Geometrically, Various Query Languages Has Research Paper

Length: 16 pages Sources: 16 Subject: Education - Computers Type: Research Paper Paper: #8866705 Related Topics: Relational Database, Artificial Intelligence, Integrity, Search Engines
Excerpt from Research Paper :

Queries are posed in terms of a certain query language over the alphabet of the global ontology and are intended to extract a set of tuples of elements of the semantic domain. In accordance with what is typical in databases, each query is required to have an associated arity and that it extract only tuples of that arity. Given a source database for O, the tuples of interest are those that are guaranteed to be in the answer of the query for every model for O. with respect to the source database. In other words, certain answers are of interest. One of the most common ways to express knowledge on a domain of interest is to use class-based formalisms, in which knowledge is represented in terms of objects grouped into classes and relationships between classes. Examples are entity-relationship diagrams in databases, UML class diagrams in software engineering, and ontology languages for the semantic web such as OWL-DL. All such formalisms can be captured in a fragment of first-order logic in which one can express inclusions and equivalences between classes and possibly pose additional constraints on the relations between classes. Such fragments correspond to a class of logics called description logics [17]. The Web Ontology Language (OWL) is a World Wide Web Consortium Standard and a leading approach to semantic Web ontologies. OWL-Description Logics (OWL-DL) uses DL as its fundamental knowledge representation mechanism. Ontology descriptions are presented formally through description logics for theoretical soundness; and in machine readable format using an OWLDL to provide practicality for our model. Software reasoners, such as Racer, support concept consistency checking, T-Box reasoning, and A-Box reasoning on models developed using SHIQ description logics translated into OWL-DL. These provide the basis for development of a knowledge base of machine interpretable knowledge representation, in OWL-DL format, that can be used for developing computational ontologies for knowledge integration in inter-organizational eBusiness processes [18].

Description Logic ALCQI

ALCQI is a notable example of an expressive DL that features constructs that are typical of conceptual modeling formalisms and that in fact allow ALCQI to capture the most important features of such formalisms. The ALCQI DL provides concept constructs for complement, intersection, union, existential restriction, universal quantification, and number restrictions. As for roles, it provides the construct for inverse roles [19].

Contextual Query Language (CQL)

This is a formal language for representing queries to information retrieval systems such as web indexes, bibliographic catalogs and museum collection information. The design objective is that queries be human readable and writable, and that the language be intuitive while maintaining the expressiveness of more complex languages.

Traditionally, query languages have fallen into two camps: Powerful, expressive languages, not easily readable nor writable by non-experts (e.g. SQL, PQF, and XQuery); or simple and intuitive languages not powerful enough to express complex concepts (e.g. CCL and Google). CQL tries to combine simplicity and intuitiveness of expression for simple, every day queries, with the richness of more expressive languages to accommodate complex concepts when necessary [20]. CQL is so-named ("Contextual Query Language") because it is founded on the concept of searching by semantics or context, rather than by syntax. The same search may be performed in a different way on very different underlying data structures in different servers, but the important thing is that both servers understand the intent behind the query. In order for multiple communities to define their own semantics, CQL uses Context Sets in order to ensure cross-domain interoperability [21].

XPath

This is a node addressing language that is used with XML documents [22]. This query language allows users to query for various index services and results are gathered and returned to the user based on the following steps:

1. The client sends the search request to its nearest DDS with the XPath query.

2. The DDS contacts with a FADA node to search all the Index Services (ISs) of the system.

3. The ISs search request is broadcasted to all the Federated Advanced Directory Architecture (FADA) nodes using the FADA internal protocol.

4. The FADA node returns to the DDS the list of ISs.

5. The DDS contacts with each IS making the XPath Query.

6. Finally, the results are gathered to the client [23].

Z39.50 search standards for cross-search capabilities for library catalogs

The Open Archives Initiative (OAI) has become another approach to integrated online...

...

OAI is a protocol for the automated harvesting of descriptive and location metadata about content from diverse online sources. Metadata is stored in a common index database where it can be searched. Searchers can then be automatically routed to the source content for any retrieved search results. This is basically the same technique employed by Web search engines, which use automated software to collect information about many Web sites, storing it in a common index. The metadata harvesting approach differs considerably from the approach taken by Z39.50.

Z39.50 searches many silos by passing a query to each separate database in a common query language. Responses are then received back from each database, in turn. The broadcast search method is similar to the approach used by many metasearch tools such as WebFeat and MuseGlobal. While Z39.50 searches rely on a common protocol and query language, metasearch tools may have to translate each separate query to suit the individual data source (information silo) being searched. While broadcast searching has had some success, as the number of different online resources grows, metadata harvesting seems to be the more promising approach to search integration [24].

ActivePrime

ActivePrime leverages AI-related techniques in three broad categories: lightweight ontologies, search-space reduction (SSR), and query optimization. In summary, lightweight ontologies are deployed as modules and classes in the Python programming language, enabling rapid, iterative development of ontologies using a popular scripting language. The ontologies also benefit from the large repository of built-in Python operators. Sophisticated operations on ontologies can be performed with just a few lines of code. SSR techniques are utilized when performing inexact matching on larger volumes of data, when record counts grow into the many thousands and millions. Query optimization techniques allow for real-time detection of duplicate records when matching one record to a large remote data base [25].

Query Optimization

Query optimization is used to allow the most efficient matching of a queried record to a remote database. The query optimization process is fairly straightforward, but the actual process that is used by the query language and the search strategies that are employed by human users will inevitably differ. Generally speaking, though, in order to optimize a query, a user's query is analyzed using the context of the fields (such as company name or state name) together with relevant domain knowledge to expand the query to gather additional information or information about expanded areas of interest. In this regard, Bidlack and Wellman give the example, "For instance, the state Massachusetts may have MA and Mass as synonyms and the query is expanded appropriately" [27]. Although the query optimization process appears intuitive and easy from a human's perspective (perhaps because the process resembles how the human brain processes information in ways that allow for its later access and retrieval), the algorithms that guide the query optimization process are truly sophisticated and robust in specific areas depending on their application. According to Bidlack and Wellman, "Besides expansion through domain knowledge, query expansion occurs using phonetic rules as well as heuristics around transposition and removal of characters. The query optimizer effectively constructs a query that has a high probability of finding potential inexact matches while only retrieving a very small subset of the remote database. The subset of records is then analyzed using SSR techniques to compute actual inexact matches" [28]. The results that are returned to the user are therefore based on several steps that winnow data to provide the most meaningful results to the query, with the respective strengths and effectiveness of each query optimization approach largely depending on the platform or information resource that is involved.

Semantic query optimization (SQO) is a real-time computing system retains local control while scaling to numerous other machines but does not use centralized query optimization and scheduling techniques [29]. Rather than using centralized query optimization and scheduling techniques, the semantic query optimization approach is "An enterprise-class data federation system supports dynamic load balancing across system resources. As loads on individual machines and networks change, the system adapts and adjusts query execution. As a result, the system can support many machines with high performance and throughput. Such a system can be viewed as the complement to a transactional approach. From this view, this system is capable of obtaining the desired data in real-time or as near to real-time as possible" [30].

Semantic query optimization uses so-called "integrity constraints" to restrict a search to make it more efficient. According to Minker, integrity constraints that are specifically designed for SQO have been developed for application in deductive databases (DDBs) as well as relational databases; in…

Sources Used in Documents:

References

[1] C. Bidlock & M.P. Wellman. 2010, Spring. "Exceptional Data Quality Using Intelligent Matching and Retrieval." AI Magazine 31(1), pp. 65-66.

[2] P. Webster. 2004, November/December. "Breaking Down: Information Silos Integrating Online Information." Online 28(6), pp. 30-32.

[3] D. Calvanese & G. de Giacomo. 2005, Spring. "Data Integration: a Logic-based Perspective." AI Magazine 26(1), pp. 59-61.

[4] J. Feather & P. Sturgers. 2003. International Encyclopedia of Information and Library Science. London: Routledge, p. 126.


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