Experiences on integrating explicit knowledge on information access tools in the medical domain

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Experiences on integrating explicit knowledge on information access tools in the medical domain

Manuel de la Villa Department of Information Technologies University of Huelva

Extractive Summarization

Query user-defined

expansion

Post-retrieval clustering

Computer-aided

summarization

+Index

Brief CV Why a research stay? In Wolverhampton?

Teaching

Integrating explicit knowledge on information access tools Knowledge sources (UMLS & Freebase) Automatic Text Summarization Information Retrieval

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Research Group in Computational Linguistics (Univ. Wolverhampton), June 20th 2011

+Brief CV

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+Teaching experience

Software Engineering Process and Methodologies, Metrics,

Requirements analysis, Design, … Software Engineering Lab (UML, NetBeans,

Subversion, Java, JUnit, Persistence…)

Multimedia applications development Adobe Director, Flash, Photoshop, Premiere Sony Sound Forge, Audacity

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+Knowledge integration

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+ Specific Domain Knowledge source. UMLS (I)


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ICD-10

MeSH

SNOMED-CT

DSM-IV

LOINC

UK-Clinical Terms

RxNorm Gene Ontology

…

A saturation of different terminologies

UMLS aims to overcome a significant barrier, the variety of ways the same concepts are expressed in different machine-readable sources.

UMLS

An homogeneus group of terminologies

+ Specific Domain Knowledge source. UMLS (II)

Project NLM Unified Medical Language System (UMLS):

Aim, to develop tools that help researchers in the knowledge representation, retrieval and integration of biomedical information.

UMLS Knowledge Sources ‏

Software tools

Three main components:

SPECIALIST Lexicon: Compilation of lexical elements (>200.000) with grammatical information and linguistic variants.

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“Anaesthetic” {base=anesthetic spelling_variant=anaesthetic entry=E0330018 cat=noun variants=reg variants=uncount }

“Anaesthetic” {base=anesthetic spelling_variant=anaesthetic entry=E0330019 cat=adj variants=inv position=attrib(3) position=pred stative }

+ Specific Domain Knowledge source. UMLS (III)

Metathesaurus: very large, multi-purpose, and multi-lingual vocabulary database (compiles more than 100 source vocabularios),

every term (>5M) associated with a concept (>1.5M), terms related (e.g., synonyms) (16M relations)

each concept assigned to one or more semantic types of the 135 existing

Different terms…

for a same concept…

Included in a semantic type

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https://uts.nlm.nih.gov/metathesaurus.html

+Specific Domain Knowledge source. UMLS (IV)

UMLS Semantic Network: is an ontology with 135 semantic types and to 54 types of relationships between types

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https://uts.nlm.nih.gov/semanticnetwork.html

+ General Domain Knowledge Source: Freebase (I)

Freebase is a large public database that collects three kinds of information: data;

texts; and media, that references…

…entities or topics (≈ 12 million). An entity is a unique single person, place, or thing.

A single concept or real-world thing. A topic could also be called an entity, resource or element or thing, it is a

fundamental unit in Freebase. /common/topic Each topic has a Guid or globally unique ID

http://www.freebase.com/view/en/barack_obama http://www.freebase.com/guid/9202a8c04000641f800000000029c277

+ General Domain Knowledge Source: Freebase (II)

Freebase connects entities together as a graph,

defines its data structure as a set of nodes and a set of links that establish relationships between the nodes.

Most of our topics are associated with one or more types (such as people, places, books, films, etc) and may have additional properties like "date of birth" for a person or latitude and longitude for a location. These types and properties and related concepts are called Schema.

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Schema (the way Freebase's data is laid out) is expressed through Types and Properties. Types are grouped together in Domains.

General Domain Knowledge Source: Freebase (III)The Schema

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+ General Domain Knowledge Source: Freebase (IV) The Schema: Medicine

+ General Domain Knowledge Source: Freebase (V) How can we use it…

As a reference or information source

Create interesting Views and Visualizations and share them with others

Embed Freebase data in your website

Use our API or Acre, our hosted app development platform, to build apps that use Freebase data

Download our Data dumps

Use Freebase's RDF for Semantic Web applications

+ General Domain Knowledge Source: Freebase (IV) The Freebase approach

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• http://api.freebase.com/api/service/mqlread?query={"query":{"type":"/music/artist","name":"U2","album":[]}}

• http://api.freebase.com/api/service/mqlread?query={"query":[{"type":"/medicine/disease", "name":null, "symptoms":{"name":"Nausea"}}]}

• Query Editor

MQL (Metaweb Query Language)


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+Experiences in Automatic summarization (I)

+ We develop a proposal with this main characteristics:

Sentences extraction

Document representation as a graph

Centered on biomedical concepts

Using concept frequency to measure relevance

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+Experiences in Automatic summarization (II)

+ Phase I: Graph generation Sentences and UMLS concepts identification

+ Phase II: Similarity algorithm Concepts overlapping between sentences

(edges) means “recommendation”

+ Phase III: Ranking algorithm Weight associated with each edge depends on

similarity

+ Phase IV: Summary building Top ranked sentences are selected

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similarity


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similarity


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similarity


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similarity


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+Automatic Summarization. Evaluation

Evaluation with ROUGE (based on n-grams) against generic summarizers Our method obtains good results, specially with small n-grams

de la Villa, M., Maña, M. “Propuesta y evaluación de un método de generación de resúmenes extractivo basado en conceptos en el ámbito biomédico”. XXV edición del Congreso Anual de la Sociedad Española para el Procesamiento del Lenguaje Natural 2009 (SEPLN´09) San Sebastián (Sept-2009).

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+Experiences in Computer-aided summarization(I)

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Computer-aided summarization combines automatic and human summarization.

The CAS system suggest an initial summary, selecting relevant sentences

The human can change the sentences selection and edit manually the summary.

Purpose: construction of a Gold-Standard building assistant.

Novelty: Considering biomedical concepts distribution (Reeve et al., 2006)

+Experiences in Computer-aided summarization(and II)

Experience in the design and construction of a

Gold-Standard building assistant (or Computer-aided summarization)

Considering biomedical concepts distribution

(Reeve et al., 2006)

-Client-server app -Centralized repository

-Supports PDF, XML

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+Experiences in Information Retrieval and Post-retrieval clustering

Experience in the design and construction of an information

retrieval system with: • Post-retrieval clustering, • orientation to biomedical

documents and • mobile devices

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Document sources: Biomed Central (web crawling in progress) Text Processing: lowercasing, stemming, stop-words ,…

Search and Informa.on Retrieval Our implementa.on

Lucene for indexing…

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Search and Informa.on Retrieval Our implementa.on (and II)

… and Lucene for searching

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Weka for Clustering Clustering

Our implementa.on

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The post-processing clustering is to associate, according to their similarity, a set of documents retrieved from a query in different subsets

Clustering algorithm:

Simple-K-Means vs Expectation Maximization

Algorithms Querys (Documents)

Simple-‐K-‐means EM

Ligaments (10) 1 2

Cancer Skin (25) 4 12

Cancer (46) 5 26

Disease (62) 8 57

Time it takes to perform the grouping in seconds

K? It depends on the number of documents retrieved.

Clustering Why Simple-‐K-‐Means?

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Cancer skin

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Visualiza.on on Mobile Devices Our interface


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User have problems to define their information needs in a query string (Jansen, Spink y Koshman, 2007). Queries containe less than three terms (75,2%) and the majority of

queries contained one (18,5%), two (32,2%)

Methods to improve (expand) query: Relevance feedback. Local analysis or global analysis.

Natural Language Processing Resources.

Experiments with users show the preferences of these to maintain control over how the query is reformulated (Belkin et al., 2001).

Experiences in Information Retrieval and Query user-defined expansion (I)

+Experiences in Information Retrieval and Query user-defined expansion (II)

Experience on using Ontologies to assist the definition of the search string… previosly

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Pre-retrieval Construction o f the Graph

How does it works?

Experiences in Information Retrieval and Query user-defined expansion (II)

+Research: Information Retrieval (and III)

… or using Ontologies to build an enriched concept graph that assist the definition of the search string

http://www.uhu.es/manuel.villa/viewmed/ de la Villa, M., Garcia, S., Maña, M. “¿De verdad sabes lo que quieres buscar? Expansión guiada visualmente de la cadena de búsqueda usando ontologías y grafos de conceptos”. XXVII edición del Congreso Anual de la Sociedad Española para el Procesamiento del Lenguaje Natural 2011 (SEPLN´11) Huelva (Sept-2011).

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+Tools knowns. Expectations.

UMLS: Metathesaurus, Semantic Network

Tools:

Metamap, MMTx API,

Semrep UTS Web Services, …

Freebase

MQL (Metaweb Query Language)

Newbie with UIMA & GATE

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I offer my collaboration if you’re interested in using any of these resources

I’m open to collaborate on whatever task you consider related and…

… to receive some guidelines to improve summarization method

Any questions?