European FP-7 project, whose aim is to facilitate the diffusion of Software Engineering methods and best practices among SMEs. As shown by a detailed experimental evaluation, the achieved effectiveness goes well beyond typical retrieval solutions

Approximating expressive queries on graph-modeled data: The GeX approach

We present the GeX (Graph-eXplorer) approach for the approximate matching of complex queries on graph-modeled data. GeX generalizes existing approaches and provides for a highly expressive graph-based query language that supports queries ranging from keyword-based to structured ones. The GeX query answering model gracefully blends label approximation with structural relaxation, under the primary objective of delivering meaningfully approximated results only. GeX implements ad-hoc data structures that are exploited by a top-k retrieval algorithm which enhances the approximate matching of complex queries. An extensive experimental evaluation on real world datasets demonstrates the efficiency of the GeX query answering

A Novel Real-Time Edge-Cloud Big Data Management and Analytics Framework for Smart Cities

Exposing city information to dynamic, distributed, powerful, scalable, and user-friendly big data systems is expected to enable the implementation of a wide range of new opportunities; however, the size, heterogeneity and geographical dispersion of data often makes it difficult to combine, analyze and consume them in a single system. In the context of the H2020 CLASS project, we describe an innovative framework aiming to facilitate the design of advanced big-data analytics workflows. The proposal covers the whole compute continuum, from edge to cloud, and relies on a well-organized distributed infrastructure exploiting: a) edge solutions with advanced computer vision technologies enabling the real-time generation of “rich” data from a vast array of sensor types; b) cloud data management techniques offering efficient storage, real-time querying and updating of the high-frequency incoming data at different granularity levels. We specifically focus on obstacle detection and tracking for edge processing, and consider a traffic density monitoring application, with hierarchical data aggregation features for cloud processing; the discussed techniques will constitute the groundwork enabling many further services. The tests are performed on the real use-case of the Modena Automotive Smart Area (MASA)

Towards User-Aware Service Composition

Our everyday life is more and more supported by the information technology in general and specific services provided by means of our electronic devices. The AMBIT project (Algorithms and Models for Building context-dependent Information delivery Tools) aims at providing a support to develop services that are automatically tailored based on the user profile. However, while the adaptation of the single services is the first step, the next step is to achieve adaptation in the composition of different services. In this paper, we explore how services can be composed in a user-aware way, in order to decide the composition that better meets users’ requirements. That is, we exploit the user profile not only to provide her customized services, but also to compose them in a suitable way