21,320 research outputs found
Schema Matching for Large-Scale Data Based on Ontology Clustering Method
Holistic schema matching is the process of identifying semantic correspondences among multiple schemas at once. The key challenge behind holistic schema matching lies in selecting an appropriate method that has the ability to maintain effectiveness and efficiency. Effectiveness refers to the quality of matching while efficiency refers to the time and memory consumed within the matching process. Several approaches have been proposed for holistic schema matching. These approaches were mainly dependent on clustering techniques. In fact, clustering aims to group the similar fields within the schemas in multiple groups or clusters. However, fields on schemas contain much complicated semantic relations due to schema level. Ontology which is a hierarchy of taxonomies, has the ability to identify semantic correspondences with various levels. Hence, this study aims to propose an ontology-based clustering approach for holistic schema matching. Two datasets have been used from ICQ query interfaces consisting of 40 interfaces, which refer to Airfare and Job. The ontology used in this study has been built using the XBenchMatch which is a benchmark lexicon that contains rich semantic correspondences for the field of schema matching. In order to accommodate the schema matching using the ontology, a rule-based clustering approach is used with multiple distance measures including Dice, Cosine and Jaccard. The evaluation has been conducted using the common information retrieval metrics; precision, recall and f-measure. In order to assess the performance of the proposed ontology-based clustering, a comparison among two experiments has been performed. The first experiment aims to conduct the ontology-based clustering approach (i.e. using ontology and rule-based clustering), while the second experiment aims to conduct the traditional clustering approaches without the use of ontology. Results show that the proposed ontology-based clustering approach has outperformed the traditional clustering approaches without ontology by achieving an f-measure of 94% for Airfare and 92% for Job datasets. This emphasizes the strength of ontology in terms of identifying correspondences with semantic level variation
A Linear Program For Holistic Matching : Assessment on Schema Matching Benchmark
International audienceSchema matching is a key task in several applications such as data integration and ontology engineering. All application fields require the matching of several schemes also known as "holistic matching", but the difficulty of the problem spawned much more attention to pairwise schema matching rather than the latter. In this paper, we propose a new approach for holistic matching. We suggest modelling the problem with some techniques borrowed from the combinatorial optimization field. We propose a linear program, named LP4HM, which extends the maximum-weighted graph matching problem with different linear constraints. The latter encompass matching setup constraints, especially cardinality and threshold constraints; and schema structural constraints, especially superclass/subclass and coherence constraints. The matching quality of LP4HM is evaluated on a recent benchmark dedicated to assessing schema matching tools. Experimentations show competitive results compared to other tools, in particular for recall and HSR quality measure
Keeping the data lake in form: proximity mining for pre-filtering schema matching
Data Lakes (DLs) are large repositories of raw datasets from disparate sources. As more datasets are ingested into a DL, there is an increasing need for efficient techniques to profile them and to detect the relationships among their schemata, commonly known as holistic schema matching. Schema matching detects similarity between the information stored in the datasets to support information discovery and retrieval. Currently, this is computationally expensive with the volume of state-of-the-art DLs. To handle this challenge, we propose a novel early-pruning approach to improve efficiency, where we collect different types of content metadata and schema metadata about the datasets, and then use this metadata in early-pruning steps to pre-filter the schema matching comparisons. This involves computing proximities between datasets based on their metadata, discovering their relationships based on overall proximities and proposing similar dataset pairs for schema matching. We improve the effectiveness of this task by introducing a supervised mining approach for effectively detecting similar datasets which are proposed for further schema matching. We conduct extensive experiments on a real-world DL which proves the success of our approach in effectively detecting similar datasets for schema matching, with recall rates of more than 85% and efficiency improvements above 70%. We empirically show the computational cost saving in space and time by applying our approach in comparison to instance-based schema matching techniques.This research was partially funded by the European Commission through the Erasmus Mundus Joint Doctorate (IT4BI-DC).Peer ReviewedPostprint (author's final draft
Towards information profiling: data lake content metadata management
There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this.We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.Peer ReviewedPostprint (author's final draft
Multilingual Schema Matching for Wikipedia Infoboxes
Recent research has taken advantage of Wikipedia's multilingualism as a
resource for cross-language information retrieval and machine translation, as
well as proposed techniques for enriching its cross-language structure. The
availability of documents in multiple languages also opens up new opportunities
for querying structured Wikipedia content, and in particular, to enable answers
that straddle different languages. As a step towards supporting such queries,
in this paper, we propose a method for identifying mappings between attributes
from infoboxes that come from pages in different languages. Our approach finds
mappings in a completely automated fashion. Because it does not require
training data, it is scalable: not only can it be used to find mappings between
many language pairs, but it is also effective for languages that are
under-represented and lack sufficient training samples. Another important
benefit of our approach is that it does not depend on syntactic similarity
between attribute names, and thus, it can be applied to language pairs that
have distinct morphologies. We have performed an extensive experimental
evaluation using a corpus consisting of pages in Portuguese, Vietnamese, and
English. The results show that not only does our approach obtain high precision
and recall, but it also outperforms state-of-the-art techniques. We also
present a case study which demonstrates that the multilingual mappings we
derive lead to substantial improvements in answer quality and coverage for
structured queries over Wikipedia content.Comment: VLDB201
AMaÏoSâAbstract Machine for Xcerpt
Web query languages promise convenient and efficient access
to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web
query language with strong emphasis on novel high-level constructs for
effective and convenient query authoring, particularly tailored to versatile
access to data in different Web formats such as XML or RDF.
However, so far it lacks an efficient implementation to supplement the
convenient language features. AMaÏoS is an abstract machine implementation
for Xcerpt that aims at efficiency and ease of deployment. It
strictly separates compilation and execution of queries: Queries are compiled
once to abstract machine code that consists in (1) a code segment
with instructions for evaluating each rule and (2) a hint segment that
provides the abstract machine with optimization hints derived by the
query compilation. This article summarizes the motivation and principles
behind AMaÏoS and discusses how its current architecture realizes
these principles
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