Salvatore Raunich

A new algorithm for clustering search results

We develop a new algorithm for clustering search results. Differently from many other clustering systems that have been recently proposed as a post-processing step for Web search engines, our system is not based on phrase analysis inside snippets, but instead uses latent semantic indexing on the whole document content. A main contribution of the paper is a novel strategy - called dynamic SVD clustering - to discover the optimal number of singular values to be used for clustering purposes. Moreover, the algorithm is such that the SVD computation step has in practice good performance, which makes it feasible to perform clustering when term vectors are available. We show that the algorithm has very good classification performance, and that it can be effectively used to cluster results of a search engine to make them easier to browse by users. The algorithm has being integrated into the Noodles search engine, a tool for searching and clustering Web and desktop documents.

Schema mapping verification: the spicy way

Schema mapping algorithms rely on value correspondences - i.e., correspondences among semantically related attributes - to produce complex transformations among data sources. These correspondences are either manually specified or suggested by separate modules called schema matchers. The quality of mappings produced by a mapping generation tool strongly depends on the quality of the input correspondences. In this paper, we introduce the Spicy system, a novel approach to the problem of verifying the quality of mappings. Spicy is based on a three-layer architecture, in which a schema matching module is used to provide input to a mapping generation module. Then, a third module, the mapping verification module, is used to check candidate mappings and choose the ones that represent better transformations of the source into the target. At the core of the system stands a new technique for comparing the structure and actual content of trees, called structural analysis. Experimental results show that, by carefully designing the comparison algorithm, it is possible to achieve both good scalability and high precision in mapping selection.

Core schema mappings

Research has investigated mappings among data sources under two perspectives. On one side, there are studies of practical tools for schema mapping generation; these focus on algorithms to generate mappings based on visual specifications provided by users. On the other side, we have theoretical researches about data exchange. These study how to generate a solution - i.e., a target instance - given a set of mappings usually specified as tuple generating dependencies. However, despite the fact that the notion of a core of a data exchange solution has been formally identified as an optimal solution, there are yet no mapping systems that support core computations. In this paper we introduce several new algorithms that contribute to bridge the gap between the practice of mapping generation and the theory of data exchange. We show how, given a mapping scenario, it is possible to generate an executable script that computes core solutions for the corresponding data exchange problem. The algorithms have been implemented and tested using common runtime engines to show that they guarantee very good performances, orders of magnitudes better than those of known algorithms that compute the core as a post-processing step.

ATOM: Automatic target-driven ontology merging

The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies to provide a unified view on them. We demonstrate a new automatic approach to merge large taxonomies such as product catalogs or web directories. Our approach is based on an equivalence matching between a source and target taxonomy to merge them. It is target-driven, i.e. it preserves the structure of the target taxonomy as much as possible. Further, we show how the approach can utilize additional relationships between source and target concepts to semantically improve the merge result.

Concise and expressive mappings with +Spicy

We introduce the +Spicy mapping system. The system is based on a number of novel algorithms that contribute to increase the quality and expressiveness of mappings. +Spicy integrates the computation of core solutions in the mapping generation process in a highly efficient way, based on a natural rewriting of the given mappings. This allows for an efficient implementation of core computations using common runtime languages like SQL or XQuery and guarantees very good performances, orders of magnitude better than those of previous algorithms. The rewriting algorithm can be applied both to mappings generated by the system, or to pre-defined mappings provided as part of the input. To do this, the system was enriched with a set of expressive primitives, so that +Spicy is the first mapping system that brings together a sophisticate and expressive mapping generation algorithm with an efficient strategy to compute core solutions.

Towards a Benchmark for Ontology Merging

Benchmarking approaches for ontology merging is challenging and has received little attention so far. A key problem is that there is in general no single best solution for a merge task and that merging may either be performed symmetrically or asymmetrically. As a first step to evaluate the quality of ontology merging solutions we propose the use of general metrics such as the relative coverage of the input ontologies, the compactness of the merge result as well as the degree of introduced redundancy. We use these metrics to evaluate three merge approaches for different merge scenarios.

Core schema mappings: Scalable core computations in data exchange

Research has investigated mappings among data sources under two perspectives. On the one side, there are studies of practical tools for schema mapping generation; these focus on algorithms to generate mappings based on visual specifications provided by users. On the other side, we have theoretical researches about data exchange. These study how to generate a solution - i.e., a target instance - given a set of mappings usually specified as tuple generating dependencies. Since the notion of a core solution has been formally identified as an optimal solution, it is very important to efficiently support core computations in mapping systems. In this paper, we introduce several new algorithms that contribute to bridge the gap between the practice of mapping generation and the theory of data exchange. We show how, given a mapping scenario, it is possible to generate an executable script that computes core solutions for the corresponding data exchange problem. The algorithms have been implemented and tested using common runtime engines to show that they guarantee very good performances, orders of magnitudes better than those of known algorithms that compute the core as a post-processing step.

The Spicy system: towards a notion of mapping quality

We introduce the Spicy system, a novel approach to the problem of automatically selecting the best mappings among two data sources. Known schema mapping algorithms rely on value correspondences -- i.e. correspondences among semantically related attributes -- to produce complex transformations among data sources. Spicy brings together schema matching and mapping generation tools to further automate this process. A key observation, here, is that the quality of the mappings is strongly influenced by the quality of the input correspondences. To address this problem, Spicy adopts a three-layer architecture, in which a schema matching module is used to provide input to a mapping generation module. Then, a third module, the mapping verification module, is used to check candidate mappings and choose the ones that represent better transformations of the source into the target. At the core of the system stands a new technique for comparing the structure and actual content of trees, called structural analysis. Experimental results show that our mapping discovery algorithm achieves both good scalability and high precision in mapping selection.

Target-driven merging of taxonomies with Atom

The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies. We propose a new taxonomy merging algorithm called Atom that, given as input two taxonomies and a match mapping between them, can generate an integrated taxonomy in a largely automatic manner. The approach is target-driven, i.e. we merge a source taxonomy into the target taxonomy and preserve the target ontology as much as possible. In contrast to previous approaches, Atom does not aim at fully preserving all input concepts and relationships but strives to reduce the semantic heterogeneity of the merge results for improved understandability. Atom can also exploit advanced match mappings containing is-a relationships in addition to equivalence relationships between concepts of the input taxonomies. We evaluate Atom for synthetic and real-world scenarios and compare it with a full merge solution.

Noodles: a clustering engine for the web

The paper describes the Noodles system, a clustering engine for Web and desktop searches. By employing a new algorithm for document clustering, based on Latent Semantic Indexing, Noodles provides good classification power to simplify browsing of search results by casual users. In the paper, we provide some background about the problem of clustering search results, give an overview of the novel techniques implemented in the system, and present its architecture and main features.