Presentation on
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The Ontology To Get Data Access To Big Data
Presented by
MAHESH RAVINDRA PATIL
Guided by
Mrs. DIPTI PATIL
Department of Computer Engineering,
KCES’s College of Engineering and IT , Jalgaon, Maharashtra, India
content
Department of Computer Engg, KCES’s COEIT,j algaon
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ABSTRACT
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Though processing time-dependent data has been investigated for a long time, the research on temporal and especially stream reasoning over linked open data and anthologies is reaching its
high point these days. In this tutorial, we give an overview of state-of-the art query languages
and engines for temporal and stream reasoning. On a more detailed level, we discuss the new
language STARQL (Reasoning-based Query Language for Streaming and Temporal ontology Access). STARQL is designed as an expressive and flexible stream query framework that offers
the possibility to embed different (temporal) description logics as filter query languages over ontologies, and hence it can be used within the OBDA paradigm (Ontology Based Data Access
in the classical sense) and within the ABDEO paradigm (Accessing Big Data over Expressive Ontologies).
What is ontology
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Although it is required from an ontology to be formally defined, there is no common definition of the term "ontology" itself. The definitions can be categorized into roughly three groups:
The definition 1 is the meaning in philosophy as we have discussed above, however it has many implications for the AI purposes. The second definition is generally accepted as a definition of what an ontology is for the AI community. The last third definition views an ontology as an inner body of knowledge, not as the way to describe the knowledge.
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Type of ontology data
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Type:-
1.Domain ontology
2. Upper ontology
3. Hybrid ontology
Domain ontology
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A domain ontology (or domain-specific ontology) represents concepts which belong to a part of the world, such as biology or politics. Each domain ontology typically models domain-specific definitions of terms.
For example, the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punched card" and "video card" meanings.
Upper ontology
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An upper ontology (or foundation ontology) is a model of the common relations and objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that contains the terms and associated object descriptions as they are used in various relevant domain ontology's.
Standardized upper ontology's available for use include BFO, BORO method, Dublin Core, GFO, Cyc, SUMO, UMBEL, the Unified Foundational Ontology (UFO), and DOLCE. Word Net has been considered an upper ontology by some and has been used as a linguistic tool for learning domain ontology.
Hybrid ontology
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The Gellish ontology is an example of a combination of an upper and a domain ontology.
Components
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Components
Contemporary ontology's share many structural similarities, regardless of the language in which they are expressed. Most ontology's describe individuals (instances), classes (concepts), attributes and relations. In this section each of these components is discussed in turn.
Characteristics
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Example
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Examples of ontology's developed using METHONTOLOGY are :
CHEMICALS (contains knowledge in the field of chemical elements and crystalline structures).
Monatomic Ions (collects information about monatomic ions).
Environmental pollutant ontology's (represent methods to identify various polluting components in water, air, ground, and the maximum permissible concentrations of these substances, considering existing laws).
The reference ontology (basic ontology for describing ontology's of “yellow pages” type directories).
Silicate ontology (simulates the properties of minerals and silicates in particular).
Ontologies developed in the IST-1999-2010,589 MKBEEM project (travel, textile catalogs, housing, used in the Multilanguage e-commerce platform).
Onto Roadmap (meta-ontology, ontology development methodologies, ontology development tools, ontology-related events (conferences, seminars, etc.)).
Application
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Advantages
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Disadvantages
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Conclusion
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In this study, we demonstrated the feasibility and advantages of using an ontology-based semantic data integration approach to link heterogeneous data sources to create a pooled data set of IDA. With a semantic data integration approach, many data processing needs and knowledge can be encoded in the ontology, and thus data analysts no longer need to worry about the syntactic, schematic, and semantic heterogeneities in data from different sources.
Future works
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A research is a never ending when one does some work in an area.
It is obvious that one can have insight regarding what can be done in future in the area.
Therefore, following are future scope of this research work:
Ranking Scheme: Although in this thesis, we have provided a way to retrieve most relevant responses against a user query. But there is also a future scope to apply a ranking scheme so that most relevant pages/responses can be provided first.
Reference
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Thank You