The document is a lecture on ontologies and databases given by Enrico Franconi. Some key points:
- An ontology is a formal conceptualization of a domain that specifies a set of constraints that define what must be true in any possible world.
- Ontology languages can range from simple to logic-based and are often expressed using diagrams. Conceptual data models like ER diagrams and UML class diagrams can also function as ontology languages.
- When querying a database via an ontology, the ontology acts as a mediator between the conceptual schema, logical schema, and data store. Deduction is used to infer additional constraints from what is specified in the ontology.
- Query answering over an ontology
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
UNIT III MINING COMMUNITIES
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in Social
Networks - Evaluating Communities – Core Methods for Community Detection & Mining Applications of Community Mining Algorithms - Node Classification in Social Networks.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Waqas Tariq
A \"sentence pattern\" in modern Natural Language Processing is often considered as a subsequent string of words (n-grams). However, in many branches of linguistics, like Pragmatics or Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to reveal the whole sophistication of grammar patterns. We present a language independent architecture for extracting from sentences more sophisticated patterns than n-grams. In this architecture a \"sentence pattern\" is considered as n-element ordered combination of sentence elements. Experiments showed that the method extracts significantly more frequent patterns than the usual n-gram approach.
Translating Ontologies in Real-World SettingsMauro Dragoni
To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. The main challenge in translating ontologies is to find the right term with respect to the domain modeled by ontology itself. Machine translation services may help in this task; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications must implement a support system addressing this task for relieve experts work in validating all translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit semantic information of the concept's context for improving the quality of label translations. The system has been tested both within the Organic.Lingua project by translating the modeled ontology in three languages and on other multilingual ontologies in order to evaluate the effectiveness of the system in other contexts. The results have been compared with the translations provided by the Microsoft Translator API and the improvements demonstrated the viability of the proposed approach.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Extraction of common conceptual components from multiple ontologiesValentina Carriero
Understanding large ontologies, with diverse semantics and modelling practices, is still an issue, and has an impact on many ontology engineering tasks. While existing methods summarise ontologies by extracting the most important nodes or subgraphs, a complete overview of an ontology, and a comparison between multiple ontologies, are not supported. Based on the hypothesis that ontologies are designed as compositions of patterns, this slides present a method able to extract conceptual components from multiple ontologies and the observed ontology design patterns implementing them.
related paper: https://arxiv.org/abs/2106.12831
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
UNIT III MINING COMMUNITIES
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in Social
Networks - Evaluating Communities – Core Methods for Community Detection & Mining Applications of Community Mining Algorithms - Node Classification in Social Networks.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Waqas Tariq
A \"sentence pattern\" in modern Natural Language Processing is often considered as a subsequent string of words (n-grams). However, in many branches of linguistics, like Pragmatics or Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to reveal the whole sophistication of grammar patterns. We present a language independent architecture for extracting from sentences more sophisticated patterns than n-grams. In this architecture a \"sentence pattern\" is considered as n-element ordered combination of sentence elements. Experiments showed that the method extracts significantly more frequent patterns than the usual n-gram approach.
Translating Ontologies in Real-World SettingsMauro Dragoni
To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. The main challenge in translating ontologies is to find the right term with respect to the domain modeled by ontology itself. Machine translation services may help in this task; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications must implement a support system addressing this task for relieve experts work in validating all translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit semantic information of the concept's context for improving the quality of label translations. The system has been tested both within the Organic.Lingua project by translating the modeled ontology in three languages and on other multilingual ontologies in order to evaluate the effectiveness of the system in other contexts. The results have been compared with the translations provided by the Microsoft Translator API and the improvements demonstrated the viability of the proposed approach.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Extraction of common conceptual components from multiple ontologiesValentina Carriero
Understanding large ontologies, with diverse semantics and modelling practices, is still an issue, and has an impact on many ontology engineering tasks. While existing methods summarise ontologies by extracting the most important nodes or subgraphs, a complete overview of an ontology, and a comparison between multiple ontologies, are not supported. Based on the hypothesis that ontologies are designed as compositions of patterns, this slides present a method able to extract conceptual components from multiple ontologies and the observed ontology design patterns implementing them.
related paper: https://arxiv.org/abs/2106.12831
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
Ontology Learning from Text
Ontology construction ‘Layer Cake’
Knowledge representation and knowledge management systems
Subtasks in ontology learning
Most Popular Ontology Learning Tools
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
Rule-based reasoning in the Semantic WebFulvio Corno
An introduction to SWRL and to rule-based reasoning languagest for the Semantic Web. The material is mostly taken from the Semantic Web Recommendations. Slides for the PhD Course on Semantic Web (http://elite.polito.it/).
In this paper we present the SMalL Ontology for malicious software classification, SMalL Java Application for antivirus systems comparison and the SMalL knowledge based file format for malware related attacks. We believe that our ontology is able to aid the development of malware prevention software by offering a common knowledge base and a clear classification of the existing malicious software. The application is a prototype regarding how this ontology might be used in conjunction with known antivirus capabilities to offer a comprehensive comparison.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
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This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Model Attribute Check Company Auto PropertyCeline George
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Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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2. Summary
◮
What is an Ontology
◮
Querying a DB via an ontology
Ontologies and Databases.
E. Franconi.
(2/38)
3. Ontologies and Constraints
◮
An ontology is a formal conceptualisation of the world: a conceptual
schema.
◮
An ontology specifies a set of constraints, which declare what should
necessarily hold in any possible world.
◮
Any possible world should conform to the constraints expressed by the
ontology.
◮
Given an ontology, a legal world description (or legal database
instance) is a finite possible world satisfying the constraints.
Ontologies and Databases.
E. Franconi.
(3/38)
4. Ontologies and Conceptual Data Models
◮
An ontology language usually introduces concepts (aka classes,
entities), properties of concepts (aka slots, attributes, roles),
relationships between concepts (aka associations), and additional
constraints.
◮
Ontology languages may be simple (e.g., involving only concepts and
taxonomies), frame-based (e.g., UML, based on concepts, properties,
and binary relationships), or logic-based (e.g. OWL, Description
Logics).
◮
Ontology languages are typically expressed by means of diagrams.
◮
Entity-Relationship schemas and UML class diagrams can be
considered as ontology languages.
Ontologies and Databases.
E. Franconi.
(4/38)
7. The role of an ontology:
an Ontology based application
Conceptual
Schema
Logical
Schema
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
8. The role of an ontology:
an Ontology based application
Constraints
Conceptual
Schema
Logical
Schema
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
9. The role of an ontology:
an Ontology based application
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
10. The role of an ontology:
an Ontology based application
Deduction
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
11. The role of an ontology:
an Ontology based application
Deduction
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
12. The role of an ontology:
an Ontology based application
Deduction
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
13. The role of an ontology:
an Ontology based application
Deduction
Constraints
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
14. The role of an ontology:
an Ontology based application
Deduction
Deduction
Constraints
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
15. The role of an ontology:
an Ontology based application
Deduction
Deduction
Constraints
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
16. The role of an ontology:
an Ontology based application
Mediator
Deduction
Deduction
Constraints
global
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
source
Data Store
Ontologies and Databases.
E. Franconi.
(7/38)
17. The role of an ontology:
an Ontology based application
Mediator
Deduction
Deduction
Constraints
global
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
← Knowledge Level
−
← Information Level
−
source
Data Store
Ontologies and Databases.
← Data Level
−
E. Franconi.
(7/38)
18. Reasoning
Given an ontology – seen as a collection of constraints – it is possible that
additional constraints can be inferred.
◮
A class is inconsistent if it denotes the empty set in any legal world
description.
◮
A class is a subclass of another class if the former denotes a subset of
the set denoted by the latter in any legal world description.
◮
Two classes are equivalent if they denote the same set in any legal
world description.
◮
A stricter constraint is inferred – e.g., a cardinality constraint – if it
holds in in any legal world description.
◮
...
Ontologies and Databases.
E. Franconi.
(8/38)
26. The democratic company
Supervisor
2..2
supervises
Employee
0..1
Employee = ∅
implies
“the classes Employee and Supervisor necessarily contain an infinite
number of instances”.
Since legal world descriptions are finite possible worlds satisfying the
constraints imposed by the ontology, the ontology is inconsistent.
Ontologies and Databases.
E. Franconi.
(12/38)
27. How many numbers?
Natural Number
1..1
rel
Even Number
1..1
Ontologies and Databases.
E. Franconi.
(13/38)
28. How many numbers?
Natural Number
1..1
rel
Even Number
1..1
implies
“the classes Natural Number and Even Number contain the same number
of instances”.
Ontologies and Databases.
E. Franconi.
(13/38)
29. How many numbers?
Natural Number
1..1
rel
Even Number
1..1
implies
“the classes Natural Number and Even Number contain the same number
of instances”.
Only if the domain is finite:
Ontologies and Databases.
Natural Number ≡ Even Number
E. Franconi.
(13/38)
30. Next on “Ontologies and Databases”:
◮
◮
What is an Ontology
Querying a DB via an ontology
◮
◮
◮
◮
◮
◮
We will see how an ontology can play the role of a “mediator”
wrapping a (source) database.
Examples will show how apparently simple cases are not easy.
We will learn about view-based query processing with GAV and LAV
mappings.
We introduce the difference between closed world and open world
semantics in this context.
We will see how only the closed world semantics should be used while
using ontologies to wrap databases, in order for the mediated system to
behave like a database (black-box metaphor)
We will see that the data complexity of query answering can be beyond
the one of SQL.
Ontologies and Databases.
E. Franconi.
(14/38)
31. The role of an ontology
Conceptual
Schema
Logical
Schema
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
32. The role of an ontology
Constraints
Conceptual
Schema
Logical
Schema
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
33. The role of an ontology
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
34. The role of an ontology
Deduction
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
35. The role of an ontology
Deduction
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
36. The role of an ontology
Deduction
Constraints
Conceptual
Schema
Logical
Schema
Query
Result
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
37. The role of an ontology
Deduction
Constraints
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
38. The role of an ontology
Deduction
Deduction
Constraints
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
39. The role of an ontology
Deduction
Deduction
Constraints
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
40. The role of an ontology
Mediator
Deduction
Deduction
Constraints
global
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
source
Data Store
Ontologies and Databases.
E. Franconi.
(15/38)
41. The role of an ontology
Mediator
Deduction
Deduction
Constraints
global
Conceptual
Schema
Query
Logical
Schema
Query
Result
Result
← Knowledge Level
−
← Information Level
−
source
Data Store
Ontologies and Databases.
← Data Level
−
E. Franconi.
(15/38)
42. Querying a Database with Constraints
◮
Basic assumption: consistent information with respect to the
constraints introduced by the ontology
◮
A Database with Constraints: complete information about each term
appearing in the ontology
◮
Problem: answer a query over the ontology vocabulary
Ontologies and Databases.
E. Franconi.
(16/38)
43. Querying a Database with Constraints
◮
Basic assumption: consistent information with respect to the
constraints introduced by the ontology
◮
A Database with Constraints: complete information about each term
appearing in the ontology
◮
Problem: answer a query over the ontology vocabulary
◮
Solution: use a standard DB technology (e.g., SQL, datalog, etc)
Ontologies and Databases.
E. Franconi.
(16/38)
44. Querying a Database with Constraints
Employee
Works-for
1..⋆
Project
Manager
Ontologies and Databases.
E. Franconi.
(17/38)
45. Querying a Database with Constraints
Employee
Works-for
1..⋆
Project
Manager
Employee = { John, Mary, Paul }
Manager = { John, Paul }
Works-for = { John,Prj-A , Mary,Prj-B }
Project = { Prj-A, Prj-B }
Ontologies and Databases.
E. Franconi.
(17/38)
46. Querying a Database with Constraints
Employee
Works-for
1..⋆
Project
Manager
Employee = { John, Mary, Paul }
Manager = { John, Paul }
Works-for = { John,Prj-A , Mary,Prj-B }
Project = { Prj-A, Prj-B }
Q(X) :- Manager(X), Works-for(X,Y), Project(Y)
=⇒ { John }
Ontologies and Databases.
E. Franconi.
(17/38)
47. Querying a Database with Constraints
over an extended vocabulary (DBox)
◮
Having a classical database with constraints is against the principle
that an ontology presents a richer vocabulary than the data stores
(i.e., it plays the role of an ontology).
Ontologies and Databases.
E. Franconi.
(18/38)
48. Querying a Database with Constraints
over an extended vocabulary (DBox)
◮
Having a classical database with constraints is against the principle
that an ontology presents a richer vocabulary than the data stores
(i.e., it plays the role of an ontology).
◮
A Database with Constraints over an extended vocabulary (or
conceptual schema with exact views, or DBox): complete information
about some term appearing in the ontology
◮
Standard DB technologies do not apply
◮
The query answering problem in this context is inherently complex
Ontologies and Databases.
E. Franconi.
(18/38)
49. Querying a Database with Constraints
over an extended vocabulary (DBox)
Employee
Works-for
1..⋆
Project
Manager
Manager = { John, Paul }
Works-for = { John,Prj-A , Mary,Prj-B }
Project = { Prj-A, Prj-B }
Ontologies and Databases.
E. Franconi.
(19/38)
50. Querying a Database with Constraints
over an extended vocabulary (DBox)
Employee
Works-for
1..⋆
Project
Manager
Manager = { John, Paul }
Works-for = { John,Prj-A , Mary,Prj-B }
Project = { Prj-A, Prj-B }
Q(X) :- Employee(X)
Ontologies and Databases.
E. Franconi.
(19/38)
51. Querying a Database with Constraints
over an extended vocabulary (DBox)
Employee
Works-for
1..⋆
Project
Manager
Manager = { John, Paul }
Works-for = { John,Prj-A , Mary,Prj-B }
Project = { Prj-A, Prj-B }
Q(X) :- Employee(X)
=⇒ { John, Paul, Mary }
Ontologies and Databases.
E. Franconi.
(19/38)
52. Querying a Database with Constraints
over an extended vocabulary (DBox)
Employee
Works-for
1..⋆
Project
Manager
Manager = { John, Paul }
Works-for = { John,Prj-A , Mary,Prj-B }
Project = { Prj-A, Prj-B }
Q(X) :- Employee(X)
=⇒ { John, Paul, Mary }
=⇒
Q’(X) :- Manager(X) ∪ Works-for(X,Y)
Ontologies and Databases.
E. Franconi.
(19/38)
60. Querying a sound DB with Constraints
over an extended vocabulary (ABox)
1. Classical DB with constraints: complete information about all terms
appearing in the ontology
2. DB with constraints over an extended vocabulary (i.e., conceptual
schema with exact views, or DBox): complete information about
some term appearing in the ontology
3. Sound DB with constraints over an extended vocabulary (aka
conceptual schema with sound views, or ABox): incomplete
information about some term appearing in the ontology
◮
Sound databases with constraints over an extended vocabulary are
crucial in data integration scenarios.
Ontologies and Databases.
E. Franconi.
(22/38)
61. Exact vs Sound views
Employee
Ontologies and Databases.
Works-for
1..⋆
Project
E. Franconi.
(23/38)
65. Querying a sound DB with Constraints
over an extended vocabulary (ABox)
Employee
Works-for
1..⋆
Project
Works-for ⊇ { John,Prj-A , Mary,Prj-A }
Project ⊇ { Prj-A, Prj-B }
Ontologies and Databases.
E. Franconi.
(24/38)
66. Querying a sound DB with Constraints
over an extended vocabulary (ABox)
Employee
Works-for
1..⋆
Project
Works-for ⊇ { John,Prj-A , Mary,Prj-A }
Project ⊇ { Prj-A, Prj-B }
Q(X) :- Works-for(Y,X)
Ontologies and Databases.
E. Franconi.
(24/38)
67. Querying a sound DB with Constraints
over an extended vocabulary (ABox)
Employee
Works-for
1..⋆
Project
Works-for ⊇ { John,Prj-A , Mary,Prj-A }
Project ⊇ { Prj-A, Prj-B }
Q(X) :- Works-for(Y,X)
=⇒ { Prj-A, Prj-B }
Ontologies and Databases.
E. Franconi.
(24/38)
68. Querying a sound DB with Constraints
over an extended vocabulary (ABox)
Employee
Works-for
1..⋆
Project
Works-for ⊇ { John,Prj-A , Mary,Prj-A }
Project ⊇ { Prj-A, Prj-B }
Q(X) :- Works-for(Y,X)
=⇒ { Prj-A, Prj-B }
=⇒
Q’(X) :- Project(X) ∪ Works-for(Y,X)
Ontologies and Databases.
E. Franconi.
(24/38)
69. DBox vs ABox
Employee
◮
Works-for
Project
Additional constraint as a standard view over the data:
Bad-Project = Project π2 Works-for
∀x. Bad-Project(x)↔ Project(x)∧¬∃y.Works-for(y,x)
Bad-Project = Project⊓¬∃Works-for−.⊤
Ontologies and Databases.
E. Franconi.
(25/38)
70. DBox vs ABox
Employee
◮
◮
◮
◮
◮
Works-for
Project
Additional constraint as a standard view over the data:
Bad-Project = Project π2 Works-for
∀x. Bad-Project(x)↔ Project(x)∧¬∃y.Works-for(y,x)
Bad-Project = Project⊓¬∃Works-for−.⊤
DBox:
Works-for = { John,Prj-A , Mary,Prj-A }
Project = { Prj-A, Prj-B }
Q(X) :- Bad-Project(X)
ABox:
Works-for ⊇ { John,Prj-A , Mary,Prj-A }
Project ⊇ { Prj-A, Prj-B }
Q(X) :- Bad-Project(X)
Ontologies and Databases.
E. Franconi.
(25/38)
71. DBox vs ABox
Employee
◮
◮
◮
◮
◮
Works-for
Project
Additional constraint as a standard view over the data:
Bad-Project = Project π2 Works-for
∀x. Bad-Project(x)↔ Project(x)∧¬∃y.Works-for(y,x)
Bad-Project = Project⊓¬∃Works-for−.⊤
DBox:
Works-for = { John,Prj-A , Mary,Prj-A }
Project = { Prj-A, Prj-B }
Q(X) :- Bad-Project(X)
=⇒ { Prj-B }
ABox:
Works-for ⊇ { John,Prj-A , Mary,Prj-A }
Project ⊇ { Prj-A, Prj-B }
Q(X) :- Bad-Project(X)
Ontologies and Databases.
E. Franconi.
(25/38)
72. DBox vs ABox
Employee
◮
◮
◮
◮
◮
Works-for
Project
Additional constraint as a standard view over the data:
Bad-Project = Project π2 Works-for
∀x. Bad-Project(x)↔ Project(x)∧¬∃y.Works-for(y,x)
Bad-Project = Project⊓¬∃Works-for−.⊤
DBox:
Works-for = { John,Prj-A , Mary,Prj-A }
Project = { Prj-A, Prj-B }
Q(X) :- Bad-Project(X)
=⇒ { Prj-B }
ABox:
Works-for ⊇ { John,Prj-A , Mary,Prj-A }
Project ⊇ { Prj-A, Prj-B }
Q(X) :- Bad-Project(X)
=⇒ { }
does not scale down to standard DB answer!
Ontologies and Databases.
E. Franconi.
(25/38)
76. Compositionality of Queries
Employee
Works-for
1..⋆
Project
◮
ABox:
Works-for ⊇ { John,Prj-A }
Project ⊇ { Prj-A, Prj-B }
◮
Query as a standard view over the data:
Q(X) :- Works-for(Y,X)
Q =
π Works-for
2
π
◮
Q = EVAL( 2 Works-for)
=⇒ { Prj-A, Prj-B }
◮
Q =
Ontologies and Databases.
π (EVAL(Works-for))
2
E. Franconi.
(26/38)
77. Compositionality of Queries
Employee
Works-for
1..⋆
Project
◮
ABox:
Works-for ⊇ { John,Prj-A }
Project ⊇ { Prj-A, Prj-B }
◮
Query as a standard view over the data:
Q(X) :- Works-for(Y,X)
Q =
π Works-for
2
π
◮
Q = EVAL( 2 Works-for)
=⇒ { Prj-A, Prj-B }
◮
Q = 2 (EVAL(Works-for))
=⇒ { Prj-A }
π
Queries are not compositional wrt certain answer semantics!
Ontologies and Databases.
E. Franconi.
(26/38)
78. Complexity of Query answering
has-border
Region
◮
1..⋆
has-colour
Colour
DBox:
Region = {Italy,France,. . .}; has-border = { Italy,France ,. . .};
Colour = { Red, Green, Blue }
Ontologies and Databases.
E. Franconi.
(27/38)
79. Complexity of Query answering
has-border
Region
◮
◮
1..⋆
has-colour
Colour
DBox:
Region = {Italy,France,. . .}; has-border = { Italy,France ,. . .};
Colour = { Red, Green, Blue }
Q :- has-colour(R1,C), has-colour(R2,C), has-border(R1,R2)
Is it unavoidable that there are two adjacent regions with the same colour?
Ontologies and Databases.
E. Franconi.
(27/38)
80. Complexity of Query answering
has-border
Region
◮
◮
1..⋆
has-colour
Colour
DBox:
Region = {Italy,France,. . .}; has-border = { Italy,France ,. . .};
Colour = { Red, Green, Blue }
Q :- has-colour(R1,C), has-colour(R2,C), has-border(R1,R2)
Is it unavoidable that there are two adjacent regions with the same colour?
◮ YES: in any legal database (i.e., an assignment of colours to regions)
there are at least two adjacent regions with the same colour.
Ontologies and Databases.
E. Franconi.
(27/38)
81. Complexity of Query answering
has-border
Region
◮
◮
1..⋆
has-colour
Colour
DBox:
Region = {Italy,France,. . .}; has-border = { Italy,France ,. . .};
Colour = { Red, Green, Blue }
Q :- has-colour(R1,C), has-colour(R2,C), has-border(R1,R2)
Is it unavoidable that there are two adjacent regions with the same colour?
◮ YES: in any legal database (i.e., an assignment of colours to regions)
there are at least two adjacent regions with the same colour.
◮ NO: there is at least a legal database (i.e., an assignment of colours to
regions) in which no two adjacent regions have the same colour.
Ontologies and Databases.
E. Franconi.
(27/38)
82. Complexity of Query answering
has-border
Region
◮
◮
1..⋆
has-colour
Colour
DBox:
Region = {Italy,France,. . .}; has-border = { Italy,France ,. . .};
Colour = { Red, Green, Blue }
Q :- has-colour(R1,C), has-colour(R2,C), has-border(R1,R2)
Is it unavoidable that there are two adjacent regions with the same colour?
◮ YES: in any legal database (i.e., an assignment of colours to regions)
there are at least two adjacent regions with the same colour.
◮ NO: there is at least a legal database (i.e., an assignment of colours to
regions) in which no two adjacent regions have the same colour.
◮ With ABox semantics the answer is always NO, since there is at least a
legal database (i.e., an assignment of colours to regions) with enough
distinct colours so that no two adjacent regions have the same colour.
Ontologies and Databases.
E. Franconi.
(27/38)
83. Complexity of Query answering
has-border
Region
◮
◮
1..⋆
has-colour
Colour
DBox:
Region = {Italy,France,. . .}; has-border = { Italy,France ,. . .};
Colour = { Red, Green, Blue }
Q :- has-colour(R1,C), has-colour(R2,C), has-border(R1,R2)
Is it unavoidable that there are two adjacent regions with the same colour?
◮ YES: in any legal database (i.e., an assignment of colours to regions)
there are at least two adjacent regions with the same colour.
◮ NO: there is at least a legal database (i.e., an assignment of colours to
regions) in which no two adjacent regions have the same colour.
◮ With ABox semantics the answer is always NO, since there is at least a
legal database (i.e., an assignment of colours to regions) with enough
distinct colours so that no two adjacent regions have the same colour.
Query answering with DBoxes is co-np-hard in data complexity (3-col),
and it is strictly harder than with ABoxes!
Ontologies and Databases.
E. Franconi.
(27/38)
84. View based Query Processing
◮
Mappings between the ontology terms and the information source
terms are not necessarily atomic.
◮
Mappings can be given in terms of a set of sound (or exact) views:
◮
GAV (global-as-view ): sound (or exact) views over the information
source vocabulary are associated to terms in the ontology
◮
◮
◮
◮
both the DB and the partial DB assumptions are special cases of GAV
an ER schema can be easily mapped to its corresponding relational
schema in some normal form via a GAV mapping
LAV (local-as-view ): a sound or exact view over the ontology
vocabulary is associated to each term in the information source;
GLAV: mix of the above.
◮
It is non-trivial, even in the pure GAV setting - which is wrongly
believed to be computable by simple view unfolding.
◮
It is mostly studied with sound views, due to the negative complexity
results with exact views discussed before.
Ontologies and Databases.
E. Franconi.
(28/38)
98. Summary
◮
Logic and Conceptual Modelling
◮
Queries with an Ontology
◮
Determinacy
Ontologies and Databases.
E. Franconi.
(32/38)
99. Determinacy (implicit definability)
A query Q over a DBox is implicitly definable under constraints if its
extension is fully determined by the extension of the DBox relations, and it
does not depend on the non-DBox relations appearing in the constraints.
Checking implicit definability under first-order logic constraints of a query
over a DBox can be reduced to classical entailment.
Ontologies and Databases.
E. Franconi.
(33/38)
100. Determinacy (implicit definability)
A query Q over a DBox is implicitly definable under constraints if its
extension is fully determined by the extension of the DBox relations, and it
does not depend on the non-DBox relations appearing in the constraints.
Checking implicit definability under first-order logic constraints of a query
over a DBox can be reduced to classical entailment.
Definition (Implicit definability)
Let DBi and DBj be any two legal databases of the constraints T which
agree on the extension of the DBox relations.
A query Q is implicitly definable from the DBox relations under the
constraints T iff the answer of Q over DBi is the same as the answer of
Q over DBj .
Ontologies and Databases.
E. Franconi.
(33/38)
101. Rewriting - or explicit definability
◮
If a query is implicitly definable, it is possible to find an equivalent
reformulation of the query using only relations in the DBox. This is
its explicit definition.
◮
It has been shown that under general first-order logic constraints,
whenever a query is implicitly definable then it is explicitly definable
in a constructive way as a first-order query.
Ontologies and Databases.
E. Franconi.
(34/38)
105. The query rewriting under constraints
process
1. Check whether the database is consistent with respect to the
constraints and, if so,
2. check whether the answer to the original query under first-order
constraints is solely determined by the extension of the DBox
relations and, if so,
3. find an equivalent (first-order) rewriting of the query in terms of the
DBox relation.
4. It is possible to pre-compute all the rewritings of all the determined
relations as SQL relational views, and to allow arbitrary SQL queries
on top of them: the whole system is deployed at run time as a
standard SQL relational database.
Ontologies and Databases.
E. Franconi.
(36/38)
106. Domain independence & range-restricted
rewritings
I cheated so far!
Ontologies and Databases.
E. Franconi.
(37/38)
107. Domain independence & range-restricted
rewritings
I cheated so far!
Unless the rewriting is a domain independent (e.g., a range-restricted)
first-order logic formula, it can not be expressed in relational algebra or
SQL!
Ontologies and Databases.
E. Franconi.
(37/38)
108. Domain independence & range-restricted
rewritings
I cheated so far!
Unless the rewriting is a domain independent (e.g., a range-restricted)
first-order logic formula, it can not be expressed in relational algebra or
SQL!
◮
We prove general conditions on the constraints and the query in order
to guarantee that the rewriting is domain independent
◮
All the typical database constraints (e.g., TGDs and EGDs) satisfy
those conditions
◮
All the ontology languages in the guarded fragment satisfy those
conditions
Ontologies and Databases.
E. Franconi.
(37/38)
110. Conclusions
Do you want to exploit ontology knowledge
(i.e., constraints or an ontology)
in your data intensive application?
Ontologies and Databases.
E. Franconi.
(38/38)
111. Conclusions
Do you want to exploit ontology knowledge
(i.e., constraints or an ontology)
in your data intensive application?
Pay attention!
TURGIA
Ontologies and Databases.
A
Made with L TEX2e
E. Franconi.
(38/38)