University of the Aegean AI LAB Ontology Learning Εργαστήριο Τεχνητής...

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University of the Aegean AI LAB www.icsd.aegean.gr Ontology Learning Εργαστήριο Τεχνητής Νοημοσύνης και Στήριξης Αποφάσεων AI Lab Department of Information and Communication Systems Eng. University of the Aegean 83200 Karlovassi, Samos, Greece

Transcript of University of the Aegean AI LAB Ontology Learning Εργαστήριο Τεχνητής...

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Ontology Learning

Εργαστήριο Τεχνητής Νοημοσύνης και Στήριξης Αποφάσεων

AI LabDepartment of Information and Communication Systems Eng.

University of the Aegean83200 Karlovassi, Samos, Greece

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Structure Introduction Ontology Learning approaches Learning from Social Data Evaluation Future trends

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The research problem lack of user involvement in semantic

content creation tasks small number of Web users (only “SW

people”), (may) annotate their Web resources semantically or build and publish ontologies

“tackle the incentive bottleneck in semantic content creation” Ontologies RDF data

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Aim of SW research community To encourage large-scale user participation the SW community must identify/propose:

incentive structures (spurs) means to motivate humans to become part of

the Semantic Web movement …to contribute their knowledge and time to create

useful ontologies and to use these in annotating documents, images, videos or even Web services

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Possible solutions/proposals Apply Web 2.0 successful stories to SW Borrow incentives from Intranet application

Apply to SW exploit collective intelligence and the

"Wisdom of Crowds“ (community-driven SW applications)

Make the creation of semantic content FUN (games?)

Automate the creation of semantic content “in some degree”

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Automated ontology learning Focus on “useful” ontologies

Ontologies that reflect users’ search intentions Automate the creation of “kick-off”

ontologies To assist users to participate in the ont. Eng.

Life cycle in a more easy way Automate the creation of “fully fledged”

ontologies to assist semantic querying of SWDs

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A modern approach Ontology learning from social data Query Logs:

Queries reflect users’ search intentions, thus: Learned ontologies will be more suitable for SW

searching Learned ontologies will reflect domain knowledge

related to a specific problem/application (assist soft. agents)

Mining techniques to cluster queries in domains

NLP and ontology matching techniques To reformulate NL query and build the ontology

(query-ontology)K. Kotis 7 - QueryOnto

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Other O.L approaches From text/corpora

Texts are noisy and hard to process Learned ontologies are too broad whereas

queries are usually expressed in an extremely synoptic manner (a short sequence of keyword terms), focused on specific views in the domain of discourse

From Web 2.0 data (tags in folksonomies) Not an easy way to identify structure (on-going research) Cannot identify POS (with high precision) focus on the creation of light-weight ontologies (mostly

taxonomies)

Both may be suitable for a “Kick-off” ontology BUT ? for a “useful” and “fully-fledged” one

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Other applications of query logs mining Detecting influenza epidemics using

search engine query data monitor health-seeking behaviour in the form

of queries to online search engines analysing large numbers of Google search

queries the relative frequency of certain queries is

highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms

Add formal semantics to solve such problem

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O.L in O.E life cycle

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Requirements Specification

Ontology Learning

Knowledge acquisition Develop&

Maintain

Exploitation

Use

Evaluate

Conceptualization

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O.L in HCOME

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Subtasks in ontology learning Extract the relevant domain terminology and synonyms

from a text collection Discover concepts which can be regarded as abstractions of

human thought Derive a concept hierarchy organizing these concepts Extend an existing concept hierarchy with new concepts Learn non-taxonomic relations between concepts Populate the ontology with instances of relations and

concepts Discover other axiomatic relationships or rules involving

concepts and relations

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Extracting the Relevant Terminology

Assumption: some terms unambiguously refer to a domain-specific concept extracting the relevant domain terminology

from a text collection counting raw frequency of terms, applying

information retrieval methods such as TF-IDF (see [Baeza-Yates & Ribeiro-Neto 1999]) OR

applying more sophisticated methods (see [Frantzi & Ananiadou 1999])

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Discovery of Synonyms apply clustering techniques to group

similar words togetherOR use some association measure to detect

pairs of statistically correlated terms ([Manning & Schόtze 1999]).

The detection of synonyms can help to cluster terms to groups of terms sharing (almost) the same meaning, thus representing ontological classes.

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Learning Conceptsand Concept Hierarchies Intentionally: by a descriptive label or its

relationships to other classes extensionally: by specifying a set of

instances belonging to this class Unsupervised hierarchical clustering techniques

known from machine learning research (very noisy as they highly depend on the frequency and behavior of the terms in the text collection under consideration) learn concepts at the same time since they also group

terms to meaning-bearing units can be regarded as abstractions over words and thus, to

some extent, as conceptsK. Kotis 16 - QueryOnto

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Learning Conceptsand Concept Hierarchies (2) supervised hierarchical clustering

directly involving the user to validate or reject certain clusters

including external information to guide the clustering process

Hearst Patterns (Marti Hearst) certain patterns in text reliably indicate a

relation of interest between terms E.g. “X such as Y” for example indicates that Y

is a subclass of X

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Extending Concept Hierarchy with new

Concepts…by adding a new concept at an appropriate position in the

existing taxonomy

Supervised methods: classifiers need to be trained which predict membership for every

concept in the existing concept hierarchy. need a considerable amount of training data for each concept, such approaches do typically not scale to arbitrary large ontologies.

Unsupervised approaches: assume a similarity function which computes a measure of fit between

the new concept and the concepts existing in the ontology. rely on an appropriate contextual representation of the different

concepts on the basis of which similarity can be computed. the hierarchical structure of the ontology needs to be considered and

somehow integrated into the similarity measure

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Learning Non-Taxonomic Relations

…learn the “flesh” of the ontology i.e. a set of non-taxonomic relationships essential for expressing domain-specific

properties of both classes and instances

E.g. identify verbs in text as indicatorsof a relation between their arguments

(object properties)

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Ontology Population…adding instances of concepts and relations

to the ontology

Hearst Patterns work well

An easy task in O.L

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O.L Applications Use of ontologies which (to a large extent) are not

axiomatized in the sense of a logical theory Such ontologies typically consist of a set of concepts and a

loosely defined taxonomic organization of these concepts Such semi-formal ontologies (Gruber, 2004) have the

potential of providing a benefit for applications which need some abstraction over plain words but do not mainly rely on logical reasoning.

Such applications can be mainly found in the fields of information retrieval, text mining and machine learning.

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BT Digital Library Case Study

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A modern Approach Mining query logs in an Organizational K.M

setting queries reflect organization-specific users’

search interests (queries already clustered) Queries’ history and personalization is

important Mining query logs in the open Web

Search Engines query logs (e.g. Yahoo!) preprocessing step is applied for the

organization (clustering) of queries in domain-specific data sets

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The architecture

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The Algorithm

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1. Identify key terms: “Cut” terms that occur more than once in the query log

2. Identify significant neighbor terms of the key terms

identifies mainly nouns, verbs, and

adjectives in order to be able to apply

simple heuristics e.g. for the creation of object properties

OWL Subsumption, equivalent and disjoint axioms WordNet Hypernym/Hyponym, Synonym and Antonym relations

Object properties and individual objects are also discovered using heuristic rules

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Domain clustering of Web queries … in order to reflect domain-specific users’

search interest (necessary condition of our approach)

Algorithm Requirements: cope with large data sets, in terms of time and

computational cost No prior input as regards to the number of

cluster Incremental algorithm, since new queries are

constantly fed in to search engines

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Domain clustering of Web queries (2) Clustering method:

Incremental DBSCAN [Ester et al. 1998] Similarity function

Cosine similarity of weighted term vectors

1. “Cut” Stop-words 2. Apply Porter Stemmer3. Compute weights (tf-idf)4. Compute the cosine similarity function

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Domain clustering of Web queries (3) Advanced methods for discovering

similarity between queries that do not share common keywords Latent Semantic Indexing (LSI or LSA) Similarity as proportional to the number of

commonly selected documents (from resulted ones)

… the nominator denotes the number of common documents clicked and the denominator expresses the maximum number of documents clicked for each query

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Technologies used JENA API PELLET reasoner Stanford POS tagger WordNet lexicon Clustering algorithms: DBSCAN Porter Stemmer Similarity functions: cosine and cross-

reference Yahoo! query log (licensed)

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Evaluation strategies Exploitation of learned ontologies in

applications (SW search e.g. SAMOS) Manual evaluation by experts (O.Eng. And

domain experts using e.g. Protégé tool) Automated evaluation by the computation

of similarity functions against a Gold ontology (e.g. OntoEval tool) Generic ontology alignment tools can be used

also

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Evaluation via predictions… researchers have realized that the output of ontology learning

algorithms is far from being perfect

To make the process controllable, we need an assessment ofhow certain an algorithm is in its predictions.

Numeric confidence values of an algorithm in the certainty of a prediction could then be used as a basis to combine different algorithms compensating for the drawbacks and false predictions of each other.

The representation of uncertainty and the combination of algorithms given their certainty are thus inherently coupled and represent one of the main open problems in the field of ontology learning.

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The OntoEval approach Dellschaft and Staab, 2006 computes measures including

Lexical Precision/ Lexical Recall, Taxonomic Precision/ Taxonomic Recall, F-Measure

Given a computed core ontology OC and a reference ontology OR ….

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Lexical Precision/recalllexical precision (LP) and lexical recall (LR) are

defined as:

reflecting how good the learned lexical terms cover the target domain

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Lexical Precision/recall (2)if one compares OC1 and OR1 with each other, one

gets LP(OC1,OR1) = 4/6 = 0.67 and LR(OC1,OR1) = 4/5 = 0.8

Example reference ontology (OR1, left) and computed ontology (OC1, right)

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Local Taxonomic precision the similarity of two concepts is computed

based on characteristic extracts from the concept hierarchy i.e. the position of a concept in the hierarchy

two extracts should contain many common objects if the characterized objects are at similar positions in the hierarchy

The proportion of common objects in the extracts should decrease with increasing dissimilarity of the characterized concepts

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Local Taxonomic precision (2) Given such a characteristic extract ce,

the local taxonomic precision tpce of two concepts c1 ϵ OC and c2 ϵ OR is defined as:

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Global Taxonomic Precision/Recall Local Taxonomic Precision + Semantic

Cotopy Semantic Cotopy (sc) = all super/sub-

concepts of a class in the ontology heavily influenced by the lexical precision of OC

because with decreasing lexical precision more and more concepts of sc(c, OC) are not contained in OR and sc(c, OR).

To overcome the problem, we can use the Common Semantic Cotopy (csc)

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Global Taxonomic Precision/Recall (2) Common Semantic Cotopy (csc)

excludes all concepts which are not also available in the other ontology’s set of concepts

use the common semantic cotopy and by computing the taxonomic precision values for the common concepts of both ontologies

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Global Taxonomic Precision/Recall (3) Balance precision/recall using their f-

measure The harmonic mean of the global taxonomic

precision and recall

The harmonic mean H of the positive real numbers x1, x2, ..., xn is defined to be

                                                                         

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Related Work to the modern appr. Mining of query logs to assist ontology learning

from relational databases Park et al (2003)

Lightweight ontologies - based on selected documents returned from queries

ORAKEL (2006) a target corpus must be available to construct lexicons

that will then assist the learning method Gulla et al (2007)

Not a fully automated approach Sekine & Suzuki (2007)

Named entities mapping against query logs

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Future work Large scale evaluation More evaluation data-sets Enrich learned ontologies from other resources

Other lexicons Existing ontology repositories SWOOGLE or WATSON

(ontology mapping) Existing web documents of on-line thesaurus (Wikipedia

and Wiktionary) applying hears patterns

Compute the union (or other kind of means) of learned items (concepts, properties, instances)

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Future work – Open issues Incorporate knowledge extracted from

The history of queries The Selected results

Extract knowledge from other social (semi-structured) data:Yahoo! AnswersFixya.com

Assign trust values to the learning objects

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References Kotis, K., A. Papasalouros, and M. Maragoudakis, "Mining

Query Logs for Learning Useful Ontologies: an Incentive to SW Content Creation", International Journal for Knowledge Engineering and Data Mining (IJKEDM), issue Special Issue on Incentives for Semantic Content Creation

Kotis, K., and A. Papasalouros, "Learning useful kick-off ontologies from Query Logs: HCOME revised", 4th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2010), Kracow, IEEE Computer Society Press, 2010.

Zavitsanos, E, Paliouras G, Vouros G, Petridis S.  2010.  Learning Subsumption Hierarchies of Ontology Concepts from Texts. Journal of Web Intelligence.

K. Kotis 43 - QueryOnto