as inference: • Integration of structured domain knowledge (ontologies) with • Statistical, information retrieval methods Provides the necessary mechanism for inference For effective semantic search of medical data. Wednesday, 11 December 13
as inference: • Integration of structured domain knowledge (ontologies) with • Statistical, information retrieval methods Provides the necessary mechanism for inference For effective semantic search of medical data. Models that elicit the meaning behind the words found in documents and queries semantics Wednesday, 11 December 13
as inference: • Integration of structured domain knowledge (ontologies) with • Statistical, information retrieval methods Provides the necessary mechanism for inference For effective semantic search of medical data. Wednesday, 11 December 13
as inference: • Integration of structured domain knowledge (ontologies) with • Statistical, information retrieval methods Provides the necessary mechanism for inference For effective semantic search of medical data. Wednesday, 11 December 13
as inference: • Integration of structured domain knowledge (ontologies) with • Statistical, information retrieval methods Provides the necessary mechanism for inference For effective semantic search of medical data. Leveraging inference and semantics to infer relevant information. Wednesday, 11 December 13
as inference: • Integration of structured domain knowledge (ontologies) with • Statistical, information retrieval methods Provides the necessary mechanism for inference For effective semantic search of medical data. Wednesday, 11 December 13
similar meaning • hypertension = high blood pressure • Exacerbated in medical domain: • Medications and abbreviations • Associational and deductive inference required to overcome 4 Wednesday, 11 December 13
competing paradigms, actually should be seen as complementary. “Attack cognitive problems on different levels” [Gardenfors, 1997] Wednesday, 11 December 13
defined syntactic structures and has definite semantic interpretations • Inference is typically based on first order logic and is therefore deductive. • Often realised as ontologies. Wednesday, 11 December 13
reasoning Reliance on deductive reasoning No natural measure of semantic similarity Dealing with uncertainty and inconsistency Explicit background knowledge Context Insensitive Coverage Standardisation and interoperability Dealing with natural language Wednesday, 11 December 13
No support for deductive reasoning Context specific Generally applicable No explicit background knowledge Support for natural language Dependence on terms Wednesday, 11 December 13
Conceptual Implication Inferences of Similarity Bag-of-concepts Model Vocabulary Mismatch Graph-based Concept Weighing Model Vocabulary Mismatch Inference of Similarity Wednesday, 11 December 13
Mismatch Conceptual Implication Inferences of Similarity Bag-of-concepts Model Vocabulary Mismatch Graph-based Concept Weighing Model Vocabulary Mismatch Inference of Similarity Wednesday, 11 December 13
“background” importance of concept in medical domain: corpus document domain w ( c, dc) = idf ( c ) ⇤ S ( vc) ⇤ log( |Vs( c ) | ) Wednesday, 11 December 13
Mismatch Conceptual Implication Inferences of Similarity Bag-of-concepts Model Vocabulary Mismatch Graph-based Concept Weighing Model Vocabulary Mismatch Inference of Similarity Wednesday, 11 December 13
or concept • Information Relationships • Relationship between IUs • Information Graph • Queries & Documents u 2 U R ✓ U ⇥ U G = hU, Ri q = hu0, . . . , um i d = hu0, . . . , un i Wednesday, 11 December 13
and document IU: 39 P(d ! q) = K uq 2q m ud 2d P(ud ! uq) / K uq 2q m ud 2d P(ud |d) (ud, uq) RSV(d, q) = Y uq 2q Y ud 2d P(ud |d) (ud, uq). Wednesday, 11 December 13
document vectors • SNOMED Relationship types • e.g., ISA, Causative agent or Finding site • Manually assigned weights u1 ra u2 49 Concept A Concept B Wednesday, 11 December 13
(22/22) #959 386637004 Is a (0.1) 360239007 Method (0.1) 128927009 Is a (0.1) Abortion (22/22) #214 Disorder of pregnancy (0/0) #1 Is a (0.1) • • • • • • • • • • • 0 2 4 6 8 10 0.0 0.4 0.8 Query 137 Depth bpref • • • • • • • • • • • 0 2 4 6 8 10 0.0 0.4 0.8 Query 139 Depth bpref Query 139: “Patients who presented to the emergency room with an actual or suspected miscarriage” • Very hard queries; semantic gap cannot be bridged. • No domain knowledge available for terms/concepts in the query. • Inference has no effect. Wednesday, 11 December 13
as inference: • Integration of structured domain knowledge (ontologies) • Statistical, information retrieval methods Provides the necessary mechanism for inference 65 Wednesday, 11 December 13
as inference: • Integration of structured domain knowledge (ontologies) • Statistical, information retrieval methods Provides the necessary mechanism for inference 65 • Corpus Graph • Concept-based representations Wednesday, 11 December 13
• Definition vs. Retrieval Inference • The “what” vs. the “how” • Devise a domain knowledge resource specifically suited to retrieval inference? 66 [Frixione and Lieto, 2012] Wednesday, 11 December 13
for Medical IR 2. Graph-based Concept Weighting model 3. Unified model of semantic search as inference: Graph Inference Model 70 [Ch. 4] [Ch. 5] [Ch. 6] Wednesday, 11 December 13
Understanding when and how to apply inference 4.5. Quality of underlying representation 5. Identification of Semantic Gap problems 6. Evaluating semantic search 71 [Ch. 6,8] [Ch. 2] Wednesday, 11 December 13
domain knowledge and data-driven IR methods. • Allows IR systems to exploit valuable information trapped in domain knowledge resources • GIN generally defined and applicable to other applications wanting to utilise structured knowledge resources for more effective semantic search 72 Wednesday, 11 December 13
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