Presentation is loading. Please wait.

Presentation is loading. Please wait.

CoopIS2001 Trento, Italy The Use of Machine-Generated Ontologies in Dynamic Information Seeking Giovanni Modica Avigdor Gal Hasan M. Jamil.

Similar presentations


Presentation on theme: "CoopIS2001 Trento, Italy The Use of Machine-Generated Ontologies in Dynamic Information Seeking Giovanni Modica Avigdor Gal Hasan M. Jamil."— Presentation transcript:

1 CoopIS2001 Trento, Italy The Use of Machine-Generated Ontologies in Dynamic Information Seeking Giovanni Modica Avigdor Gal Hasan M. Jamil

2 CoopIS2001 Trento, Italy Motivating example

3 CoopIS2001 Trento, Italy Preliminaries Definition: An ontology is an explicit representation of a conceptualization. (Gruber 1993) Conjecture I: Applications in a given domain base their information exchange on some (shared) underlying ontology. Observation: Application in a given domain use different ontology representation. Conjecture II: Given an application A such that A utilizes an ontology representation O A, and an ontology O, there exists an invertible mapping f A such that f A (O A )=O

4 CoopIS2001 Trento, Italy Problem description Given two applications A and B, such that A utilizes an ontology representation O A and B utilizes an ontology representation O B, introduce a mapping f BA such that f BA (O B )=O A In a perfect world: –O is known. –f A is known. –f B is known. O A = f A -1 (f B (O B )) Alas: –O is unknown. At best, an approximation of O exists, in a form of a standard. –f A and f B are unknown: lack of documentation, the mental state of a designer, etc.

5 CoopIS2001 Trento, Italy Proposed solution Given two applications A and B, such that A utilizes an ontology representation O A and B utilizes an ontology representation O B, introduce a mapping f BA such that f BA depends on the ontology representation. A matching is associated with a degree of confidence in the matching. 0 identifies non-matching terms. 1 identifies a crisp matching.

6 CoopIS2001 Trento, Italy Ontology representation Dynamic information seeking: –HTML forms Labels Input fields Scripts –Assumptions: Labels represent terms in an ontology ( e.g., Pick-up Date). Input fields provide constraints on the value domains ( e.g., {Day, 1, … 31}). Scripts, among other things, suggest a precedence relationship (e.g., Pick-up Locations is required before selecting a Car Type).

7 CoopIS2001 Trento, Italy Ontology representation Conceptual modeling approach Based on Bunge: –Terms (things) –Values –Composition –Precedence

8 CoopIS2001 Trento, Italy Ontology extraction and matching URL (e.g. HTML Parsing DOM Tree Phase 1 Parsing Phase 2 Labeling HTML Elements Label Identification FORM Elements rules Form Rendering Phase 3 Ontology Phase 4 Merging KB Submission Matching Algorithms Target/Candidate Ontology Target Ontology CandidateO ntology Refined Ontology Ontology Creation Thesaurus

9 CoopIS2001 Trento, Italy Phase 1: Parsing

10 CoopIS2001 Trento, Italy Phase 2: Labeling

11 CoopIS2001 Trento, Italy Phase 2: Labeling

12 CoopIS2001 Trento, Italy Phase 2: Labeling

13 CoopIS2001 Trento, Italy Merging Heuristics for the ontology merging (Frakes and Baeza-Yates, 1992) : Textual matching: Date datePickup pickup Ignorable characters removal: *Country country De-hyphenation: Pick-up PickupPickup Pick up Stop terms removal: Date of Return Return Date Stop terms: a, to, do, does, the, in, or, and, this, those, that, … etc. Substring matching: Pickup Location Code Pick-up location (66%) Content matching: Dropoff Day (1,..,31) Return Day (1,..,31)(100%) Dropoff Return Thesaurus matching: Dropoff Location Return Location (100%)

14 CoopIS2001 Trento, Italy Phase 4: Merging

15 CoopIS2001 Trento, Italy Preliminary Results Two metrics are used for performance analysis (Frakes and Baeza-Yates, 1992) : Recall (completeness) Precision (soundness) Parameters: t r : number of terms retrieved t m : number of terms matched t e : number of terms effectively matched Recall:Precision:

16 CoopIS2001 Trento, Italy Preliminary Results Example: # of terms in Ontology1: 20 # of matches identified: 15 Recall: 75%(15/20) # of effective matches: 10 Precision: 66% (10/15) A third metric is used to compare the recall and precision. For a precision value P, a recall value R and an importance measure b, the combined metric E is calculated as (Frakes and Baeza-Yates, 1992) :

17 CoopIS2001 Trento, Italy Preliminary Results

18 CoopIS2001 Trento, Italy Preliminary Results

19 CoopIS2001 Trento, Italy Preliminary Results

20 CoopIS2001 Trento, Italy Summary and Future Work We have introduced: –Automatic ontology creation –Automatic matching process –Preliminary results Future work oriented towards: –Incorporation of query facilities into the tool –Automatic navigation of web sites for ontology extraction –Dynamic translation between queries against the target ontology to queries against the multiple candidate ontologies


Download ppt "CoopIS2001 Trento, Italy The Use of Machine-Generated Ontologies in Dynamic Information Seeking Giovanni Modica Avigdor Gal Hasan M. Jamil."

Similar presentations


Ads by Google