Modelling retrieval models in a probabilistic relational algebra with a new operator: the relational Bayes

Roelleke, Thomas; Wu, Hengzhi; Wang, Jun; Azzam, Hany

doi:10.1007/s00778-007-0073-y

Cited by 22 publications

(15 citation statements)

References 48 publications

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“…The PDatalog layer in HySpirit itself is embedded in an abstraction hierarchy similar to programming languages. A probabilistic relational algebra (PRA) [16] is the 'machine layer' of the HySpirit stack on which PDatalog is built upon. PRA offers typical relational operations like SELECT, PROJECT, UNION, JOIN and SUBTRACTION in a probabilistic fashion.…”

Section: Discussionmentioning

confidence: 99%

“…The following program show-cases conditional atoms, which are a main concept in HySpirit PDatalog. While [16,13] describes the relational Bayes and the translation of probabilistic SQL (ProbSQL) to probabilistic relational algebra (PRA) expressions, we provide here for the first time the semantics for PDatalog.…”

Section: P(body Is True)mentioning

confidence: 99%

“…Then, if a query involves access to connected relations, the clients sends a request (usually in PRA or PDatalog language, but eventually also in SQL or Prob-SQL [16]) to the server engines, and the server returns the respective tuples. [9] provides more details about the clientserver architecture to scale applications.…”

Section: Parallel and Distributed Processingmentioning

confidence: 99%

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Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit

Frommholz

Roelleke

2016

Datenbank Spektrum

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Probabilistic Datalog (PDatalog, proposed in 1995) is a probabilistic variant of Datalog and a nice conceptual idea to model Information Retrieval in a logical, rule-based programming paradigm. Making PDatalog work in real-world applications requires more than probabilistic facts and rules, and the semantics associated with the evaluation of the programs. We report in this paper some of the key features of the HySpirit system required to scale the execution of PDatalog programs.Firstly, there is the requirement to express probability estimation in PDatalog. Secondly, fuzzy-like predicates are required to model vague predicates (e.g. vague match of attributes such as age or price). Thirdly, to handle large data sets there are scalability issues to be addressed, and therefore, HySpirit provides probabilistic relational indexes and parallel and distributed processing. The main contribution of this paper is a consolidated view on the methods of the HySpirit system to make PDatalog applicable in real-scale applications that involve a wide range of requirements typical for data (information) management and analysis.

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Section: Discussionmentioning

confidence: 99%

Section: P(body Is True)mentioning

confidence: 99%

Section: Parallel and Distributed Processingmentioning

confidence: 99%

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Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit

Frommholz

Roelleke

2016

Datenbank Spektrum

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“…We indicated above that we are not restricted to this particular retrieval method, but can use the expressive power of FVPD, PD or even PRA to introduce new retrieval approaches to annotation-based IR within POLAR. For example, Rölleke et al (2007) shows possible alternative retrieval functions expressed in PD and based on well-known concepts like, e.g., language models. These retrieval approaches might be integrated into POLAR as distinctive FVPD dialects or even on the PD or PRA level.…”

Section: Term_k(td) :-Acc_fragment(ds) and Fragment_term(ts)mentioning

confidence: 99%

A probabilistic framework for information modelling and retrieval based on user annotations on digital objects

Frommholz

2009

SIGIR Forum

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Annotations are a means to make critical remarks, to explain and comment things, to add notes and give opinions, and to relate objects. Nowadays, they can be found in digital libraries and collaboratories, for example as a building block for scientific discussion on the one hand or as private notes on the other. We further find them in product reviews, scientific databases and many "Web 2.0" applications; even well-established concepts like emails can be regarded as annotations in a certain sense. Digital annotations can be (textual) comments, markings (i.e. highlighted parts) and references to other documents or document parts. Since annotations convey information which is potentially important to satisfy a user's information need, this thesis tries to answer the question of how to exploit annotations for information retrieval. It gives a first answer to the question if retrieval effectiveness can be improved with annotations.A survey of the "annotation universe" reveals some facets of annotations; for example, they can be content level annotations (extending the content of the annotation object) or meta level ones (saying something about the annotated object). Besides the annotations themselves, other objects created during the process of annotation can be interesting for retrieval, these being the annotated fragments. These objects are integrated into an object-oriented model comprising digital objects such as structured documents and annotations as well as fragments. In this model, the different relationships among the various objects are reflected. From this model, the basic data structure for annotation-based retrieval, the structured annotation hypertext, is derived.In order to thoroughly exploit the information contained in structured annotation hypertexts, a probabilistic, object-oriented logical framework called POLAR is introduced. In PO-LAR, structured annotation hypertexts can be modelled by means of probabilistic propositions and four-valued logics. POLAR allows for specifying several relationships among annotations and annotated (sub)parts or fragments. Queries can be posed to extract the knowledge contained in structured annotation hypertexts. POLAR supports annotation-based retrieval, i.e. document and discussion search, by applying an augmentation strategy (knowledge augmentation, propagating propositions from subcontexts like annotations, or relevance augmentation, where retrieval status values are propagated) in conjunction with probabilistic inference, where P (d → q), the probability that a document d implies a query q, is estimated. POLAR's semantics is based on possible worlds and accessibility relations. It is implemented on top of four-valued probabilistic Datalog.POLAR's core retrieval functionality, knowledge augmentation with probabilistic inference, is evaluated for discussion and document search. The experiments show that all relevant POLAR objects, merged annotation targets, fragments and content annotations, are able to increase retrieval effectiveness when used as a cont...

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“…Therefore, constructing of the data services should include two implementations: database and data service application. In the past, many researchers proposed methodologies for the construction of databases and a few of the methods dealt with the issue of constructing data retrieve models and model transformation [2], [9], [21], [24]. A number of researches made many attempts in the area of applying the model-driven approach to construct SOA-based applications.…”

Section: Introductionmentioning

confidence: 99%

Applying Model-Driven Approach to Building Rapid Distributed Data Services

Huang

2012

IEICE Trans. Inf. & Syst.

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SUMMARYThe globalization of commerce has increased the importance of retrieving and updating complex and distributed information efficiently. Web services currently show that the most promise for building distributed application systems and model-driven architecture is a new approach to developing such applications. The expanding scale and complexity of enterprise information systems (EISs) under distributed computing environments has made sharing and exchanging data particularly challenging. Data services are applications tailored specifically for information oriented tasks to deal with business service requirements, and are heavily dependent on the distributed architecture of consumer data processing. The implementation of a data service can eliminate inconsistency among various application systems in the exchange of data. This paper proposes a data-oriented model-driven developmental framework to deal with these issues, in which a platform independent model (PIM) is divided into a service model, a logic data model, and a service composition model. We also divide a platform specific model (PSM) into a physical data model and a data service model. In this development method, we define five metamodels and outline a set of rules governing the transformation from PIMs into PSMs. A code generator is also included to transform each PSM into the application code. We include a case study to demonstrate the feasibility and merits of the proposed development framework with a case study.

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Modelling retrieval models in a probabilistic relational algebra with a new operator: the relational Bayes

Cited by 22 publications

References 48 publications

Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit

Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit

A probabilistic framework for information modelling and retrieval based on user annotations on digital objects

Applying Model-Driven Approach to Building Rapid Distributed Data Services

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