Semantics & data mining & document processing

Nima KavianiSchool of Interactive Arts and Technology

Simon Fraser University - SURREY

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Towards Semantic Web Mining[5]

The idea is to combine two fast-developing research areas, Semantic Web, and Web Mining.

Semantic Web can be used to improve the results of web mining by exploiting new semantic structures in the web

Web mining is useful to enhance the concepts and instances by learning the definition of structures for knowledge organization and to provide the population of such knowledge organization

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Web Mining

Definition: the application of data mining techniques to the content, structure and usage of web resources

Web Content Mining: a form of text mining to extract data from content of the web page

Web Structure Mining: extracts information reside in the structure of hypertext (the idea behind links and also the usage for page rankings)

Web Usage Mining: the web resource that is being mined is the record of the requests made by the user to capture the user behaviors

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Semantic Web

Definition: to add semantic annotation to web documents so that they can be easily understand by human and read by machines for further inferences

Ontology Learning: semi-automatic extraction of semantics from the web to create an ontology.

Mapping and Merging Ontologies: to merge different ontologies and build a new domain specific ontology (described by Davis)

Instance Learning: automatic or semi-automatic methods to extract information from web-related documents, either to help in annotating new documents or to extract additional information from existing unstructured or partially structured documents.

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Creating an Ontology

Ontology is a conceptualization of domain into human understandable but machine readable formats. A quadruple of entities, attributes, relationships and axioms. [3]

Steps in creating an ontology for the data [8]: determining the scope of the ontology reusing existing Ontologies enumerating all the concepts needed defining the taxonomy defining the properties defining facets of the concepts defining instances

Are normallyPerformed by

OntologyEngineer

Can be performed

semi-automatically

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Ontology Learning

Why do we try to make the ontology learning automatic (semi-automatic)?

The source data is usually stored as unstructured, semi-structured (HTML, XML) or structured (Data Bases) format and should be processed in order to be used in creating the ontology[3].

Laborious and cumbersome task

Time consuming

Dynamic nature of available domains

Lack of tools and guidelines [1]

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Semi-Automatic Ontology Learning

It aims to integrate multitude of disciplines in order to facilitate the construction of Ontologies[12]. because of tacit information available, human intervention is always required [5].

Steps in Building an ontology automatically Acquisition of concepts Establishment of concept taxonomies Discovering of non-taxonomic conceptual relations Pruning the generated ontology

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Acquisition of concepts and establishing taxonomic relations

Using IR and NLP techniques, concepts can be extracted quite efficiently.

Techniques are normally a combination of methods below with a tendency to consider one of them more effectively. Computational Linguistic Information Retrieval

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Methods used in acquiring the concepts-1

Computational linguistic approach Pre-processing the text to extract dependencies and single-word nouns

POS-tagger [11] (part-of-speech dependency parser) [2, 4] Minipar (State-of-the-art dependency) [1]

Extracting multi-word noun phrases [2] Shallow parse the text Filter out word phrases with interesting POS-tag patterns Decide for each phrase whether it is a noun phrases

Extracting taxonomical relations[14] Uses regular expressions to find ISA relations Defining regular expression relations like:

NP {, NP}* {,} or other NP Bruises, wounds, broken bones, or other injuries

The overall process is a combination of the tools below[12] Tokenizer: Regular expressions to find nouns Lexicon: as a big repository for stems Lexical analyzer: mixes results from the two methods above and extracts new concepts Chunk parse: works on phrases to generate syntactic dependency relations-uses POS-tagger. Heuristics: includes correlations beside linguistic-base dependency relations.

more terms Low precision High recall (it’s more important in learning)

Regular expressions to extract taxonomies

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An information retrieval method using term weightings [12] Counting relevant terms and extract the more frequent ones as concepts

lefl,d: the frequency of appearance of term l in the document d dfl: the number of documents in the corpus D that term l occurs in cfl: the total number of occurrences of term l in the corpus D

Methods to find the taxonomy Clustering (starts from scratch and uses distributional data about words) Classification (uses an available hierarchy and refines it) Lexico-Syntactic (regular expressions)

Methods used in acquiring the concepts-2

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Methods used in acquiring the concepts-3

Combines information extraction with Ontologies and bootstraps[9]

ontology is used to improve the quality of extraction extracted information is used to improve the ontology the idea is to use indicative terms to find informative terms

and then to use informative terms to find new indicators it is trying to extract a pattern to make indicators and

informative concepts relevant

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Methods used in acquiring the concepts-4

Specific purpose concept and taxonomy extraction [7]

Methodology: neighborhood of initial keywords The anterior word of a word classifies it (in English) The posterior word of a word represents the domain (in English) coronary heart disease

Sends the query to the search engine and extracts anterior and posterior words of a word and decides on if the word is an instance or subclass.

Clustering is performed according to the coincidence amount

Synonymy is satisfied by using constraints and omitting the initial word

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Current Status

Results IR and Computational Linguistic can solve the problem Current methods are trying to derive concepts and form

taxonomical relations using the biggest available corpus, World Wide Web.

Problems to be solved Current efforts are mostly using hand-crafted concept

hierarchies Hardly can find synonyms for a set of available concepts. Hardly can make the process of discovering synonyms

automatic using currently found synonyms

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Establishment of non-taxonomic relations between concepts

The most important and challenging task in building an ontology.

Finding data concepts and taxonomic relations are simpler in comparison to construct non-taxonomic relation between concepts.

These approaches are generally a combination of Natural Language Processing and Machine Learning

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Methods proposed to establish relations-1

Clustering [13] ASIUM: a software designed based on unsupervised

clustering method Does not require any annotation of texts by hand Learns knowledge in the form of

Subcategorization frames <to travel> <subject: human> <by: vehicle> subject is the syntactic role by is the proposition human and vehicle are restrictions of their selection

Ontologies

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Methods proposed to establish relations-1

Pre-Processing the text SYLEX provides training text which is attachment of

verbs to noun phrases and clauses. The first step is done by getting the training text as

input and generating instantiated Subcategorization frames as output. <verb> <subject> <object>

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Methods proposed to establish relations-1

Clustering Algorithm Factorizing similar instantiated subcategorization frames Clustering algorithm used in ASIUM

Links represent generality relations Breadth-First Bottom-up clustering Two classes are aggregated Distance is defined as the portion of common head words in the

two clusters taking into account their frequencies Clusters with a distance less than the threshold are aggregated The threshold doesn’t change in different levels Available clusters, only in the same level, are taken into account

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Methods proposed to establish relations-1

card(c1) and card(c2): the number of different head words in cluster C1 and C2

Ncomm the number of different common head words between C1 and C2

is the sum of the frequencies of the head words of Cj wordiCj is the i-th head word of cluster Cj f(wordiCj) is its frequency

minimizes the influences of word frequencies

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Methods proposed to establish relations-1

This generality results in change of instantiated Subcategorization frames into Subcategorization frames

Cooperation of user in the process of building the ontology is required User labels the clusters User validates the new clusters

Rejects those words that restrict the given verbs Partitions new clusters into sub-clusters which would not have

been identified before Clusters in each level must get validated before proceeding to

the next level User can partition the clusters and label sub-concepts if he

find the newly generated classes useless or meaningless

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Example

drive travel

car motorbike

father neighbor father mother

by by

car train

using

bicycle

factorizingcar, bicycle car, train, motorbike

car, train, motorbike,bicycle

clusteringMotorized vehicle

Subjects

verbs

Objects

proposition

Passenger

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Generalized association rules [10] A set of transactions are defined Each transaction consists of a set of items where each item

is from a set of concepts Two factors are considered in estimating amount of relevancy of two

different concepts Xk and Yk in an association rule:

Support: percentage of transactions that contain Xk and Yk as a subset

Confidence: percentage of transactions that Yk is seen when Xk appears in a transaction

Some changes have been applied to the basic association rule algorithm to make it suitable for associations at the right level of the taxonomy

Methods proposed to establish relations-2

kk YX

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Methods proposed to establish relations-3

Fuzzy Formal Concept Analysis (FFCA)[3] FCA is based on lattice theory and is used for conceptual

knowledge discovery

Hierarchical relationship of concepts is organized as a lattice rather than a tree

The method uses a citation database to generate concepts

Steps in generating ontology using this method are: FFCA Concept Clustering Ontology generation

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Current Status

Results Methods proposed have reduced the amount of effort by a

human engineer

Problems to be solved They all consider a single-layer generalization, however, in

many case a multi-layer generalization would result in a better hierarchy

Still human plays a key role in designing the ontology and the quality of the design depends on his works

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Pruning the generated hierarchy

The generated ontology contains concepts that are not interesting and should be removed.

Methods used to remove uninteresting nodes are: Using a rule based method according to the following condition [6]

Nodes without a domain node are removed Intermediate nodes with the following properties are removed

Nodes without siblings It’s not the root of any concept Conditions which are held in the ontology

Using IR techniques[12] Considering term frequencies, comparing the frequency of the current term

with the frequency in a generic corpus, and removing the term if its frequency in the domain is lower than that of the term in a generic corpus

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Conclusion

A progress in building ontologies with web-pages rather than static texts as their instances is seen.

There is not a clear and defined way to evaluate automatically built ontologies and these ontologies are compared with hand-crafted ones.

The above fact hampers the comparison between two semi-automatically built ontologies

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References

1. Sabou, M., Wroe, C., Goble, C., and Mishne, G. Learning domain ontologies for Web service descriptions: an experiment in bioinformatics. In Proceedings of the 14th international Conference on World Wide Web (Chiba, Japan, May 10 - 14, 2005). WWW '05. ACM Press, New York, NY, 2005.

2. van Hage, W. R., de Rijke, M., Marx M., Information Retrieval Support for Ontology Construction and Use. In Proceedings of the 3rd International Semantic Web Conference, Jan 2004, Pages 518 – 533, LNCS, Springer 2004.

3. Quan, T. T. , Hu,i S. C., Fong, A.C.M., Cao, T. H. Automatic Generation of Ontology for Scholarly Semantic Web. In Proceedings of the 3rd International Semantic Web Conference, Jan 2004, Pages 726 – 740, LNCS, Springer 2004.

4. Sabou, M., Wroe, C., Goble, C., and Mishne, G. Learning domain ontologies for Web service descriptions: an experiment in bioinformatics. In Proceedings of the 14th international Conference on World Wide Web (Chiba, Japan, May 10 - 14, 2005). WWW '05. ACM Press, New York, NY, 2005.

5. Berendt, B., Hotho, A., and Stumme, G. Towards semantic web mining. In I. Horrocks and J. Hendler (Eds.), The Semantic Web - ISWC 2002. In Proceedings of the 1st International Semantic Web Conference, June 9-12th, 2002, Sardinia, Italy, pages 264--278. LNCS, Heidelberg, Germany: Springer, 2002.

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References

6. Navigli, R. and Velardi, P.: Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites. In Computational Linguistics, Volume 30, Issue 2. June 2004.

7. Sa’nchez, D. and Moreno, A. Web Mining Techniques for Automatic Discovery of Medical Knowledge. In Proceedings of Artificial Intelligence in Medicine, 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005.

8. Noy, N. F., and McGuinness, D. L. . Ontology Development 101: A Guide to Creating Your First Ontology. Knowledge Systems Laboratory, March, 2001.

9. Kavalec, M., Svatek, V. Information Extraction and Ontology Learning Guided by Web Directory. In ECAI Workshop on NLP and ML for ontology engineering, Lyon 2002.

10. Maedche, A. and Staab, S. 2000. Mining Ontologies from Text. In Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management. Pages 189-202, LNCS, vol. 1937. Springer, London, 2000.

11. Schmid, H. Probabilistic part-of-speech tagging using decision trees. In International Conference on New Methods in Language Processing, pages 44--49, Manchester, UK, 1994.

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References

12. Maedche, A. and Staab, S. 2001. Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16, 2, Mar. 2001.

13. Faure, D. and N'edellec, C. ASIUM: Learning subcategorization frames and restrictions of selection. In the 10th Conference on Machine Learning (ECML 98) -- Workshop on Text Mining, Chemnitz, Germany, April 1998.

14. Hearst, M. Automatic Acquisition of Hyponyms from Large Text Corpora. In Proceedings of the 14th International Conference on Computational Linguistics, Nantes, France, 1992.