Leveraging Terminological Structure for Object Reconciliation

Transcription

1 Leveraging Terminological Structure for Object Reconciliation Jan Noessner, Mathias Niepert, Christian Meilicke, and Heiner Stuckenschmidt Lehrstuhl für Künstliche Intelligenz Prof. Dr. Heiner Stuckenschmidt University of Mannheim

2 Agenda Agenda Introduction Motivating Example Approach Algorithms Evaluation Future Work Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 2

3 Introduction 1 Introduction Different communities use different terms: Object Reconciliation / Record Linkage / Entity Resolution / Instance Matching: Typical task: Determine individuals (e.g. Arnold Schwarzenegger and Arni ) that belong to the same real world object. RDF dataset 1: RDF dataset 2: Governor elected to played in Kindergarden Cop Kindergarden Cop Terminator I Arnold Schwarzenegger Arni Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 3

4 Introduction 1 Introduction Existing work: Most existing work has focused on the design of specialized measures which estimate the similarity of objects based on their lexical properties. The use of schema information in the context of formal ontologies has only recently been proposed (for example in Fatiha Sais et al andhassanzadehet al. 2009) Our approach: considers schema information in a holistic and well-founded way. In particular: We leverage schema information to exclude logically inconsistent links between objects and to increase the logical overlap. We assume that both A-Boxes are described by the same T-Box. Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 4

5 Motivating Example 2 Motivating Example T-Box: Woman - Man Dog Person Person Woman Man A-Box 1 a 1 a 2 Woman(a 3 ) Dog(a 4 ) A-Box 2 b 2 b 1 Dog(b 3 ) Woman(b 4 ) a 3 a 4 b 4 b 3 a 5 a 6 b 6 b 5 Lexical similarity: σ= {1if the names are equal (e.g. Chris and Chris ), 0else.} Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 5

6 Standard Lexical Similarity Measures Motivating Example 2 Maximizing only the lexical similarity No differentiation between the pairs of individuals with name Alice(blue squares) A-Box 1 a 1 a 2 Bob(green squares), Chris(red squares). A-Box 2 b 2 b 1 Woman(a 3 ) Dog(a 4 ) Dog(b 3 ) Woman(b 4 ) a 3 a 4 b 4 b 3 a 5 a 6 b 6 b 5 Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 6

7 Approach 3 Reasoning based Approach Gray-colored assertions can be inferred from the given ones with respect to the T-Box a 1 a 3 a 5 a 2 Woman(a 3 ) Dog(a 4 ) a 4 a 6 Woman - Man Dog Person Person Woman Man Person(a 1 ) Woman(a 1 ) a 1 Person(a 3 ) Woman(a 3 ) a 3 Person(a 2 ) Man(a 2 ) a 2 Person(a 4 ) Dog(a 4 ) a 4 Person(a 5 ) Woman(a 5 ) Person(a 6 ) Man(a 6 ) a 5 a 6 Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 7

8 Approach 3 Step 1: Only Consider Valid Alignments We have a set of constraints to ensure the consistency of the instance alignments in order to avoid conflicts of the correspondences with the terminology. One example is: T A 1 C (a) T A 2 C (b) A-Box 1 (part) Person(a 2 ) Man(a 2 ) a 2 Person(a 4 ) Dog(a 4 ) T A 1 Person(a 2 ) T A 2 Person(b 4 ) A-Box 2 (part) Person(b 2 ) Man(b 2 ) b 2 Person(b 4 ) Dog(b 4 ) a 4 b 4 Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 8

9 Approach 3 Step 1: Only Consider Valid Alignments We have a set of constraints to ensure the consistency of the instance alignments in order to avoid conflicts of the correspondences with the terminology. One example is: T A 1 C (a) T A 2 C (b) A-Box 1 (part) Person(a 2 ) Man(a 2 ) a 2 Person(a 4 ) Dog(a 4 ) Onlypossiblevalid alignment: a 2,b 2 A-Box 2 (part) Person(b 2 ) Man(b 2 ) b 2 Person(b 4 ) Dog(b 4 ) a 4 a 4,b 4 b 4 Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 9

10 Approach 3 Step 2: Generate Optimal Alignment A-Box 1 A-Box 2 Person(a 1 ) Woman(a 1 ) a 1 Person(a 2 ) Man(a 2 ) a 2 Person(b 2 ) Man(b 2 ) Person(b 1 ) Woman(b 1 ) b 2 b 1 Person(a 3 ) Woman(a 3 ) Person(a 4 ) Dog(a 4 ) Person(b 4 ) Dog(b 4 ) Person(b 3 ) Woman(b 3 ) a 3 a 4 b 4 b 3 Person(a 5 ) Woman(a 5 ) a 5 Person(a 6 ) Man(a 6 ) a 6 Person(b 6 ) Man(b 6 ) b 6 Person(b 5 ) Woman(b 5 ) b 5 Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 10

11 Approach 3 Step 2: Generate Optimal Alignment Search the valid alignment with the maximal overlap: Two possible correspondences for Chris: <a 5, b 6 > <a 6, b 5 > and <a 5, b 5 > <a 6, b 6 >: First possibility: <a 5, b 6 > <a 6, b 5 >: σ(a 5,b 6 ) = 1 Person(a 5 ) Woman(a 5 ) a 5 Person(a 6 ) Man(a 6 ) a 6 σ(a 6,b 5 ) = 1 Person(b 5 ) Woman(b 5 ) b 5 Person(b 6 ) Man(b 6 ) b 6 Adds a score of 2 to the total overlap Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 11

12 Approach 3 Step 2: Generate Optimal Alignment Second possibility: <a 5, b 5 > <a 6, b 6 >: 2 σ(a 5,b 5 ) = 2 2 σ(a 6,b 6 ) = 2 Person(a 5 ) Woman(a 5 ) a 5 Person(a 6 ) Man(a 6 ) a 6 Person(b 5 ) Woman(b 5 ) b 5 Person(b 6 ) Man(b 6 ) b 6 [σ(a 5,b 5 ) + σ(a 6,b 6 )] / 2= 1 Adds a score of 5 to the total overlap Choose<a 5, b 5 > <a 6, b 6 > for the optimal alignment Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 12

13 Approach 3 Step 2: Generate Optimal Alignment A-Box 1 A-Box 2 Person(a 1 ) Woman(a 1 ) a 1 Person(a 2 ) Man(a 2 ) a 2 Person(b 2 ) Man(b 2 ) Person(b 1 ) Woman(b 1 ) b 2 b 1 Person(a 3 ) Woman(a 3 ) Person(a 4 ) Dog(a 4 ) Person(b 4 ) Dog(b 4 ) Person(b 3 ) Woman(b 3 ) a 3 a 4 b 4 b 3 Choose<a 1, b 1 > <a 3, b 3 > for the optimal alignment (because of the relation) <a 1, b 1 > <a 2, b 2 > <a 3, b 3 > <a 4, b 4 > <a 5, b 5 > <a 6, b 6 > is the optimal alignment. Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 13

14 Algorithms 4 Algorithms The underlying problem is the inexact multi-labeled graph matching problem NP-complete We implemented two algorithms using our approach: Optimal wabs*: Transformation to a linear integer program. Approximative wabs*: ApplicationoftheinexactgraphmatchingalgorithmofCour et al. (2007) in theareaof computer vision. Our baseline algorithms uses only lexical similarity. Optimal One-to-one: Maximizes the sum of the lexical confidence values with integer linear programming. by calculating the best functional one-to-one alignmen. GreedyOne-to-one: A fast Greedy method considering only functional one-to-one alignments. (*) wabs = weighted A-Box Similarity Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 14

15 Evaluation 5 Evaluation: Datasets Lexical similarity: SoftTFIDF string matching approach from Cohen et al. (2003) Benchmark: IIMB benchmark dataset of Ferrara et al. (2008) ( Data from a movie domain. 70 transformations in 4 transformation categories: Values Transformations (VT): Typographical errors are simulated. Structural Transformations (ST): modification of the datatype properties. Combination of VT and ST (VT& ST): The combination of the previous two types. Logical Transformations (LT): Instances are moved to different classes. Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 15

16 Evaluation 5 Evaluation: Results Optimal wabs; VT; Approximative wabs; VT; Optimal One-to-one; VT; Greedy One-to-one; VT; Optimal wabs; ST; Approximative wabs; ST; Optimal One-to-one; ST; Greedy One-to-one; ST; Optimal wabs; VT & ST; Approximative wabs; VT & ST; Optimal One-to-one; VT & ST; Greedy One-to-one; VT & ST; Optimal wabs; LT; Approximative wabs; LT; Optimal One-to-one; LT; Greedy One-to-one; LT; Optimal wabs Approximative wabs Optimal One-to-one Greedy One-to-one Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 16 Precision Recall Optimal wabs; VT; Approximative wabs; VT; Optimal One-to-one; VT; Greedy One-to-one; VT; Optimal wabs; ST; Approximative wabs; ST; Optimal One-to-one; ST; Greedy One-to-one; ST; Optimal wabs; VT & ST; Approximative wabs; VT & ST; Optimal One-to-one; VT & ST; Greedy One-to-one; VT & ST; Optimal wabs; LT; Approximative wabs; LT; Optimal One-to-one; LT; Greedy One-to-one; LT; Leveraging schema information improves Precision and Recall significantly. The approximative wabs approach has still very high Precision and Recall values. Stable against different transformation categories.

17 Evaluation 5 Evaluation: Results Execution Times in Seconds. Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 17

18 Future Work 6 Future Work Extend the approach to work with two different T-Boxes. Step1: Automatically process a terminological alignment to merge different T-Boxes Step2: Apply the approach on the merged T-Box Extend the approach to create both, instance and terminological alignments, in one step. Improve performance and scalability of the approach Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 18

19 Thank you Thank you! Thank you for your attention. Ifyouhaveanyquestions, feelfreetoask! Jan Noessner - Lehrstuhl für künstliche Intelligenz University of Mannheim 19