Keyword Search in RDF Databases
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1 Keyword Search in RDF Databases Charalampos S. Nikolaou Department of Informatics & Telecommunications University of Athens MSc Dissertation Presentation April 15, 2011
2 Outline Background User Requirements Our Approach Implementation Experimental Evaluation Other Directions to Keyword Search Conclusions & Future Work
3 Background
4 Keyword Search on (Semi-)Structured data Data model Graph-based data model Schema-aware Schema-agnostic (comes with specialized indexes)
5 Keyword Search on (Semi-)Structured data Data model Graph-based data model Schema-aware Schema-agnostic (comes with specialized indexes) Exploration Algorithms Backward Expansion Bidirectional Expansion Variants of the above
6 Keyword Search on (Semi-)Structured data Data model Graph-based data model Schema-aware Schema-agnostic (comes with specialized indexes) Exploration Algorithms Backward Expansion Bidirectional Expansion Variants of the above Structure of the Answer Trees Graphs Nodes/Entities (Multi-)Relations
7 Keyword Search on (Semi-)Structured data Data model Graph-based data model Schema-aware Schema-agnostic (comes with specialized indexes) Structure of the Answer Trees Graphs Nodes/Entities (Multi-)Relations Exploration Algorithms Backward Expansion Bidirectional Expansion Variants of the above Ranking/Scoring of Answers TF/IDF and other more complex measures from IR Node/Edge weights Page Rank like, Hub/Authority nodes Path length Number of nodes/edges
8 Keyword Search on (Semi-)Structured data Data model Graph-based data model Schema-aware Schema-agnostic (comes with specialized indexes) Structure of the Answer Trees Graphs Nodes/Entities (Multi-)Relations Exploration Algorithms Backward Expansion Bidirectional Expansion Variants of the above Ranking/Scoring of Answers TF/IDF and other more complex measures from IR Node/Edge weights Page Rank like, Hub/Authority nodes Path length Number of nodes/edges
9 User Requirements
10 Requirements Use Case Historians interested in the evolution of Biotechnology and Renewable Energy (see Papyrus Project)
11 Requirements Use Case Historians interested in the evolution of Biotechnology and Renewable Energy (see Papyrus Project) 1. Search for information about certain concepts, facts, events e.g., stem cells and public opinion
12 Requirements Use Case Historians interested in the evolution of Biotechnology and Renewable Energy (see Papyrus Project) 1. Search for information about certain concepts, facts, events 2. Search may involve temporal restrictions e.g., evolution of biotechnology in 20th century
13 Requirements Use Case Historians interested in the evolution of Biotechnology and Renewable Energy (see Papyrus Project) 1. Search for information about certain concepts, facts, events 2. Search may involve temporal restrictions 3. Source of information may contain indefinite temporal information e.g., around 7000 BC biotechnology involved brewing beer, fermenting wine and baking bread with help of yeast
14 Our Approach
15 Data Model Extension of the temporal RDF data model with indefinite time intervals for the validity of a triple: (s,p,o)[i] Validity of a resource r: (r, subclass, Resource)[i]
16 Data Model Extension of the temporal RDF data model with indefinite time intervals for the validity of a triple: (s,p,o)[i] Validity of a resource r: (r, subclass, Resource)[i] Indefinite time intervals (set I ): (s 1,s 2,e 1,e 2 ), where s 1,s 2,e 1,e 2 are natural numbers s 1 s 2 e 1 e 2
17 Data Model Extension of the temporal RDF data model with indefinite time intervals for the validity of a triple: (s,p,o)[i] Validity of a resource r: (r, subclass, Resource)[i] Indefinite time intervals (set I ): (s 1,s 2,e 1,e 2 ), where s 1,s 2,e 1,e 2 are natural numbers s 1 s 2 e 1 e 2 Indefinite time point: (s,e,s,e) s e
18 Query Language Keyword-based language extended with temporal constraints expressed in a controlled natural language
19 Query Language Keyword-based language extended with temporal constraints expressed in a controlled natural language Temporal constraints (set TR): Allen s temporal relations: before, after, meets, met by, overlaps, overlapped by, starts, started by, finishes, finished by, during, contains Other lexical forms (formal/informal) used in speech and writing
20 Query Language Keyword-based language extended with temporal constraints expressed in a controlled natural language Temporal constraints (set TR): Allen s temporal relations: before, after, meets, met by, overlaps, overlapped by, starts, started by, finishes, finished by, during, contains Other lexical forms (formal/informal) used in speech and writing Time intervals: [s 1 s 2,e 3 e 4 ], with each s i (e i ) being of the form yyyy/mm/dd
21 Example Database Berlin has been a city since some point in the 12th century (ex:city, rdf:type, rdfs:class). (ex:city, rdfs:label, "City"). (ex:berlin, foaf:name, "Berlin"). (ex:berlin, rdf:type, ex:city)[ , 2011].
22 Example Database Berlin has been a city since some point in the 12th century (ex:city, rdf:type, rdfs:class). (ex:city, rdfs:label, "City"). (ex:berlin, foaf:name, "Berlin"). (ex:berlin, rdf:type, ex:city)[ , 2011]. Information needs (1) I would like information about cities during the period city during [1200, 1210]
23 Example Database Berlin has been a city since some point in the 12th century (ex:city, rdf:type, rdfs:class). (ex:city, rdfs:label, "City"). (ex:berlin, foaf:name, "Berlin"). (ex:berlin, rdf:type, ex:city)[ , 2011]. Information needs (1) I would like information about cities during the period city during [1200, 1210] Answer There is such a city named Berlin
24 Example Database Berlin has been a city since some point in the 12th century (ex:city, rdf:type, rdfs:class). (ex:city, rdfs:label, "City"). (ex:berlin, foaf:name, "Berlin"). (ex:berlin, rdf:type, ex:city)[ , 2011]. Information needs (2) I would like information about cities during the period city during [1100, 1110]
25 Example Database Berlin has been a city since some point in the 12th century (ex:city, rdf:type, rdfs:class). (ex:city, rdfs:label, "City"). (ex:berlin, foaf:name, "Berlin"). (ex:berlin, rdf:type, ex:city)[ , 2011]. Information needs (2) I would like information about cities during the period city during [1100, 1110] Answer There is possibly a city named Berlin
26 Example The ideal answer The answer to both questions should include the individual Berlin and the class City, but in the second case these two entities should have lower score to convey the notion of possibility
27 Data Structures Data graph The underlying RDF graph
28 Data Structures Data graph The underlying RDF graph Schema graph A summary of the data graph, containing data-driven schema information
29 Data Structures Data graph The underlying RDF graph Schema graph A summary of the data graph, containing data-driven schema information Query graph Super graph of schema graph containing elements from the RDF graph
30 Data graph Subject Predicate Object pro1 type P roject pro2 type P roject pro1 name P apyrus pub1 type P ublication pub1 author res1 pub1 author res2 pub1 year 2010 pub2 type P ublication res1 type Researcher res2 type Researcher univ1 name DI&T res2 name Y. Ioannidis res1 name M. Koubarakis res1 worksat univ1 univ1 type University univ2 type University University subclass Agent Agent subclass Resource pub1 hasp roject pro Papyrus type name type pro 1 Project res2 name Yannis pro 2 Ioannidis hasproject type author year res 3 type Publication pub 2 DI&T Manolis Koubarakis pub 1 Researcher type type author type subclass name worksat res1 Agent subclass name univ 1 type subclass subclass univ 2 University Person Resource type Figure: a) RDF triples b) Data graph
31 Schema and Query graphs Project Researcher worksat University hasproject Publication author subclass Person subclass Agent subclass subclass Resource Figure: a) schema graph
32 Schema and Query graphs Project Manolis Koubarakis Project hasproject Publication author Researcher subclass Person subclass worksat Agent subclass subclass University Resource hasproject Publication year author name Researcher subclass subclass worksat Agent subclass subclass University 2010 Person Resource Figure: a) schema graph b) query graph for koubarakis publications during 2010
33 Keyword Search Algorithm Query processing in 4 phases: 1. Keyword Interpretation (KI): Interpret keywords as RDF graph elements (keyword elements) and construct the query graph
34 Keyword Search Algorithm Query processing in 4 phases: 1. Keyword Interpretation (KI): Interpret keywords as RDF graph elements (keyword elements) and construct the query graph 2. Graph Exploration (GE): Explore the query graph starting from keyword elements for finding top-k subgraphs
35 Keyword Search Algorithm Query processing in 4 phases: 1. Keyword Interpretation (KI): Interpret keywords as RDF graph elements (keyword elements) and construct the query graph 2. Graph Exploration (GE): Explore the query graph starting from keyword elements for finding top-k subgraphs 3. Query Mapping (QM): Map top-k subgraphs to SPARQL queries and evaluate them
36 Keyword Search Algorithm Query processing in 4 phases: 1. Keyword Interpretation (KI): Interpret keywords as RDF graph elements (keyword elements) and construct the query graph 2. Graph Exploration (GE): Explore the query graph starting from keyword elements for finding top-k subgraphs 3. Query Mapping (QM): Map top-k subgraphs to SPARQL queries and evaluate them 4. Entity Transformation (ET): Transform and rank entities appearing in subgraphs and results from query evaluation to entities
37 Keyword Search Algorithm Query processing in 4 phases: 1. Keyword Interpretation (KI): Interpret keywords as RDF graph elements (keyword elements) and construct the query graph 2. Graph Exploration (GE): Explore the query graph starting from keyword elements for finding top-k subgraphs 3. Query Mapping (QM): Map top-k subgraphs to SPARQL queries and evaluate them 4. Entity Transformation (ET): Transform and rank entities appearing in subgraphs and results from query evaluation to entities Running query koubarakis publications during 2010
38 Keyword Interpretation koubarakis publications during 2010 Project Researcher worksat University hasproject Publication author subclass Person subclass Agent subclass subclass Resource
39 Keyword Interpretation koubarakis publications during 2010 Project Manolis Koubarakis hasproject Publication year author name Researcher subclass subclass worksat Agent subclass subclass University 2010 Person Resource
40 Graph Exploration koubarakis publications during 2010
41 Graph Exploration koubarakis publications during 2010 Project Manolis Koubarakis hasproject Publication year author name Researcher subclass subclass worksat Agent subclass subclass University 2010 Person Resource
42 Graph Exploration koubarakis publications during 2010 Project Manolis Koubarakis hasproject Publication year author nam e Researcher subclass subclass worksat Agent subclass subclass University 2010 Person Resource
43 Query Mapping koubarakis publications during 2010
44 Query Mapping koubarakis publications during 2010 Project Manolis Koubarakis hasproject Publication year author nam e Researcher subclass subclass worksat Agent subclass subclass University 2010 Person Resource
45 Query Mapping koubarakis publications during 2010 Manolis Koubarakis nam e Publication author Researcher year 2010
46 Query Mapping koubarakis publications during 2010 Manolis Koubarakis nam e Publication author Researcher year 2010 Conjunctive query name(x, Koubarakis ) type(x, Researcher) author(y, x) year(y, 2010 ) type(y, Publication)
47 Query Mapping koubarakis publications during 2010 SPARQL Query SELECT?x?y?xl?yl WHERE {?x rdf:type ex:researcher.?x foaf:name "Manolis Koubarakis".?y ex:author?x.?x rdf:type ex:publication.?x ex:year "2010".?x rdfs:label?xl.?y rdfs:label?yl. }
48 Query Mapping koubarakis publications during 2010 Table: SPARQL result?x?y?xl?yl res 1 pub 1 Manolis Koubarakis
49 Entity Transformation koubarakis publications during 2010 Table: Answer Rank Entity 1 Manolis Koubarakis 2 Publication 3 pub 1 4 Researcher
50 Entity Transformation koubarakis publications during 2010 How ranking is performed? Table: Answer Rank Entity 1 Manolis Koubarakis 2 Publication 3 pub 1 4 Researcher
51 Scoring Functions Scoring of a graph (cont d) For any cost function c f : V E [0,1]: C G = c f (n) n p i p i P
52 Scoring Functions Scoring of a graph (cont d) For any cost function c f : V E [0,1]: C G = p i P n p i c f (n) Path length c f (n) c p (n) = 1/diam G, for any element n
53 Scoring Functions Scoring of a graph (cont d) For any cost function c f : V E [0,1]: C G = p i P n p i c f (n) Path length c f (n) c p (n) = 1/diam G, for any element n Keyword Matching c f (n) c kw (n) = { ε > 0, if 1 sim(n) = 0 1 sim(n), otherwise
54 Scoring Functions Scoring of a graph Popularity 1 n agg V, if n V C c f (n) c pop (n) = 1 n inc V, if n V E 1 n agg E, if n L R
55 Scoring Functions Scoring of a graph Popularity Combine 1 n agg V, if n V C c f (n) c pop (n) = 1 n inc V, if n V E 1 n agg E, if n L R c f (n) c comb (n) = c p (n) c kw (n) c pop (n)
56 Scoring Functions Scoring of an entity Entity Cost Represents the weighted average of the cost of an entity over the subgraphs it appears Cost(e,S) = C cf (e) minsgcost SG S (e) SG i SG S (e) 1 C cf (SG i )
57 Scoring Functions Scoring of an entity Entity Cost Represents the weighted average of the cost of an entity over the subgraphs it appears Cost(e,S) = C cf (e) minsgcost SG S (e) SG i SG S (e) 1 C cf (SG i ) Final Entity Cost Represents the average cost of an entity derived both during graph exploration and query mapping Cost(e,SGE)+Cost(e,QE) 2, if e SGE QE Cost(e) = Cost(e,SGE), if e SGE and e / QE Cost(e,QE), if e QE and e / SGE
58 Implementation
59 System Architecture Figure: The architecture of the keyword querying system
60 System Implementation RDF Store:
61 System Implementation RDF Store: Sesame
62 System Implementation RDF Store: Sesame Keyword index and full-text search:
63 System Implementation RDF Store: Sesame Keyword index and full-text search: LuceneSail
64 System Implementation RDF Store: Sesame Keyword index and full-text search: LuceneSail Query Processor:
65 System Implementation RDF Store: Sesame Keyword index and full-text search: LuceneSail Query Processor: Java implementation
66 Experimental Evaluation
67 Experimental Setup Machine CPU: Intel(R) Core(TM)2 Quad CPU 3.00 GHz, L KB Main Memory: 4 GB, 1033 MHz Hard Disk: 750 GB, 7200 rpm, 8 MB Buffer
68 Experimental Setup Machine CPU: Intel(R) Core(TM)2 Quad CPU 3.00 GHz, L KB Main Memory: 4 GB, 1033 MHz Hard Disk: 750 GB, 7200 rpm, 8 MB Buffer Datasets History Ontology (HO) Semantic Web Dog Food Ontology (SWDF) Digital Bibliography & Library Project Ontology (DBLP)
69 Experimental Setup Machine CPU: Intel(R) Core(TM)2 Quad CPU 3.00 GHz, L KB Main Memory: 4 GB, 1033 MHz Hard Disk: 750 GB, 7200 rpm, 8 MB Buffer Datasets History Ontology (HO) Semantic Web Dog Food Ontology (SWDF) Digital Bibliography & Library Project Ontology (DBLP) Evaluated Dimensions Efficiency: Load Scalability, Query Answering Performance Effectiveness: Precision/Recall, F-measure, NDCG
70 Dataset Characteristics Triples Classes Properties Instances Avg. Inst./Class HO 8, ,405 4 SWDF 88, , DBLP 55,364, ,609, ,775 HO: High schema complexity and small number of instances SWDF: Medium schema complexity and number of instances DBLP: Low schema complexity and high number of instances
71 Efficiency Load/Service Scalability # users # sessions # queries HO SWDF #seconds (log scale) #seconds (log scale) #users #users Figure: Load scalability for a) HO/SWDF and b) DBLP
72 Efficiency Index Performance (cont d) #seconds (thousands) LucenInf Inference Lucene Native Native Triple indexing on disk Lucene Native+Keyword indexing Inference Native+Schema graph inference LuceneInf Lucene+Schema graph inference #triples (millions) Figure: Index construction (DBLP)
73 Efficiency Index Performance #seconds HISTORY DOGFOOD #ms HISTORY DOGFOOD DBLP Dataset Native Lucene Inference LuceneInf HISTORY DOGFOOD DBLP Dataset Figure: a) Index construction time, b) Load times of schema graph index
74 Efficiency Scoring Function Performance (cont d) Figure: Tasks performance: HO
75 Efficiency Scoring Function Performance (cont d) Figure: Tasks performance: SWDF
76 Efficiency Scoring Function Performance Figure: Tasks performance: DBLP
77 Efficiency Scoring Function Performance Conclusion Scoring functions have similar computational characteristics
78 Efficiency Query Performance (cont d) Figure: Tasks performance: HO
79 Efficiency Query Performance (cont d) Figure: Tasks performance: SWDF
80 Efficiency Query Performance (cont d) Figure: Tasks performance: DBLP
81 Efficiency Query Performance Table: Tasks performance: Overall contribution KI GE QM ET HO SWDF DBLP
82 Efficiency Query Performance Table: Tasks performance: Overall contribution KI GE QM ET HO SWDF DBLP Conclusions HO: QP dominated by graph exploration SWDF: QP rather fairly distributed among querying tasks DBLP: QP dominated by keyword indexing
83 Effectiveness Measurement methodology (cont d) Precision (or how succinct is the answer) P = #(relevant items retrieved) #(retrieved items)
84 Effectiveness Measurement methodology (cont d) Precision (or how succinct is the answer) P = #(relevant items retrieved) #(retrieved items) Recall (or how much did it cover the question) R = #(relevant items retrieved) #(relevant items)
85 Effectiveness Measurement methodology (cont d) Precision (or how succinct is the answer) P = #(relevant items retrieved) #(retrieved items) Recall (or how much did it cover the question) R = #(relevant items retrieved) #(relevant items) F1-measure (or how much it is to the point) F 1 = 2PR P + R
86 Effectiveness Measurement methodology NDCG (or how good was the ranking) r i log 2 (i) NDCG k = r 1 + k i=2, k = 15 IDCG k
87 Effectiveness Measurement methodology NDCG (or how good was the ranking) Judgement 5 history experts r i log 2 (i) NDCG k = r 1 + k i=2, k = 15 IDCG k 20 keyword queries (2-3 keywords each) Relevance judgements for relevant entities
88 Effectiveness Figure: Effectiveness evaluation results (HO)
89 Other Directions to Keyword Search
90 Other Directions to Keyword Search Browsing Rich interfaces for result exploration, discovery of hidden inter-connections, and query refinement Zero-effort web publishing
91 Other Directions to Keyword Search Result Snippets Generation of small passage descriptions for quick judgement of results
92 Other Directions to Keyword Search Result Clustering Clustering of results according to different interpretations of the semantics of the query
93 Other Directions to Keyword Search Query Cleaning Semantic linkage and spelling corrections of database-relevant query keywords Segmentation of nearby query keywords so that each segment corresponds to a high quality data term
94 Conclusions & Future Work
95 Conclusions Design & implementation of a keyword-based system on RDF graphs with indefinite temporal information
96 Conclusions Design & implementation of a keyword-based system on RDF graphs with indefinite temporal information Evaluation results: rather scalable
97 Conclusions Design & implementation of a keyword-based system on RDF graphs with indefinite temporal information Evaluation results: rather scalable, modest performance
98 Conclusions Design & implementation of a keyword-based system on RDF graphs with indefinite temporal information Evaluation results: rather scalable, modest performance, modest effectiveness
99 Conclusions Design & implementation of a keyword-based system on RDF graphs with indefinite temporal information Evaluation results: rather scalable, modest performance, modest effectiveness Keyword interpretation and graph exploration call for improvement
100 Future Work Sesame/LuceneSail substitution by BigOWLIM
101 Future Work Sesame/LuceneSail substitution by BigOWLIM Keyword interpretation and graph exploration improvement
102 Future Work Sesame/LuceneSail substitution by BigOWLIM Keyword interpretation and graph exploration improvement Challenge: Integration and querying of semi-structured data and linked-data, stored in different formats (RDF, XML) and data sources (ontologies, knowledge bases, databases)
103 Future Work Sesame/LuceneSail substitution by BigOWLIM Keyword interpretation and graph exploration improvement Challenge: Integration and querying of semi-structured data and linked-data, stored in different formats (RDF, XML) and data sources (ontologies, knowledge bases, databases) Schema-agnostic or hybrid data model
104 This is the End...
105 References Check my dissertation
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