Enriching knowledge graphs with text processing...

Enriching knowledge graphs with text processing techniques

J.-M. Le Goff, CERN

A. Rattinger CERN & Graz University of Technology

I-Know, Graz, Austria 12 October 2017

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ERCIM News 111:https://ercim-news.ercim.eu/en111/r-i/collaboration-spotting-a-visual-analytics-platform-to-assist-knowledge-discovery

Agenda • Our approach:

• Building knowledge graph and graph data model • Identifying Concepts and relationships in full text • Enriching knowledge graph

• Text mining for graph extensions (preliminary) • Use case: patents and publications analytics

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Heterogeneous data sources Text Data model

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Why building a knowledge graph out of datasets?

• Datasets contain valuable information for visual analytics • Businesses, applications, domains, etc.

• Datasets are difficult to use directly for visual analytics • They contain complex structures with various data types • They come with different data representations

• Structured, semi-structured and unstructured • Only subsets are of interest for a particular analysis • Need domain specific information to understand analytics output • Build data network with elements of interest in datasets

• Vertices: data instances with labelled data types • Relationships: interconnectivity

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Data network stored as a graph • Complexity • Interconnectivity • Scalability • Multi dimensionality

Graphs are natural representations of large

and interconnected networks

• Node and relationship labels • Compact graph structure • Graph query language • No need for schema evolution

Data model is embedded in the graph

itself

• Data model: labels and relationships • Labels Graph dimensions • Relationships Interconnectivity between

Labels

Graphs of connected elements constitute multi-dimensional

networks

5 Data Network = Knowledge graph

Knowledge graph Data model

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Graph of data instances and relationships Graph data model: Schema

Data model embedded in knowledge graph

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Data source

1

Data source

n

Data sources

Knowledge Graph

…

Visual analytics

Visual analytics is performed on the network using its schema

Processing Populating Organising Labelling

Labelling • Labelling Vertices

• Semi-structured data: • Metadata Structural information

• Tags labels

• Structured data: • Relational Databases tables, fields labels

• Text processing to create new labels, new vertices • Labelling Relationships

• Semi-structured data: • Relationships from nested tags (Has, isPartOf, etc.)

• Structured data: • Relational Databases No labels in E-R Models

• Vertex Labels + text information to label relationships • Ex: IsA, send, receive, live, own, etc.

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Ex: Publications/Patents Metadata • Published Items

• Publications: (Scopus, WoK, etc.) • Organisation address (in data) • Keyword (in data) • Category (in data)

• Journal Category

• Patents: (PatStat, etc.) • Organisation Address (in data) • Category (in data)

• Patent class

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Data Model from Metadata tags

Document metadata Data Model: Graph of labels

Kw: Keyword, PubItem: Published Item OrgAdd: Organisation Address 10

PubItem:Pub

PubItem:Pat

Org: Addre

ss

KW Cat: Scat

Cat: PatClas

has has

has

has

has

Publications/Patents Metadata • Published Items

• Publications: • Organisation address Text processing

• Organisation (from other data sources: Company, Institute) • City • Country

• Keyword (in data) • Category (in data)

• Journal Category • Patents:

• Organisation Address Text processing • Organisation (from other data sources: Company, Institute) • City • Country

• Category (in data) • Patent class

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Exploiting text information

Document metadata Data Model: Graph of labels

Kw: Keyword, Org: Organisation, Inst: Institute, Comp: Company, Cny: Country Cty: City, OrgAdd: Organisation Address 12

PubItem:Pat

Cat: PatClas

PubItem:Pub: WoK

KW Cat: Scat

Cty Cny

Org: Comp

Org Address

Org: Inst

has has

has

has

has

isa

isa

isLocated isLocated

Data sources: - patent & publications metadata - Patent full text (USPTO)

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Preliminary work

Publications/Patents analytics

• Use case: Who are the key organisations active in a particular technology? • Motivations

• Technology monitoring, • How an emerging technology is evolving (Research Industry)

• foresight studies, • looking for partners, join collaborations

• Company, institution landscape

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Publications/Patents analytics (2) • Use case: What is the organisation

landscape of a technology? • Technique:

• Search for pub/pat matching “technology terms” • Titles and/or abstracts

• Issues • Quality of the “technology terms” to identify a technology • Search terms may not correspond to a single technology • Some pertinent publications and patents may not contain the

“technology terms”

• Use text processing to address these issues

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Add search output to knowledge graph

Search output: A subset of publications and patents matching “technology terms”

Search

PubItem:Pat

Cat: PatClas

PubItem:Pub: WoK

KW Cat: Scat

Cty Cny

Org: Comp

Org Address

Org: Inst

has has

has

has

has

isa

isa

isLocated isLocated

Illustrating the approach Through Silicon Via

• Search terms on titles of pub/pat: • “Through Silicon Via”

• Exact matching High quality output • “TSV”

• More but with lower quality output

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Wikipedia

Technology: Through Silicon Via (TSV)

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Keywords: Through Silicon Via (title)

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Keywords: TSV (title)

iKnow2017 20 TSV also means: Taura Syndrome Virus

Methodology

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Preliminary work

Approach • Index Patents • Patent specific preprocessing • Create document embedding / Feature

vectors (Doc2Vec) • Dimensionality reduction / Manifold learning

(t-SNE, LargeVis) • Visualization (Datashader – Large scale

visualization)

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Index Patents / Preprocessing • Current Dataset: USPTO Patents from 2006

- 2014 • Candidate Generation: Patents are indexed

for fuzzy search (lucene) • Preprocessing

• Clean HTML syntax • Remove references, stopwords

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Document Embedding • Numeric representation of text documents • Gensim Implementation - Based on

word2vec-CBOW

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Concept: Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents.

Dimensionality Reduction • Our dataset contains 2 million points with

300 Dimensions • Many costly techniques (MDS, t-SNE)

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Large Scale Visualization • Datashader

• Rasterization pipeline • Handles large amounts

of data

• Patent Class Labels • A: Human necessities • B: Performing operations;

transporting • C: Chemistry; Metallurgy • D: Textiles; Paper • E: Fixed Constructions • F: Mechanical engineering;

lighting; • G: Physics • H: Electricity

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Colours correspond to distinct labels (2,022,349 Patents)

Full text processing • Patent full text: Title + Abstract + Description

• USPTO full text patent 2006 – 2015 • Objective

• 1: Look for pertinent patents that do not contain ”Through Silicon Via”

• 2: Look for different meanings of “TSV”

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International Patent Classification (IPC)

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• Sections from A (“Human Necessities”) to H (“Electricity”) • Classes (A01 "Agriculture; forestry; animal

husbandry; trapping; fishing") • Subclasses, Group Number, Subgroup

• Example: H01L 23/00 (Details of semiconductor or other solid state devices)

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Patent Class G (Physics) Overview

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378,692 Patents

Patent Search (TSV or Through Silicon Via)

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Most prominent patent classes (1696 results) Document Embedding

Comparison of Through Silicon Via vs TSV

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• Relevant data differs for relevant search results

“Through Silicon Via” (954 Results) “TSV” (982 Results)

Through Silicon Via vs TSV

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• Relevant documents to the search for different terms

“Through Silicon Via” (954 Results) “TSV” (982 Results)

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Derive new relationships

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Search

PubItem:Pat

Cat: PatClas

PubItem:Pub: WoK

KW Cat: Scat

Cty Cny

Org: Comp

Org Address

Org: Inst

has has

has

has

has

isa

isa

isLocated isLocated

Search extended to publications

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• Publications: Titles + Abstracts • Publications offer a different viewpoint • Publications are classified according to the

n-closest patent classes

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• G6F: Electric Digital Data Processing (Through Silicon Via (Antenna))

• A61K: Medical or Veterinary Science (Taura-Syndrom-Virus, tachycardia beat, Tellerspülvermögens)

Conclusion • Networks populated with metadata need to be

enriched to properly support Visual Analytics • Enrichment can come from additional data sources or

from text processing on the documents referenced in the metadata

• Preliminary text processing results on patents (title, abstract and description) indicate that it is possible to: • Enrich a patent set w.r.t. a search result • Regroup patents via patent categories showing different

meaning of search terms • Link some of the publications with nearby patents

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Thank you for your attention!