Emanuele Della Valle

An Introduction to Information Retrieval

Information retrieval is a discipline that deals with the representation, storage, organization, and access to information items. The goal of information retrieval is to obtain information that might be useful or relevant to the user: library card cabinets are a “traditional” information retrieval system, and, in some sense, even searching for a visiting card in your pocket to find out a colleague’s contact details might be considered as an information retrieval task. In this chapter we introduce information retrieval as a scientific discipline, providing a formal characterization centered on the notion of relevance. We touch on some of its challenges and classic applications and then dedicate a section to its main evaluation criteria: precision and recall.

C-SPARQL: SPARQL for continuous querying

C-SPARQL is an extension of SPARQL to support continuous queries, registered and continuously executed over RDF data streams, considering windows of such streams. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with knowledge that evolves over time. We present C-SPARQL by means of examples in Urban Computing.

C-SPARQL: A CONTINUOUS QUERY LANGUAGE FOR RDF DATA STREAMS

This article defines C-SPARQL, an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time. C-SPARQL is presented by means of a full specification of the syntax, a formal semantics, and a comprehensive set of examples, relative to urban computing applications, that systematically cover the SPARQL extensions. The expression of meaningful queries over streaming data is strictly connected to the availability of aggregation primitives, thus C-SPARQL also includes extensions in this respect.

Querying RDF streams with C-SPARQL

Continuous SPARQL (C-SPARQL) is a new language for continuous queries over streams of RDF data. CSPARQL queries consider windows, i.e., the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with knowledge evolving over time. Examples of such application domains include real-time reasoning over sensors, urban computing, and social semantic data. In this paper, we present the C-SPARQL language extensions in terms of both syntax and examples. Finally, we discuss existing applications that already use C-SPARQL and give an outlook on future research opportunities.

Incremental reasoning on streams and rich background knowledge

This article presents a technique for Stream Reasoning, consisting in incremental maintenance of materializations of ontological entailments in the presence of streaming information. Previous work, delivered in the context of deductive databases, describes the use of logic programming for the incremental maintenance of such entailments. Our contribution is a new technique that exploits the nature of streaming data in order to efficiently maintain materialized views of RDF triples, which can be used by a reasoner. By adding expiration time information to each RDF triple, we show that it is possible to compute a new complete and correct materialization whenever a new window of streaming data arrives, by dropping explicit statements and entailments that are no longer valid, and then computing when the RDF triples inserted within the window will expire. We provide experimental evidence that our approach significantly reduces the time required to compute a new materialization at each window change, and opens up for several further optimizations.

A First Step Towards Stream Reasoning

While reasoners are year after year scaling up in the classical, time invariant domain of ontological knowledge, reasoning upon rapidly changing information has been neglected or forgotten. On the contrary, processing of data streams has been largely investigated and specialized Stream Database Management Systems exist. In this paper, by coupling reasoners with powerful, reactive, throughput-efficient stream management systems, we introduce the concept of Stream Reasoning. We expect future realization of such concept to have high impact on the future Internet because it enables reasoning in real time, at a throughput and with a reactivity not obtained in previous works.

Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics

A combined approach of deductive and inductive reasoning can leverage the clear separation between the evolving (streaming) and static parts of online knowledge at conceptual and technological levels. What are the hottest topics discussed on Twitter? Which topics have my close friends discussed in the last hour? Which movie is my friend most likely to watch next? Which Tuscan red wine should I recommend? With many popular social networks publishing microblogs and feeds, the information required to answer these queries is becoming available on the Web.

Social Listening of City Scale Events Using the Streaming Linked Data Framework

City-scale events may easily attract half a million of visitors in hundreds of venues over just a few days. Which are the most attended venues? What do visitors think about them? How do they feel before, during and after the event? These are few of the questions a city-scale event manger would like to see answered in real-time. In this paper, we report on our experience in social listening of two city-scale events (London Olympic Games 2012, and Milano Design Week 2013) using the Streaming Linked Data Framework.

Model-driven design and development of semantic Web service applications

This article proposes a model-driven methodology to design and develop semantic Web service applications and their components, described according to the emerging WSMO standard. In particular, we show that business processes and Web engineering models have sufficient expressive power to support the semiautomatic extraction of semantic descriptions (i.e., WSMO ontologies, goals, Web services, and mediators), thus partially hiding the complexity of dealing with semantics. Our method is based on existing models for the specification of business processes (BPMN) combined with Web engineering models for designing and developing semantically rich Web applications (WebML). The proposed approach leads from an abstract view of the business needs to a concrete implementation of the application by means of several design steps; high-level models are transformed into software components. Our framework increases the efficiency of the whole design process, yielding to the construction of semantic Web service applications spanning over several enterprises.

BOTTARI: An augmented reality mobile application to deliver personalized and location-based recommendations by continuous analysis of social media streams

In 2011, an average of three million tweets per day was posted in Seoul. Hundreds of thousands of tweets carry the live opinion of some tens of thousands of users about restaurants, bars, cafes, and many other semi-public points of interest (POIs) in the city. Trusting this collective opinion to be a solid base for novel commercial and social services, we conceived BOTTARI: an augmented reality application that offers personalized and localized recommendation of POIs based on the temporally weighted opinions of the social media community. In this paper, we present the design of BOTTARI, the potentialities of semantic technologies such as inductive and deductive stream reasoning, and the lessons learnt in experimentally deploying BOTTARI in Insadong-a popular tourist area in Seoul-for which we have been collecting tweets for three years to rate the hundreds of restaurants in the district. The results of our study demonstrate the feasibility of BOTTARI and encourage its commercial spread.