Valeria Fionda

Semantic navigation on the web of data: specification of routes, web fragments and actions

The massive semantic data sources linked in the Web of Data give new meaning to old features like navigation; introduce new challenges like semantic specification of Web fragments; and make it possible to specify actions relying on semantic data. In this paper we introduce a declarative language to face these challenges. Based on navigational features, it is designed to specify fragments of the Web of Data and actions to be performed based on these data. We implement it in a centralized fashion, and show its power and performance. Finally, we explore the same ideas in a distributed setting, showing their feasibility, potentialities and challenges.

N auti LOD: A Formal Language for the Web of Data Graph

The Web of Linked Data is a huge graph of distributed and interlinked datasources fueled by structured information. This new environment calls for formal languages and tools to automatize navigation across datasources (nodes in such graph) and enable semantic-aware and Web-scale search mechanisms. In this article we introduce a declarative navigational language for the Web of Linked Data graph called NautiLOD. NautiLOD enables one to specify datasources via the intertwining of navigation and querying capabilities. It also features a mechanism to specify actions (e.g., send notification messages) that obtain their parameters from datasources reached during the navigation. We provide a formalization of the NautiLOD semantics, which captures both nodes and fragments of the Web of Linked Data. We present algorithms to implement such semantics and study their computational complexity. We discuss an implementation of the features of NautiLOD in a tool called swget, which exploits current Web technologies and protocols. We report on the evaluation of swget and its comparison with related work. Finally, we show the usefulness of capturing Web fragments by providing examples in different knowledge domains.

Biological Network Querying Techniques: Analysis and Comparison

Research in systems biology has made available large amounts of data about interactions among cell building blocks (e.g., proteins, genes). To properly look up these data and mine useful information, the design and development of automatic tools has become crucial. These tools leverage Biological Networks as a formal model to encode molecular interactions. Biological networks can be fed as input to graph-based techniques useful to infer new information about cellular activity and evolutive processes of the species. In this context, a rather interesting family of techniques is that of network querying. Network querying tools search a whole biological network to identify conserved occurrences of a given query module for transferring biological knowledge. Indeed, inasmuch as the query network generally encodes a well-characterized functional module, its occurrences in the queried network suggest that the latter (and, as such, the corresponding organism) features the function encoded by the former. The aim of this paper is that of analyzing and comparing tools devised to query biological networks. This analysis is intended to help in understanding problems and research issues, state of the art and opportunities for researchers working in this area.

A technique to search for functional similarities in protein-protein interaction networks

We describe a method to search for similarities across protein-protein interaction networks of different organisms. The technique core consists in computing a maximum weight matching of bipartite graphs resulting from comparing the neighbourhoods of proteins belonging to different networks. Both quantitative and reliability information are exploited. We tested the method on the networks of S. cerevisiae, D. melanogaster and C. elegans. The experiments showed that the technique is able to detect functional orthologs when the sole sequence similarity does not prove itself sufficient. They also demonstrated the capability of our approach in discovering common biological processes involving uncharacterised proteins.

Querying graphs with preferences

This paper presents GuLP a graph query language that enables to declaratively express preferences. Preferences enable to order the answers to a query and can be stated in terms of nodes/edge attributes and complex paths. We present the formal syntax and semantics of GuLP and a polynomial time algorithm for evaluating GuLP expressions. We describe an implementation of GuLP in the GuLP-it system, which is available for download. We evaluate the GuLP-it system on real-world and synthetic data.

The complexity of mixed multi-unit combinatorial auctions: Tractability under structural and qualitative restrictions

Mixed multi-unit combinatorial auctions (MMUCAs) are extensions of classical combinatorial auctions (CAs) where bidders trade transformations of goods rather than just sets of goods. Solving MMUCAs, i.e., determining the sequences of bids to be accepted by the auctioneer, is computationally intractable in general. However, differently from classical combinatorial auctions, little was known about whether polynomial-time solvable classes of MMUCAs can be singled out on the basis of their characteristics. The paper fills this gap, by studying the computational complexity of MMUCA instances under structural and qualitative restrictions, which characterize interactions among bidders and types of bids involved in the various transformations, respectively.

Protein-Protein Interaction Network Querying by a “Focus and Zoom” Approach

We propose an approach to network querying in protein-protein interaction networks based on bipartite graph weighted matching. An algorithm is presented that first “focuses” the potentially relevant portion of the target graph by performing a global alignment of this one with the query graph, and then “zooms” on the actual matching nodes by considering their topological arrangement, hereby obtaining a (possibly) approximated occurrence of the query graph within the target graph. Approximation is related to node insertions, node deletions and edge deletions possibly intervening in the query graph. The technique manages networks of arbitrary topology. Moreover, edge labels are used to represent and manage the reliability of involved interactions. Some preliminary experimental analysis is also accounted for in the paper.

The swget portal: Navigating and acting on the web of linked data

This paper presents the swget portal. By using the portal, users can instruct software modules to (virtually) move from one place (data source) to another on theWeb of Data, interpret knowledge and trigger actions much in the same spirit of intelligent agents. Instructions are specified via navigational expressions in the NautiLOD language. Such expressions are included into swget scripts that is, RDF documents that can be shared, modified, and reused. We discuss examples with real data in dierent scenarios showing the usefulness and potentialities of the portal. We also provide an evaluation of the performance of the portal.

Building knowledge maps of Web graphs

We research the problem of building knowledge maps of graph-like information. There exist well-consolidated cartographic principles and techniques for mapping physical landscapes. However, we live in the digital era and similarly to the Earth, the Web is simply too large and its interrelations too complex for anyone to grasp much of it through direct observation. Thus, the problem of applying cartographic principles also to digital landscapes is intriguing. We introduce a mathematical formalism that captures the general notion of map of a graph and enables its development and manipulation in a semi-automated way. We present an implementation of our formalism on the Web of Linked Data graph and discuss algorithms that efficiently generate and combine (via an algebra) regions and maps. We present the MaGe tool, implementing the map framework, and discuss examples of knowledge maps.

S+EPPs: construct and explore bisimulation summaries, plus optimize navigational queries; all on existing SPARQL systems

We demonstrate S+EPPs, a system that provides fast construction of bisimulation summaries using graph analytics platforms, and then enhances existing SPARQL engines to support summary-based exploration and navigational query optimization. The construction component adds a novel optimization to a parallel bisimulation algorithm implemented on a multi-core graph processing framework. We show that for several large, disk resident, real world graphs, full summary construction can be completed in roughly the same time as the data load. The query translation component supports Extended Property Paths (EPPs), an enhancement of SPARQL 1.1 property paths that can express a significantly larger class of navigational queries. EPPs are implemented via rewritings into a widely used SPARQL subset. The optimization component can (transparently to users) translate EPPs defined on instance graphs into EPPs that take advantage of bisimulation summaries. S+EPPs combines the query and optimization translations to enable summary-based optimization of graph traversal queries on top of off-the-shelf SPARQL processors. The demonstration showcases the construction of bisimulation summaries of graphs (ranging from millions to billions of edges), together with the exploration benefits and the navigational query speedups obtained by leveraging summaries stored alongside the original datasets.