Summarizing semantic graphs: a survey <s> Conclusions and future work <s> The evolution of ontologies is an undisputed necessity in ontologybased data integration. In such systems ontologies are used as global schema in order to formulate queries that are answered by the data integration systems. Yet, few research efforts have focused on addressing the need to reflect ontology evolution onto the underlying data integration systems. In most of these systems, when ontologies change their relations with the data sources, i.e., the mappings, are recreated manually, a process which is known to be error-prone and timeconsuming. In this paper, we provide a solution that allows query answering under evolving ontologies without mapping redefinition. To achieve that, query rewriting techniques are exploited in order to produce equivalent rewritings among ontology versions. Whenever equivalent rewritings cannot be produced we a) guide query redefinition or b) provide the best "over-approximations". We show that our approach can greatly reduce human effort spent since continuous mapping redefinition on evolving ontologies is no longer necessary. <s> BIB001 </s> Summarizing semantic graphs: a survey <s> Conclusions and future work <s> Information systems rely more and more on semantic web ontologies to share and interpret data within and across research domains. However, an important problem when dealing with ontologies is the fact that they are living artefacts and subject to change. When ontologies evolve, queries formulated using a past ontology version might become invalid and should be redefined or adapted. In this paper we propose a solution in order to identify the impact of ontology evolution on queries and to ease query migration. We present a module that receives as input the sequence of changes between the two ontology versions along with a set of queries and automatically identifies the specific change operations that affect the input queries. Besides the automatic identification of the affecting change operations, query migration is further aided by providing an explanation for the specific invalidation. This explanation is presented graphically by means of change paths that represent the evolution of the specific parts of the ontology that invalidate the query. We evaluate the time complexity of our approach and show how it can possibly reduce the human effort spent on query redefinition/adaptation. <s> BIB002 </s> Summarizing semantic graphs: a survey <s> Conclusions and future work <s> The evolution of ontologies is an undisputed necessity in ontology-based data integration. Yet, few research efforts have focused on addressing the need to reflect the evolution of ontologies used as global schemata onto the underlying data integration systems. In most of these approaches, when ontologies change their relations with the data sources, i.e., the mappings, are recreated manually, a process which is known to be error-prone and time-consuming. In this paper, we provide a solution that allows query answering in data integration systems under evolving ontologies without mapping redefinition. This is achieved by rewriting queries among ontology versions and then forwarding them to the underlying data integration systems to be answered. To this purpose, initially, we automatically detect and describe the changes among ontology versions using a high level language of changes. Those changes are interpreted as sound global-as-view (GAV) mappings, and they are used in order to produce equivalent rewritings among ontology versions. Whenever equivalent rewritings cannot be produced we a) guide query redefinition or b) provide the best ''over-approximations'', i.e., the minimally-containing and minimally-generalized rewritings. We prove that our approach imposes only a small overhead over traditional query rewriting algorithms and it is modular and scalable. Finally, we show that it can greatly reduce human effort spent since continuous mapping redefinition is no longer necessary. <s> BIB003 </s> Summarizing semantic graphs: a survey <s> Conclusions and future work <s> The aggregation of heterogeneous data from different institutions in cultural heritage and e-science has the potential to create rich data resources useful for a range of different purposes, from research to education and public interests. In this paper, we present the architecture and functionality of X3ML data exchange framework, that handles effectively and efficiently the schema mapping, URI definition and generation, and data transformation steps of the provision and aggregation process. The X3ML framework is based on the X3ML mapping definition language that offers the building blocks for describing both schema mappings and URI generation policies, and the X3ML engine, that handles the URI generation and the data transformation. The X3ML framework supports the cognitive process of mapping and it has a lot of advantages compared to other existing tools including that the schema mappings are expressed in a declarative way, and are both human and machine readable allowing domain experts to understand them, the schema matching and the URI generation policies comprise different distinct steps in the exchange workflow, and follow different life cycles. Furthermore X3ML is symmetric and potentially invertible allowing bidirectional interaction between providers and aggregator and thus supporting not only a rich aggregators’ repository but also corrections and improvements in the providers’ data bases. <s> BIB004 </s> Summarizing semantic graphs: a survey <s> Conclusions and future work <s> The aggregation of heterogeneous data from different institutions in cultural heritage and e-science has the potential to create rich data resources useful for a range of different purposes, from research to education and public interests. In this paper, we present the X3ML framework, a framework for information integration that handles effectively and efficiently the steps involved in schema mapping, uniform resource identifier (URI) definition and generation, data transformation, provision and aggregation. The framework is based on the X3ML mapping definition language for describing both schema mappings and URI generation policies and has a lot of advantages when compared with other relevant frameworks. We describe the architecture of the framework as well as details on the various available components. Usability aspects are discussed and performance metrics are demonstrated. The high impact of our work is verified via the increasing number of international projects that adopt and use this framework. <s> BIB005
In this survey, we present a comprehensive state-of-the-art in semantic graph summarization. To this direction, we introduce a taxonomy of the works in the area (based on different properties/criteria that the works adhere to), that can help practitioners and researchers to determine the method most suitable for their data and goal. In this taxonomy, we grouped the main methods of the algorithms presented into four main categories structural, statistical, pattern-mining and hybrid, identifying subcategories whenever possible. In addition, we also classified works in the field according to their input, output, availability on the internet and purpose, showing the rapidly evolving dynamics in the area. In general, RDF graph summaries can be useful in various application scenarios ranging from data understanding to query answering and from RDF graph data indexing to RDF graph visualization. Structural quotient summaries are most applicable to indexing and query answering through graph reduction; this holds especially for quotients built through equivalence relations such as bisimilarity (possibly bounded). Non-quotient summaries mostly target visualization, schema discovery and data understanding. Patternmining summaries provide in many cases logical rules besides the summary graph as part of the final result, so could be possibly more useful in RDF graph compression scenarios. Summaries could also be really useful in data integration scenarios BIB003 , where instead of generating mappings BIB005 BIB004 between data source schemas, summaries could be used to drive the definition of the mapping. Extending this to a scenario where the sources can also evolve BIB001 BIB002 , summaries can play a key role in schema understanding and mapping redefinition. Different RDF summarization scenarios each bring their very specific requirements (e.g., whether the summary size is bounded or not, whether a schema is present or not, whether to summarize the data or the schema, whether the summary needs to reflect all the structure or not etc.); in many cases more than one algorithm or family of algorithms will provide suitable results. The goal of our survey was to provide enough information to the users of these algorithms (i.e., application developers or researchers) in order to be able to easily refer to the characteristics of each approach, and evaluate their suitability to their application requirements. Despite the considerable amount of work in the area of semantic graph summarization, there still are many important open problems in the field. Below we mention two of the most notable ones. The first one is the quality of the produced RDF summary. Since the result summary for the different algorithms, varies among a selection of nodes, quotients, some other frequency structure or a complete graph with various types of nodes and links identifying a single golden standard is a complex task. On the other hand, even domain experts in many cases disagree on which specific elements should be selected in a semantic summary. However, having in mind our proposed taxonomy, we believe that the next step in the area is the establishment of different golden standards specific to each subcategory focusing on specific purpose input and output. Another open issue we perceive as really important, is the dynamic nature of all these datasets. As new information becomes available due to new experimental evidence or observations and erroneous past conceptualizations are constantly updated many datasets are rapidly changing. However, summarization in most cases and especially for big data sources is a time-consuming process that should be constantly updated to facilitate data exploration. As such, novel ideas should focus in this dynamicity, augmenting the exploration experience of end-users. The work in RDF graph summarization gains more importance as the RDF Knowledge Bases become larger and more connected and thus we expect to see additional advances in the field in the near future.