Representation and Reasoning about Changing Semantics in Heterogeneous Data Sources

Zhu, Hongwei; Madnick, Stuart E.; Siegel, Michael

doi:10.2139/ssrn.577242

Cited by 2 publications

(2 citation statements)

References 19 publications

(16 reference statements)

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“…For detailed descriptions, interested readers are referred to [15] for the corporate householding scenario (where we illustrate how entity aggregational ontological heterogeneity is resolved), and [21] for the Yugoslavia example (where we show how temporal entity aggregational heterogeneity is resolved). We are currently extending COIN to address the problem of relationship 8 Mediated results are rounded for easy reading.…”

Section: Fig 9 Reconciliation Of Semantic Differences In Mdq1mentioning

confidence: 99%

Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity

Zhu

Madnick

2005

SSRN Journal

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-In this paper, we first identify semantic heterogeneities that, when not resolved, often cause serious data quality problems. We discuss the especially challenging problems of temporal and aggregational ontological heterogeneity, which concerns how complex entities and their relationships are aggregated and reinterpreted over time. Then we illustrate how the COntext INterchange (COIN) technology can be used to capture data semantics and reconcile semantic heterogeneities in a scalable manner, thereby improving data quality.Index Terms -Data Semantics, Semantic Heterogeneity, Aggregation, Temporal, Ontology, Context. I. INTRODUCTIONN our research, we have discovered that many problems arise due to confusion regarding data semantics. To illustrate how complex this can become, consider Fig. 1. This data summarizes the P/E ratio for DaimlerChrysler obtained from four different financial information sources -all obtained on the same day within minutes of each other. Note that the four sources gave radically different values for P/E ratio. are all correct! The issue is, what do you really mean by "P/E ratio" 1 . The answer lies in the multiple interpretations and uses of the term "P/E ratio" in financial circles. The earnings are for the entire year in some sources but in one source are only for the last quarter. Even when earnings are for a full year, are they:-the last 12 months? -the last calendar year? -the last fiscal year? or -the last three historical quarters and the estimated current quarter -a popular usage? Such information, which we call context, is often not explicitly captured in a form that can be used by the query answering system to reconcile semantic differences in data from different sources. Serious consequences can result from not being aware of the differences in contexts and data semantics. Consider a financial trader that used DBC to get P/E ratio information yesterday and got 19.19. Today he used Bloomberg and got 5.57 (low P/E's usually indicate good bargains) -thinking that something wonderful had happened he might decide to buy many shares of DaimlerChrysler today. In fact, nothing had actually changed, except for changing the source that he used. It would be natural for this trader (after possibly losing a significant amount of money due to this decision) to feel that he had encountered a data quality problem.We would argue that what appeared to be a data quality problem is actually a data misinterpretation problem. The data source did not have any "error," the data that it provided was exactly the data that it intended to provideit just did not have the meaning that the receiver expected. In other words, the issue is not what is right or wrong, it is about how data in one context can be used in a different context.Before going any further, it should be noted that if all sources and all receivers of data always had the exact same meanings, this problem would not occur. This is a desirable goal -one frequently sought through 1 Some of these sites even provide a glossary which give...

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Section: Fig 9 Reconciliation Of Semantic Differences In Mdq1mentioning

confidence: 99%

Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity

Zhu

Madnick

2005

SSRN Journal

Self Cite

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“…A metadata description was added to COIN (the wellknown information integration project of MIT) system through the context of its receiver client to solve temporal semantic conflicts [15]. A temporal ontology model based on OWL has been presented [16] previously: this model expressed valid time of knowledge, and therefore formed a widely supported universal storage model and management mechanism.…”

Section: Introductionmentioning

confidence: 99%

Research on Temporal Operators and Applications of Temporal Ontology

Fang¹,

Sun

Yan³

et al. 2014

Computers and Advanced Technology in Education / 820: Modelling, Simulation and Identification / 821: Environmental Management

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Ontology, as a knowledge representation approach, continues to evolve over time as the domain develops. It is of particular importance when introducing ontologies into the information integration field, to eliminate semantic conflicts brought about by changes along the time axis. This paper first introduces a temporal ontology model which can be used to express and deal with time related information, then defines a set of rich query operators and a basic reasoning operator. Further, a prototype Temporal Ontology Management System (TOMS) enhanced with the above operators is applied to an information integration background with temporal semantic conflicts. Testing shows that TOMS is both capable of managing temporal ontologies and supporting temporal queries and basic reasoning.

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Representation and Reasoning about Changing Semantics in Heterogeneous Data Sources

Cited by 2 publications

References 19 publications

Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity

Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity

Research on Temporal Operators and Applications of Temporal Ontology

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