Natural Language Interface For Basic Query Specification Computer Science Essay

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The main purpose of this survey is to create a natural language interface for basic query specification. This natural language processing (NLP)-based interface allows users to formulate queries as sentences in English using a part of speech (POS) encoding algorithm. Then, the system groups specified query as an object-appearance, spatial and trajectory object queries based on similarity using POS tagging information. Sends the query constructed in the form of Prolog facts for the engine, which interacts with the knowledge base and to respond to users ' queries to relational databases and object query processing.

Video databases have become popular in various areas for recent advances in technology. Archive video systems need user-friendly interfaces to retrieve video frames. In this document, describes a UI based on natural language processing (NLP) of a video database system. The video database is based on a template based on the contents of video data space-time. The data model is focused on semantic content that includes objects, activities and spatial properties of objects. With this data model can be a query space-time relations between objects video and also the trajectories of moving objects. In this video database system, a natural language interface allows querying flexible. Queries, which are shown as English phrases, are analyzed using the link Parser. The query Semantic representations are extracted from their syntactic structures using information extraction techniques. The extracted Semantic representations are used to call the related parties of the underlying database video system to return query results. The query processor integrates intermediate query results returned by these two components. The system sends your final results to clients. This process is harder to have just a list of words identified by their parts of speech, because some words can represent more than one part of speech at different times. In many languages, an enormous percentage of forms of speech are ambiguous.

Contents

Executive summary: 1

Video databases have become popular in various areas for recent advances in technology. Archive video systems need user-friendly interfaces to retrieve video frames. In this document, describes a UI based on natural language processing (NLP) of a video database system. The video database is based on a template based on the contents of video data space-time. The data model is focused on semantic content that includes objects, activities and spatial properties of objects. With this data model can be a query space-time relations between objects video and also the trajectories of moving objects. In this video database system, a natural language interface allows querying flexible. Queries, which are shown as English phrases, are analyzed using the link Parser. The query Semantic representations are extracted from their syntactic structures using information extraction techniques. The extracted Semantic representations are used to call the related parties of the underlying database video system to return query results. The query processor integrates intermediate query results returned by these two components. The system sends your final results to clients. This process is harder to have just a list of words identified by their parts of speech, because some words can represent more than one part of speech at different times. In many languages, an enormous percentage of forms of speech are ambiguous. 1

Contents 2

Introduction: 5

Critical Analysis of the Literature Survey: 7

Natural language processing (NLP)-based interface allows users to formulate queries as sentences in English using a part of speech (POS) encoding algorithm. Then, the system groups specified query as an object-appearance, spatial and trajectory object queries based on similarity using POS tagging information. Sends the query constructed in the form of Prolog facts for the engine, which interacts with the knowledge base and to respond to users ' queries to relational databases and object query processing. The query processor integrates intermediate query results returned by these two components. The system sends your final results to clients. So, we developed an interface of queries based on natural language that is convenient and provides greater flexibility when specifying query. The POS pattern-matching approach that we use in identifying query enables users to specify query without complying with strict rules. Part of speech tagging is the process of marking of words in a text with their corresponding parts of speech. This process is harder to have just a list of words identified by their parts of speech, because some words can represent more than one part of speech at different times. In many languages, an enormous percentage of forms of speech are ambiguous. There are eight English parts of speech: nouns, verbs, adverbs, prepositions, pronouns, adverbs, conjunctions, and interjections. A video system, database, that BilVideo provides integrated support for queries spatiotemporal, semantic and low level of functionality. As a further development of this system, we present a natural language-based interface to query specification. The query processor integrates intermediate query results returned by these two components. Systems at the end of the deliverables to Web clients. What motivates our work is the need for a natural language-based interface convenient and flexible way to integrate the interface for text-based query and the query interface in visual BilVideo system, because it is very easy for novice users spatial query specification using text or Visual interfaces. (For examples of how others have attempted to handle these issues, see the sidebar "Job related", next page). So, we developed an interface of queries based on natural language that is convenient and provides greater flexibility when specifying query. The POS pattern-matching approach that we use in identifying query enables users to specify query without complying with strict rules. This approach also allows us to easily adjust our query interface as we add new types of queries to BilVideo system. 7

BilVideo System: 8

The database management system of video BilVideo provides integrated support for queries space-time and semantic for video. A knowledge base-composed a basis in fact and a complete set of rules implemented in the prologue--handles query time and space. These queries contain any combination of conditions for the direction, topology, 3D object relationships, appearance, trajectory and projection trajectories object based on the similarity. The rules in the knowledge base to reduce substantially the number of facts that represent relationships space and time that the system needs to store. A database of functionality which is stored in an object-relational database management system which handles semantic query .BilVideo has the following parts: 8

Natural language interface that can handle these queries and other forms of query given in English. There was a considerable amount of work in a query video frames in natural languages. Use parser to convert media descriptions (or annotations) and build semantic ontology trees from the parsed query. However, these usually are application-specific and domain-dependent (for example querying only recordings of road cameras on site or querying only the parts of the transmission in Informedia News). Not every system using natural language can capture high-level semantics. The Informedia video system that is using the natural-language interface keyword matching, cannot respond to queries detailed nor handle structures with attributes. This white paper, we propose a general-purpose video database querying of system by adding a natural language interface for a data model video another contribution system query facility is the use of techniques of extracting information in order to find the semantic representation of the user's query. SOCIS, the system of crime scene photographs are annotated with text and keywords are extracted to index the transaction. However photos, only spatial relationships in images are extracted in that system. In our system, on the other hand, many other types of queries can be extracted from phrases and their Semantic representations are mapped to the data model underlying video. 10

The important issue to match a particular query with the data underlying video in systems using natural language interfaces. When the natural language queries are parsed, the first objective is to extract entities that occur in the query and match them with entities in the database. However, sometimes you can't get an exact match for your query from the database. For example, the user can query a machine where there is an entity but rather Mercedes cars and Fiat exist as a principal video. To not answer with an empty result set, a query-based ontology is used after the analysis phase. The similarity between entities in the database and analyzed by the query is evaluated using a hierarchy is-a. The root of the tree is semantically more generalized leaves. The highest value of similarity of the entity is chosen to be in the result. Therefore, a natural language interface that uses a query based returns the results of closed-in addition to exact matches consignment. Although there are many different algorithms that semantic similarity of ontology, none of the methods gives the best result. In our system, we preferred a combined method of a Board of Wu and Palmer method with a corpus of count based method, because it gave the best results in our tests. excellent ontology based query was used earlier in some other video systems, but these systems construct its ontology that must be changed whenever the domain change. excellent Semantic representations are constructed from the output of the analysis module, by extracting information system. Section 4 describes the module extraction and analysis technique used in query processing. Section 5 presents the details of the basis of ontology query that supplies the results of closing-game. We also explain the expansion of Semantic representations that are extracted from natural language processing module. We give the assessment results for basic research of Ontology. Finally, it presents the conclusions and future work. Related work on natural language query processing 11

Natural language techniques over video databases 13

Due to a rich set of semantic structures and space-time properties in models of video data, is more complex to support querying the database of video. Natural language query systems should be able to handle more complex query structures. A Web-based Visual query interface, which specifies the query using visual sketches and displays the results; and textual query language, which specifies the query using a structured query language (SQL) extended. 13

Web Site Implementation: 13

Currently BilVideo uses a Web-based interface visual query for specific query. excellent interface uses Windows specification for space and trajectory queries. The specifications and the formation of these queries vary greatly; therefore, the interface creates specific Windows handles each type. Users can query time of video content by combining these two types of queries with predicates primitive time (before, during, and so on). Users can also make queries from Visual sketches. The system automatically calculates the most salient relationships between objects by specifying conditions for queries. 16

System Development Life Cycle: 16

Requirement Analysis in this Project 21

Problem Recognition: 21

Evaluation and Synthesis: 21

b. Requirement Specification 22

Query Processing 22

The system uses the recombination of associations of invoking syntactically similar strings to form a carriers. The system uses lists of equivalence for the subparts of organization of the string generate lists of equivalence for our strings longer than the province in an order controlled by potential these/parse to be evaluated. The power of recombination of entries from: vector elements in building longer strings provides a means of representing the complexity collocational. Grammaticality score is based on the number and the similarity of the vector elements. 24

Conclusion: 25

References: 26

Introduction:

This natural language processing (NLP)-based interface allows users to formulate queries as sentences in English using a part of speech (POS) encoding algorithm. Then, the system groups specified query as an object-appearance, spatial and trajectory object queries based on similarity using POS tagging information. Processing of natural language query interface allows you to formulate as sentences in English using part of Speech tagging algorithm. The system then groups the facts and the properties of the object from the image. The system then sends the query constructed from a query processing engine that interacts with a relational database is the knowledge and the object in response to user queries. The query processor integrates all related information and produces the result required.

A video system, database, that BilVideo provides integrated support for queries spatiotemporal, semantic and low level of functionality. As a further development of this system, we present a natural language-based interface for specifying query. This natural language processing (NLP)-based interface allows users to formulate queries as sentences in English using a part of speech (POS) encoding algorithm. Then, the system groups specified query as an object-appearance, spatial and trajectory object queries based on similarity using POS tagging information. Sends the query constructed in the form of Prolog facts for the engine, which interacts with the knowledge base and to respond to users ' queries that contain a combination of spatiotemporal, semantic, color, shape and texture video relational database queries and query processing object. Each project plan has some steps to a successful conclusion. Ignored for success of every project need to undergo the phases 5 min. These steps are followed by a sequence that is known to be development life-cycle of the system.

Critical Analysis of the Literature Survey:

Natural language processing (NLP)-based interface allows users to formulate queries as sentences in English using a part of speech (POS) encoding algorithm. Then, the system groups specified query as an object-appearance, spatial and trajectory object queries based on similarity using POS tagging information. Sends the query constructed in the form of Prolog facts for the engine, which interacts with the knowledge base and to respond to users ' queries to relational databases and object query processing. The query processor integrates intermediate query results returned by these two components. The system sends your final results to clients. So, we developed an interface of queries based on natural language that is convenient and provides greater flexibility when specifying query. The POS pattern-matching approach that we use in identifying query enables users to specify query without complying with strict rules. Part of speech tagging is the process of marking of words in a text with their corresponding parts of speech. This process is harder to have just a list of words identified by their parts of speech, because some words can represent more than one part of speech at different times. In many languages, an enormous percentage of forms of speech are ambiguous. There are eight English parts of speech: nouns, verbs, adverbs, prepositions, pronouns, adverbs, conjunctions, and interjections. A video system, database, that BilVideo provides integrated support for queries spatiotemporal, semantic and low level of functionality. As a further development of this system, we present a natural language-based interface to query specification. The query processor integrates intermediate query results returned by these two components. Systems at the end of the deliverables to Web clients. What motivates our work is the need for a natural language-based interface convenient and flexible way to integrate the interface for text-based query and the query interface in visual BilVideo system, because it is very easy for novice users spatial query specification using text or Visual interfaces. (For examples of how others have attempted to handle these issues, see the sidebar "Job related", next page). So, we developed an interface of queries based on natural language that is convenient and provides greater flexibility when specifying query. The POS pattern-matching approach that we use in identifying query enables users to specify query without complying with strict rules. This approach also allows us to easily adjust our query interface as we add new types of queries to BilVideo system.

BilVideo System:

The database management system of video BilVideo provides integrated support for queries space-time and semantic for video. A knowledge base-composed a basis in fact and a complete set of rules implemented in the prologue--handles query time and space. These queries contain any combination of conditions for the direction, topology, 3D object relationships, appearance, trajectory and projection trajectories object based on the similarity. The rules in the knowledge base to reduce substantially the number of facts that represent relationships space and time that the system needs to store. A database of functionality which is stored in an object-relational database management system which handles semantic query .BilVideo has the following parts:

An object Extractor, which extracts the key from keyframes. objects To video made Extractor, which extracts spatiotemporal relationships between objects video and store them in the knowledge base as facts; Annotator video, which extracts the semantic data from video clips and stores them in the database of querying data video for its semantic content.

It is possible to compute and query spatial relationships between two rectangular areas, hence the objects covered by those rectangles. The calculated and interrogate the spatial relationships between two rectangular areas, so the objects covered by such rectangles. You can also manage spatial relationships like left, right, top, bottom, top left, top right, bottom, left, bottom right, as directional relations and overlaps, equal, inside, contain, touch and disjoint as topological relations. The model also supports querying the trajectory of an object given initial regions and finish. The template can query time and space on the videos and give also the inclusion of Fuzziness in spatial queries and space-time. Our model of video data previously had only a graphical query interface. Later, the system is integrated with a natural language interface where the user can express queries in English. In this paper, we present this interface natural language queries to the database system video. The ability to query the system in a natural language instead of an artificial language can be exemplified by the following types of queries.

• Find the frames where the prime minister meets the minister of foreign affairs. (A journalist may be posing this kind of query frequently)

• Show all intervals where the goals are scored. (This query may be used in a sports event archive)

• Show all cars leaving the parking lot. (A security camera recording can be queried in this fashion)

Natural language interface that can handle these queries and other forms of query given in English. There was a considerable amount of work in a query video frames in natural languages. Use parser to convert media descriptions (or annotations) and build semantic ontology trees from the parsed query. However, these usually are application-specific and domain-dependent (for example querying only recordings of road cameras on site or querying only the parts of the transmission in Informedia News). Not every system using natural language can capture high-level semantics. The Informedia video system that is using the natural-language interface keyword matching, cannot respond to queries detailed nor handle structures with attributes. This white paper, we propose a general-purpose video database querying of system by adding a natural language interface for a data model video another contribution system query facility is the use of techniques of extracting information in order to find the semantic representation of the user's query. SOCIS, the system of crime scene photographs are annotated with text and keywords are extracted to index the transaction. However photos, only spatial relationships in images are extracted in that system. In our system, on the other hand, many other types of queries can be extracted from phrases and their Semantic representations are mapped to the data model underlying video.

The important issue to match a particular query with the data underlying video in systems using natural language interfaces. When the natural language queries are parsed, the first objective is to extract entities that occur in the query and match them with entities in the database. However, sometimes you can't get an exact match for your query from the database. For example, the user can query a machine where there is an entity but rather Mercedes cars and Fiat exist as a principal video. To not answer with an empty result set, a query-based ontology is used after the analysis phase. The similarity between entities in the database and analyzed by the query is evaluated using a hierarchy is-a. The root of the tree is semantically more generalized leaves. The highest value of similarity of the entity is chosen to be in the result. Therefore, a natural language interface that uses a query based returns the results of closed-in addition to exact matches consignment. Although there are many different algorithms that semantic similarity of ontology, none of the methods gives the best result. In our system, we preferred a combined method of a Board of Wu and Palmer method with a corpus of count based method, because it gave the best results in our tests. excellent ontology based query was used earlier in some other video systems, but these systems construct its ontology that must be changed whenever the domain change. excellent Semantic representations are constructed from the output of the analysis module, by extracting information system. Section 4 describes the module extraction and analysis technique used in query processing. Section 5 presents the details of the basis of ontology query that supplies the results of closing-game. We also explain the expansion of Semantic representations that are extracted from natural language processing module. We give the assessment results for basic research of Ontology. Finally, it presents the conclusions and future work. Related work on natural language query processing

A natural language interface should query the video content, in order to provide a flexible system, where the user can use his own penis for querying. The user does not have to learn an artificial query language, which is a great advantage of using a natural language in a query.

The first studies of natural language processing queries depend on simple matching techniques. These are simple methods that do not require parsing algorithm. SAVVY is an example of this approach. In this system, some models are written for different query types and these models are run after the query is entered. For example, consider a table consisting of names of countries and their capitals. Suppose that a pattern is written as "Retrieves the capital of the country, if the query contains the word" capital "before the name of a country". Then the query "what is the capital of Italy?", will respond "Rome" as a result. However, since the results of this technique was not satisfactory, more flexible and complex techniques have been examined.

The method used in Lunar system supports an approach based on syntax where an analysis algorithm is used to generate a parse tree second user query. This method is used especially in database systems specific to the application. A database query language must be provided by the system to enable the mapping from parse tree for the database query. Moreover, it is difficult to decide the mapping rules from the parse tree for the query language (SQL) that uses the database. The system uses semantic grammars where the techniques of syntactic and semantic transformation processing techniques are used together. The disadvantage of this method is the semantic approach needs a specific domain of knowledge, which is very difficult to adapt the system to another domain. In fact, a new grammar must be developed when the system is configured for a different domain.

Some languages of intermediate representation can be used to convert natural language instructions into a query language known formal. MASQUE/SQL is an example of this approach. Is a front-end language for relational databases that can be reached via SQL. User defines the kinds of domain which database references using a hierarchy is one in a domain-integrated editor. In addition, words that constitutes their logical queries with predicates are also declared by the user. Queries are processed in the first place in a language like Prolog IDUWKHUHKDYHEHHQQRUHVXOWV, then in SQL. The advantage of this technique is that the build system for the query logic is independent from the database and therefore is very flexible in domain substitutions.

Natural language techniques over video databases

Due to a rich set of semantic structures and space-time properties in models of video data, is more complex to support querying the database of video. Natural language query systems should be able to handle more complex query structures. A Web-based Visual query interface, which specifies the query using visual sketches and displays the results; and textual query language, which specifies the query using a structured query language (SQL) extended.

Web Site Implementation:

The website where you can access our text retrieval system is http://web.njit.edu/~kks2/CIS634/project/ and this is how it appears to the user:

It gives users the chance to put a natural language or Boolean query. You can also select the number of results you want these etc. 10 or 20. Lets say make a natural language search of "old Indian" as done in the next screen:

You will see the number of results returned. Will tell you the number of query terms that are in the document as "Term: 1" indicates that only one out of two term in your query is present in the document and will also show the weight of the term with regard to that document. You can also do Boolean search as on the next slide. When the system takes this query removes all stop words, with the exception of and, or and not, removes the wild character, not arising and then search the documents in the collection of text.

Subsequently, a user can also do advanced search where you can look up words in the title only, or only in the body or in the title or body. Here you get an option to use the thesaurus. If you use the thesaurus is getting more documents listed since it allows you to search for synonyms of words in your query, and also displays the documents that contain the synonym. Displays the next screen.

Currently BilVideo uses a Web-based interface visual query for specific query. excellent interface uses Windows specification for space and trajectory queries. The specifications and the formation of these queries vary greatly; therefore, the interface creates specific Windows handles each type. Users can query time of video content by combining these two types of queries with predicates primitive time (before, during, and so on). Users can also make queries from Visual sketches. The system automatically calculates the most salient relationships between objects by specifying conditions for queries.

System Development Life Cycle:

System Analysis is first stage according to System Development Life Cycle model. This System Analysis is a process that starts with the analyst. Analysis is a detailed study of the various operations performed by a system and their relationships within and outside of the system. One aspect of analysis is defining the boundaries of the system and determining whether or not a candidate system should consider other related systems. During analysis, data are collected on the available files, decision points, and transactions handled by the present system.Logical system models and tools that are used in analysis. Training, experience, and common sense are required for collection of the information needed to do the analysis.

The model life cycle stages represents the software life cycle as a series of subsequent activities. Each phase requires well-defined information input, using well-defined processes and translates into well-defined products. The gradual model consists of the following stages. This model is sometimes called the waterfall graph, the implication is that products in series from one level to another in a smooth transition. The Analysis Stage consists of Planning and Requirements definition Major include understanding the customer’s problem, performing

A feasibility study, the development of a recommended solution strategy, determination of acceptance criteria and schedule development process. Planning products are a system definition and a project plan.

The Design follows the analysis Software. Its software components, specifying dependencies between components specifying some structure, keeping a record of design decisions and providing the implementation phase of the project concerns the design. Design consists of the detailed planning and architectural design.

The implementation phase of software development involves translation design specifications in source code and debugging unit tests, documentation and source code. To improve software quality, methods are structured control constructs, built in and user-defined data types, type-safe control, flexible scope rules exception management mechanism, competition, constructs and separates fill forms.

System testing phase involves two types of tests, integration tests and acceptance tests. Developing a strategy for integration of the components of a software system in a complex operation requires careful planning, so that the modules are available for integration when necessary. Acceptance testing involves planning and execution of the various tests to prove the implemented system satisfies the requirements document.

The maintenance phase comes after customer acceptance and release system for production work. Maintenance tasks include feature enhancements, adaptation of the software to new computing environments, and fix software bugs.

This project follows the Phased Life Cycle Model or the Water Fall model to a large extent.

The analysis phase consisted of listening to the needs and the needs of the Department concerned to obtain the required format of the system as you want from them, taking the required data to be stored for future use, etc., during design of the structure of the system was designed and all screens requests were formatted. So this has been demonstrated that the approval of the official and the system was constructed. Implementation phase was also made to Honeypot IT Consulting Pvt. Ltd. as they provided a computer with all the software you need and with required configuration. Coding, and debugging was done even after this phase is enhanced, as did as requested by your guide. The test was done to control any errors, bugs or undesirable behavior in the system. Have undergone separately individual modules, as well as the entire system. Requirements:

The software requirements specification is manufactured at the culmination of activity analysis. The function and performance are assigned to the software as part of the system engineering are refined by establishing a more complete description of information, a detailed description of the behaviour and functional and indication of performance requirements and design constraints, appropriate validation criteria and other data relevant to the requirements. The requirement phase basically consists of three activities:

Requirement Analysis

Requirement Specification

Requirement Analysis:

Requirement analysis, is an activity of software engineering that bridges the gap between allocation level system software and software designer. Provides the system engineer to specify the function of the software and performance indicate software interface with the other elements of the system and establish constraints that software must meet.

The basic objective of this phase is to get a clear picture of the needs and demands of the end user and the organization. Analysis involves interaction between clients and analysis. Research analysts Usually a problem by asking questions and reading existing documents. Analysts discover the real needs of the user, even if they don't know their clearly. During analysis, it is essential that a set of consistent and complete specifications emerges for the system. Here it is vital to resolve the contradictions that may emerge from the information received from various parties. This is essential to ensure the consistency of the final specifications.

It may be divided into 4 areas of effort.

Problem recognition

Evaluation and synthesis

Specification

Review

Each Requirement analysis method has a unique point of view. However all analysis methods are related by a set of operational principles. They are

The information domain of the problem must be represented and understood.

The functions that the software is to perform must be defined.

The behavior of the software as a consequence of external events must be defined.

The models that depict information function and behavior must be partitioned in a hierarchical or layered fashion.

The analysis process must move from essential information to implementation detail.

Requirement Analysis in this Project

The main aim in this stage is to assess what kind of a system would be suitable for a problem and how to build it. The requirements of this system can be defined by going through the existing system and its problems. They discussing (speak) about the new system to be built and their experience from it.

The main objective of this phase is to determine what type of system would be suitable for an issue and how to build it. The requirements for this system can be defined through the existing system and its problems. Discussing (speak) on new system to be built and their expectations from it. The steps involved are ctations from it. The steps involved would be

Problem Recognition:

The main problem is that the more time is taken to retrieve user information requested. This should be deleted. A comprehensive solution must be developed which will facilitate to enter the query as a natural language and keep data more quickly and efficiently.

Evaluation and Synthesis:

The system must be designed only after a full assessment of an existing one, on which we can see that much depends on the means of communication. In the proposed system information about customers and projects, query and process entries get information is effective and convenient. So that this is such that there is no loss of time.

b. Requirement Specification

Specification:

The user specifications, project management division here had to be taken. This Division has provided the required format for the query. The appearance of forms and their field names, different screens that wanted, the stages of this database, etc., were all given. The system has been built according to all specifications.

Specification Principles:

Software requirements specification plays an important role in creating quality software solutions. Specification is basically a process of representation. Requirements are represented so that ultimately leads to successful software implementation.

Requirements may be specified in a variety of ways. However there are some guidelines worth following: -

Representation format and content should be relevant to the problem

Information contained within the specification should be nested

Diagrams and other notational forms should be restricted in number and consistent in use.

Representations should be revisable.

Query Processing

Instead of using the limited graphical user interface for queries, a natural language interface has decided to use for flexibility. The idea is to map query phrase English in their Semantic representations using a parser and a form of extracting information. The query Semantic representations are inserted into the underlying video data model to process the query and display the results. The main structure of the system is given in Figure 1. In the rest of this section, the system of querying using natural language is explained in detail.

A system of computer-based analysis using vectors (lists) to represent the elements of natural language, providing a robust, distributed way to grammaticality score of an input string using as material of a large corpus of texts in natural language.

The system uses the recombination of associations of invoking syntactically similar strings to form a carriers. The system uses lists of equivalence for the subparts of organization of the string generate lists of equivalence for our strings longer than the province in an order controlled by potential these/parse to be evaluated. The power of recombination of entries from: vector elements in building longer strings provides a means of representing the complexity collocational. Grammaticality score is based on the number and the similarity of the vector elements.

Conclusion:

The system described in this document uses a natural language interface for creating queries to retrieve information from a database that supports a query time and space based on the content. In order to implement the interface of natural language, a light analysis algorithm is used to analyze queries and to find the Semantic representations of query uses an algorithm for extracting information. The main part of the extraction step is the detection of objects, events, activities and space-time relations. The semantic representation is built as the result of analysis the sentence with the link in a knowledge base. The semantic representation is used to map query functions form video database query processing. A search of conceptual Ontology is implemented as part of the natural language interface. Using the ontological structure, Word Net, the system retrieves the objects or activities for the words of closest date in the query. A method based on the edge is combined with techniques based on corpus in order to obtain greater precision by the system. The Semantic representations enriched with the ontology are sent to the database system of video to call the appropriate query.

Video has been discontinued in interactive E-Learning. How to make them searchable to meet your individual needs is an important and complex task. The system offers users a feeling of being in communication with a mentor in real time. We have developed a or new two-step approach, leading the video content-based indexing and retrieval to identify appropriate video clips to address users ' interests. The approach integrates the natural language processing, named entity extraction, text and frame-based indexing and search techniques of video information.

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