Browse Prior Art Database

A System and Method for Automatically Constructing Semantic Queries Based on Domain Path Extraction

IP.com Disclosure Number: IPCOM000196563D
Publication Date: 2010-Jun-07
Document File: 4 page(s) / 75K

Publishing Venue

The IP.com Prior Art Database

Abstract

This is a system and method for automatically constructing semantic queries based on domain path extraction. The system contains two main modules: the system construction module and the semantic query construction module. The formal one includes four sub-modules: AN-ON mapping building, super path extraction, semantic query expansion, and query fragment generation.

This text was extracted from a PDF file.
At least one non-text object (such as an image or picture) has been suppressed.
This is the abbreviated version, containing approximately 56% of the total text.

Page 1 of 4

A System and Method for Automatically Constructing Semantic Queries Based on Domain Path Extraction

Observations: 1) Query semantics is broken up into fragments of triple patterns ,

which leads to more complexity of query expression. 2)

expressivity, ontology is always complicated and hard to handle by non-professional end users. 3) Users are always familiar with their own application model in the host environment. Here the application model indicates the external model in the application host environment to use the semantic query services, a graph model in our context.

Previous work focuses on two categories: query helper and wizard, and query template. This solution is different from previous work (a help for manually inputting semantic queries).

The main contribution of this method is that end users can get rid of query grammar , complex query expression, and ontology. Users only need to focus on the application model in the host environment.

Fig. 1

Although with rich

1

[This page contains 1 picture or other non-text object]

Page 2 of 4

Fig. 2

Fig. 1 and Fig.2 shows the difference before and after the use of the technology .

Concepts and definitions.

Application model

                ): external model in the application host environment to use the semantic query services.

Application node

(

AM

): node in the application model.

Ontology model (OM ): internal model to provide the semantic services, can be the ontology model or other supported models.

Ontology node (ON ): node in the ontology model.

Ontology edge (OE ): property edge in the ontology model.

Super node (SN ): a set of ontology nodes that maps to an application node .

Steps for system construction:
1. Build the application node-to-ontology node mapping
2. Extract all the super paths
3. Expand to semantic path
4. Generate query fragments

AN-ON mapping building:
1. Build the mapping between application nodes and ontology nodes (

AN-ON

(

AN

mapping F )

F(

AN, {ON

}

)

Super node (SN )

SN = {{ON

} | {ON

}

in F

}

A set of ontology nodes that maps to an application node

If more than one super nodes correspond to an application node

Identify the key properties that can classify their super nodes Example

AM

= {

AN

('Patient'),

AN

('Doctor'),

AN

('PatientEncounter'), …}

OM = {ON ('Entity'), ON ('Role'), ON ('Act'), ON ('Participation'), …}

F = {(

AN

('Patient'), {ON ('Entity'), ON ('Role')}), (

AN

('PatientEncounter'), {ON

2

[This page contains 1 picture or other non-text object]

Page 3 of 4

('Act')}), …}

SN set = {{ON ('Entity'), ON ('Role')}, {ON ('Act')}}

Super path extraction: Super path (SP ) The path between two super nodes, and there's no other super nodes on the path


Construct the pair-

wise path relationship between super nodes

Bottom-up BFS search

For each super node
- Find the margin ontology node first
- Perform BFS search based on the margin nodes
- For each forward move, check whether the current iteration visited another super node. If...