Schema Evolution publication categorizer

The Linked Open Data community publishes an increasing number of data sources on the so-called Data Web and interlinks them to support data integration applications. We investigate how the composition of existing links and mappings can
help discovering new links and mappings between LOD sources. Often there will be many alternatives for composition so that the problem arises which paths can provide the best linking results with the least computation effort. We therefore investigate

The purely manual specification of semantic correspondences between schemas is almost infeasible for very large schemas or when many different schemas have to be matched. Hence, solving such large-scale match tasks asks for automatic or semi-automatic schema matching approaches. Large-scale matching needs especially be supported for XML schemas and different kinds of ontologies due to their increasing use and size, e.g. in e-business, web and life science appli-cations.

The Clio project at IBM Almaden investigates foundational aspects of data transformation, with particular emphasis on the design and execution of schema mappings. We now use Clio as part of a broader data-flow framework in which mappings are just one component. These data-flows express complex transformations between several source and target schemas and require multiple mappings to be specified. This paper describes research issues we have encountered as we try to create and run these mapping-based data-flows.

Recently, schema mapping has found considerable interest in both
research and practice. Determining matching components of database or XML
schemas is needed in many applications, e.g. for e-business and data integra-
tion. In this paper a complete generic solution of the schema mapping problem
is presented. A hybrid semantic schema mapping algorithm which semi-
automatically finds mappings between two data representation schemas is in-
troduced. The algorithm finds mappings based on the hierarchical organization

In different areas ontologies have been developed and many of these ontologies contain overlapping information. Often we would therefore want to be able to use multiple ontologies. To obtain good results, we need to find the relationships between terms in the different ontologies, i.e. we need to align them. Currently, there already exist a number of different alignment strategies. However, it is usually difficult for a user that needs to align two ontologies to decide which of the different available strategies are the most suitable.

Semantic mapping is a fundamental step towards application interoperability, data integration and service oriented computing over the Internet. It consists in matching semantically equivalent concepts coming from heterogeneous data sources. This basic task is nevertheless tedious and often error prone if handled manually. Therefore, many systems have been developed for its automation.

To date, the principal use case for schema matching research has
been as a precursor for code generation, i.e., constructing
mappings between schema elements with the end goal of data
transfer. In this paper, we argue that schema matching plays
valuable roles independent of mapping construction, especially as
schemata grow to industrial scales. Specifically, in large
enterprises human decision makers and planners are often the
immediate consumer of information derived from schema
matchers, instead of schema mapping tools. We list a set of real

Information integration is a key challenge faced by all major organizations, business and governmental ones alike. Two research facets of this challenge that have received considerable attention in recent years are data exchange and data integration. The study of data exchange and data integration has been facilitated by the systematic use of schema mappings, which are high-level specifications that describe the relationship between two database schemas.

There is a great deal of research on ontology integration which makes use of rich logical constraints to reason about the structural and logical alignment of ontologies. There is also considerable work on matching data instances from heterogeneous schema or ontologies. However, little work exploits the fact that ontologies include both data and structure. We aim to close this gap by presenting a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies.

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.

We present the AgreementMaker system for matching real-world schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing dierent levels of granularity of the components being matched (conceptual vs.

Schema mappings are high-level specifications that describe the relationship between two database schemas. They are an important tool in several areas of database research, notably in data integration and data exchange. However, a concrete theory of schema mapping optimization including the formulation of optimality criteria

The inversion of schema mappings has been identified as one of the fundamental operators for the development of a
general framework for metadata management. In fact, during the last years three alternative notions of inversion for

A schema mapping is a declarative specification of the relationship between instances of a source schema and a target schema. The data exchange (or data translation) problem asks: given an instance over the source schema, materialize an instance (or solution) over the target schema that satisfies the schema mapping. In general, a given source instance may have numerous different solutions. Among all the solutions, universal solutions and core universal solutions have been singled out and extensively studied.

The recent growing of the Semantic Web requires the need to cope with highly semantic heterogeneities among available ontologies. Ontology matching techniques aim to tackle this problem by establishing correspondences between ontologies’ elements. An intricate obstacle faces the ontology matching problem is its scalability against large number and large-scale ontologies. To tackle these challenges, in this paper, we propose a new matching framework based on Prüfer sequences. The proposed approach is applicable for matching a database of XML trees .

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.

Schema evolution poses serious challenges in historical data management. Traditionally the archival data has been (i) either migrated under the current schema version, to ease querying, but compromising archival quality, or (ii) maintained under the original schema version in which they firstly appeared, leading to a perfect archival quality, but to a taxing query interface.

System interoperability is a well known issue, especially for heterogeneous information systems, where ontology-based representations may support automatic and user-transparent integration. In this paper we present X-SOM: an ontology mapping and integration tool. The contribution of our tool is a modular and extensible architecture that automatically combines several matching techniques by means of a neural network, performing also ontology debugging to avoid inconsistencies.

The life of a modern Information System is often characterized by (i) a push toward integration with other systems, and (ii) the evolution of its data management core in response to continuously changing application requirements. Most of the current proposals dealing with these issues from a database perspective rely on the formal notions of mapping and query rewriting.

The old problem of managing the history of database information is now made more urgent and complex by fast-spreading web information systems. Indeed, systems such as Wikipedia are faced with the challenge of managing the history of their databases in the face of intense database schema evolution. Our PRIMA system addresses this difficult problem by introducing two key pieces of new technology.

Schema integration is the process by which schemata from heterogeneous databases are conceptually integrated into a single cohesive schema. In this work we propose a modeling framework for schema integration, capturing the inherent uncertainty accompanying the integration process. The model utilizes a fuzzy framework to express a confidence measure, associated with the outcome of a schema integration process.

Information seeking is the process in which human beings recourse to information resources in order to increase their level of knowledge with respect to their goals. In this paper we offer a methodology for automating the evolution of ontologies and share the results of our experiments in supporting a user in seeking information using interactive systems. The main conclusion of our experiments is that if one narrows down the scope of the domain, ontologies can be extracted with a very high level of precision (more than 90% in some cases).

We propose the use of application semantics to enhance the process of semantic reconciliation. Application semantics involve those elements of the business reasoning that affect the way concepts are presented to users, their layout, etc. In particular, we pursue in this paper the notion of precedence, in which temporal constraints determine the ordering of concepts when presented to the user.

Schema matching is a basic operation of data integration, and several tools for automating it have been proposed and evaluated in the database community. Research in this area reveals that there is no single schema matcher that is guaranteed to succeed in finding a good mapping for all possible domains and, thus, an ensemble of schema matchers should be considered. In this paper, we introduce schema metamatching, a general framework for composing an arbitrary ensemble of schema matchers and generating a list of best ranked schema mappings.

In today's Web, many functionality-wise similar Web services are offered through heterogeneous interfaces (operation
definitions) and business protocols (ordering constraints defined on legal operation invocation sequences). The typical
approach to enable interoperation in such a heterogeneous
setting is through developing adapters. There have been approaches for classifying possible mismatches between service
interfaces and business protocols to facilitate adapter development. However, the hard job is that of identifying, given
two service specications, the actual mismatches between