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Tutorial on Text Mining and Link Analysis for Web and Semantic Web:

Tutorial on Text Mining and Link Analysis for Web and Semantic Web Marko Grobelnik, Dunja Mladenic Jozef Stefan Institute Ljubljana, Slovenia KDD 2007, San Jose CA, August 12th 2007 Tutorial web site: http://www.kdd2007.com/tutorials.html#tmal Slides: http://analytics.ijs.si/events/Tutorial-TextMiningLinkAnalysis-KDD2007-SanJose-Aug2007/ Examples: http://www.textmining.net/ (TextGarden software suite)

Outline:

Outline Text-Mining How to deal with text data on various levels? Link-Analysis How to analyze graphs in the Web context? Semantic-Web How semantics fits into the picture? Wrap-up …what did we learn and where to continue?

Text-Mining:

Text-Mining How to deal with text data on various levels?

Why do we analyze text?:

Why do we analyze text? The ultimate goal (or “the mother of all tasks”) is understanding of textual content… …but, since this seems to be too hard task, we have number of easier sub-tasks of some importance which we are able to deal with.

What is Text-Mining?:

What is Text-Mining? “…finding interesting regularities in large textual datasets…” (adapted from Usama Fayad) …where interesting means: non-trivial, hidden, previously unknown and potentially useful “…finding semantic and abstract information from the surface form of textual data…”

Why dealing with Text is Tough? (M.Hearst 97):

Why dealing with Text is Tough? (M.Hearst 97) Abstract concepts are difficult to represent “Countless” combinations of subtle, abstract relationships among concepts Many ways to represent similar concepts E.g. space ship, flying saucer, UFO Concepts are difficult to visualize High dimensionality Tens or hundreds of thousands of features

Why dealing with Text is Easy? (M.Hearst 97):

Why dealing with Text is Easy? (M.Hearst 97) Highly redundant data …most of the methods count on this property Just about any simple algorithm can get “good” results for simple tasks: Pull out “important” phrases Find “meaningfully” related words Create some sort of summary from documents

Who is in the text analysis arena?:

Who is in the text analysis arena? Data Analysis Computational Linguistics Search & DB Knowledge Rep. & Reasoning / Tagging

What dimensions are in text analytics?:

What dimensions are in text analytics? Three major dimensions of text analytics: Representations …from character-level to first-order theories Techniques …from manual work, over learning to reasoning Tasks …from search, over (un-, semi-) supervised learning, to visualization, summarization, translation …

How dimensions fit to research areas?:

How dimensions fit to research areas? Represent. Techniques Tasks NLP Inf. Retrieval ML/Text-Mining SW / Web2.0 Scientific work Politics Sharing of ideas, intuitions, methods and data

Broader context: Web Science:

Broader context: Web Science http://webscience.org/

Text-Mining How do we represent text?:

Text-Mining How do we represent text?

Levels of text representations:

Levels of text representations Character (character n-grams and sequences) Words (stop-words, stemming, lemmatization) Phrases (word n-grams, proximity features) Part-of-speech tags Taxonomies / thesauri Vector-space model Language models Full-parsing Cross-modality Collaborative tagging / Web2.0 Templates / Frames Ontologies / First order theories

Levels of text representations:

Levels of text representations Character Words Phrases Part-of-speech tags Taxonomies / thesauri Vector-space model Language models Full-parsing Cross-modality Collaborative tagging / Web2.0 Templates / Frames Ontologies / First order theories

Character level:

Character level Character level representation of a text consists from sequences of characters… …a document is represented by a frequency distribution of sequences Usually we deal with contiguous strings… …each character sequence of length 1, 2, 3, … represent a feature with its frequency

Good and bad sides:

Good and bad sides Representation has several important strengths: …it is very robust since avoids language morphology (useful for e.g. language identification) …it captures simple patterns on character level (useful for e.g. spam detection, copy detection) …because of redundancy in text data it could be used for many analytic tasks (learning, clustering, search) It is used as a basis for “string kernels” in combination with SVM for capturing complex character sequence patterns …for deeper semantic tasks, the representation is too weak

Levels of text representations:

Word level:

Word level The most common representation of text used for many techniques …there are many tokenization software packages which split text into the words Important to know: Word is well defined unit in western languages – e.g. Chinese has different notion of semantic unit

Words Properties:

Words Properties Relations among word surface forms and their senses: Homonomy: same form, but different meaning (e.g. bank: river bank, financial institution) Polysemy: same form, related meaning (e.g. bank: blood bank, financial institution) Synonymy: different form, same meaning (e.g. singer, vocalist) Hyponymy: one word denotes a subclass of an another (e.g. breakfast, meal) Word frequencies in texts have power distribution: …small number of very frequent words …big number of low frequency words

Stop-words:

Stop-words Stop-words are words that from non-linguistic view do not carry information …they have mainly functional role …usually we remove them to help the methods to perform better Stop words are language dependent – examples: English: A, ABOUT, ABOVE, ACROSS, AFTER, AGAIN, AGAINST, ALL, ALMOST, ALONE, ALONG, ALREADY, ... Dutch: de, en, van, ik, te, dat, die, in, een, hij, het, niet, zijn, is, was, op, aan, met, als, voor, had, er, maar, om, hem, dan, zou, of, wat, mijn, men, dit, zo, ... Slovenian: A, AH, AHA, ALI, AMPAK, BAJE, BODISI, BOJDA, BRŽKONE, BRŽČAS, BREZ, CELO, DA, DO, ...

Word character level normalization:

Word character level normalization Hassle which we usually avoid: Since we have plenty of character encodings in use, it is often nontrivial to identify a word and write it in unique form …e.g. in Unicode the same word could be written in many ways – canonization of words:

Stemming (1/2):

Stemming (1/2) Different forms of the same word are usually problematic for text data analysis, because they have different spelling and similar meaning (e.g. learns, learned, learning,…) Stemming is a process of transforming a word into its stem (normalized form) …stemming provides an inexpensive mechanism to merge

Stemming (2/2):

Stemming (2/2) For English is mostly used Porter stemmer at http://www.tartarus.org/~martin/PorterStemmer/ Example cascade rules used in English Porter stemmer ATIONAL -> ATE relational -> relate TIONAL -> TION conditional -> condition ENCI -> ENCE valenci -> valence ANCI -> ANCE hesitanci -> hesitance IZER -> IZE digitizer -> digitize ABLI -> ABLE conformabli -> conformable ALLI -> AL radicalli -> radical ENTLI -> ENT differentli -> different ELI -> E vileli -> vile OUSLI -> OUS analogousli -> analogous

Levels of text representations:

Phrase level:

Phrase level Instead of having just single words we can deal with phrases We use two types of phrases: Phrases as frequent contiguous word sequences Phrases as frequent non-contiguous word sequences …both types of phrases could be identified by simple dynamic programming algorithm The main effect of using phrases is to more precisely identify sense

Google n-gram corpus:

Google n-gram corpus In September 2006 Google announced availability of n-gram corpus: http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html#links Some statistics of the corpus: File sizes: approx. 24 GB compressed (gzip'ed) text files Number of tokens: 1,024,908,267,229 Number of sentences: 95,119,665,584 Number of unigrams: 13,588,391 Number of bigrams: 314,843,401 Number of trigrams: 977,069,902 Number of fourgrams: 1,313,818,354 Number of fivegrams: 1,176,470,663

Example: Google n-grams:

Example: Google n-grams ceramics collectables collectibles 55 ceramics collectables fine 130 ceramics collected by 52 ceramics collectible pottery 50 ceramics collectibles cooking 45 ceramics collection , 144 ceramics collection . 247 ceramics collection </S> 120 ceramics collection and 43 ceramics collection at 52 ceramics collection is 68 ceramics collection of 76 ceramics collection | 59 ceramics collections , 66 ceramics collections . 60 ceramics combined with 46 ceramics come from 69 ceramics comes from 660 ceramics community , 109 ceramics community . 212 ceramics community for 61 ceramics companies . 53 ceramics companies consultants 173 ceramics company ! 4432 ceramics company , 133 ceramics company . 92 ceramics company </S> 41 ceramics company facing 145 ceramics company in 181 ceramics company started 137 ceramics company that 87 ceramics component ( 76 ceramics composed of 85 serve as the incoming 92 serve as the incubator 99 serve as the independent 794 serve as the index 223 serve as the indication 72 serve as the indicator 120 serve as the indicators 45 serve as the indispensable 111 serve as the indispensible 40 serve as the individual 234 serve as the industrial 52 serve as the industry 607 serve as the info 42 serve as the informal 102 serve as the information 838 serve as the informational 41 serve as the infrastructure 500 serve as the initial 5331 serve as the initiating 125 serve as the initiation 63 serve as the initiator 81 serve as the injector 56 serve as the inlet 41 serve as the inner 87 serve as the input 1323 serve as the inputs 189 serve as the insertion 49 serve as the insourced 67 serve as the inspection 43 serve as the inspector 66 serve as the inspiration 1390 serve as the installation 136 serve as the institute 187

Levels of text representations:

Part-of-Speech level:

Part-of-Speech level By introducing part-of-speech tags we introduce word-types enabling to differentiate words functions For text-analysis part-of-speech information is used mainly for “information extraction” where we are interested in e.g. named entities which are “noun phrases” Another possible use is reduction of the vocabulary (features) …it is known that nouns carry most of the information in text documents Part-of-Speech taggers are usually learned by HMM algorithm on manually tagged data

Part-of-Speech Table:

Part-of-Speech Table http://www.englishclub.com/grammar/parts-of-speech_1.htm

Part-of-Speech examples:

Part-of-Speech examples http://www.englishclub.com/grammar/parts-of-speech_2.htm

Levels of text representations:

Taxonomies/thesaurus level:

Taxonomies/thesaurus level Thesaurus has a main function to connect different surface word forms with the same meaning into one sense (synonyms) …additionally we often use hypernym relation to relate general-to-specific word senses …by using synonyms and hypernym relation we compact the feature vectors The most commonly used general thesaurus is WordNet which exists in many other languages (e.g. EuroWordNet) http://www.illc.uva.nl/EuroWordNet/

WordNet – database of lexical relations:

WordNet – database of lexical relations WordNet is the most well developed and widely used lexical database for English …it consist from 4 databases (nouns, verbs, adjectives, and adverbs) Each database consists from sense entries – each sense consists from a set of synonyms, e.g.: musician, instrumentalist, player person, individual, someone life form, organism, being

WordNet – excerpt from the graph:

WordNet – excerpt from the graph sense sense relation 26 relations 116k senses

WordNet relations:

WordNet relations Each WordNet entry is connected with other entries in the graph through relations Relations in the database of nouns:

Levels of text representations:

Vector-space model level:

Vector-space model level The most common way to deal with documents is first to transform them into sparse numeric vectors and then deal with them with linear algebra operations …by this, we forget everything about the linguistic structure within the text …this is sometimes called “structural curse” because this way of forgetting about the structure doesn’t harm efficiency of solving many relevant problems This representation is referred to also as “Bag-Of-Words” or “Vector-Space-Model” Typical tasks on vector-space-model are classification, clustering, visualization etc.

Bag-of-words document representation:

Bag-of-words document representation

Word weighting:

Word weighting In the bag-of-words representation each word is represented as a separate variable having numeric weight (importance) The most popular weighting schema is normalized word frequency TFIDF: Tf(w) – term frequency (number of word occurrences in a document) Df(w) – document frequency (number of documents containing the word) N – number of all documents TfIdf(w) – relative importance of the word in the document The word is more important if it appears several times in a target document The word is more important if it appears in less documents

Example document and its vector representation:

Example document and its vector representation TRUMP MAKES BID FOR CONTROL OF RESORTS Casino owner and real estate Donald Trump has offered to acquire all Class B common shares of Resorts International Inc, a spokesman for Trump said. The estate of late Resorts chairman James M. Crosby owns 340,783 of the 752,297 Class B shares. Resorts also has about 6,432,000 Class A common shares outstanding. Each Class B share has 100 times the voting power of a Class A share, giving the Class B stock about 93 pct of Resorts' voting power. [RESORTS:0.624] [CLASS:0.487] [TRUMP:0.367] [VOTING:0.171] [ESTATE:0.166] [POWER:0.134] [CROSBY:0.134] [CASINO:0.119] [DEVELOPER:0.118] [SHARES:0.117] [OWNER:0.102] [DONALD:0.097] [COMMON:0.093] [GIVING:0.081] [OWNS:0.080] [MAKES:0.078] [TIMES:0.075] [SHARE:0.072] [JAMES:0.070] [REAL:0.068] [CONTROL:0.065] [ACQUIRE:0.064] [OFFERED:0.063] [BID:0.063] [LATE:0.062] [OUTSTANDING:0.056] [SPOKESMAN:0.049] [CHAIRMAN:0.049] [INTERNATIONAL:0.041] [STOCK:0.035] [YORK:0.035] [PCT:0.022] [MARCH:0.011] Original text Bag-of-Words representation (high dimensional sparse vector)

Similarity between document vectors:

Similarity between document vectors Each document is represented as a vector of weights D = <x> Cosine similarity (dot product) is the most widely used similarity measure between two document vectors …calculates cosine of the angle between document vectors …efficient to calculate (sum of products of intersecting words) …similarity value between 0 (different) and 1 (the same)

Levels of text representations:

Language model level:

Language model level Language modeling is about determining probability of a sequence of words The task typically gets reduced to the estimating probabilities of a next word given two previous words (trigram model): It has many applications including speech recognition, OCR, handwriting recognition, machine translation and spelling correction Frequencies of word sequences

Levels of text representations:

Full-parsing level:

Full-parsing level Parsing provides maximum structural information per sentence On the input we get a sentence, on the output we generate a parse tree For most of the methods dealing with the text data the information in parse trees is too complex

Levels of text representations:

Cross-modality level:

Cross-modality level It is very often the case that objects are represented with different data types: Text documents Multilingual texts documents Images Video Social networks Sensor networks …the question is how to create mappings between different representation so that we can benefit using more information about the same objects

Example: Aligning text with audio, images and video:

Example: Aligning text with audio, images and video The word “tie” has several representations (http://www.answers.com/tie&r=67) Textual Multilingual text (tie, kravata, krawatte, …) Audio Image: http://images.google.com/images?hl=en&q=necktie Video (movie on the right) Out of each representation we can get set of features and the idea is to correlate them KCCA (Kernel Correlation Analysis) method generates mappings between different representations into “modality neutral” data representation Visual word for the tie Basic image SIFT features (constituents for visual word)

Levels of text representations:

Collaborative tagging:

Collaborative tagging Collaborative tagging is a process of adding metadata to annotate content (e.g. documents, web sites, photos) …metadata is typically in the form of keywords …this is done in a collaborative way by many users from larger community collectively having good coverage of many topics …as a result we get annotated data where tags enable comparability of annotated data entries

Example: flickr.com tagging:

Example: flickr.com tagging Tags entered by users annotating photos

Example: del.icio.us tagging:

Example: del.icio.us tagging Tags entered by users annotating Web sites

Levels of text representations:

Template / frames level:

Template / frames level Templates are the mechanism for extracting the information from text …templates always focused on specific domain which includes consistent patterns on where specific information is positioned Templates are one of the basic methods for information extraction

Examples of templates of KnowItAll system:

Examples of templates of KnowItAll system Generic approach of extracting is described in Unsupervised named-entity extraction from the Web: An experimental study (Oren Etzioni et al) KnowItAll system uses the following generic templates: NP “and other” <class1> NP “or other” <class1> <class1> “especially” NPList <class1> “including” NPList <class1> “such as” NPList “such” <class1> “as” NPList NP “is a” <class1> NP “is the” <class1> …each template represents specific relationship between the words appearing in the variable slots From template patterns KnowItAll bootstraps new templates

Levels of text representations:

Ontologies level:

Ontologies level Ontologies are the most general formalism for describing data objects …in the recent years ontologies got popular through Semantic Web and OWL standard Ontologies can be of various complexity – from relatively simple ones (light weight described with simple) to heavy weight (described with first order theories. Ontologies could be understood also as very generic data-models where we can store extracted information from text

Example: text represented in the First Order Logic:

Text-Mining Typical tasks on text:

Text-Mining Typical tasks on text

Document Summarization:

Document Summarization

Document Summarization:

Document Summarization Task: the task is to produce shorter, summary version of an original document Two main approaches to the problem: Selection based – summary is selection of sentences from an original document Knowledge rich – performing semantic analysis, representing the meaning and generating the text satisfying length restriction

Selection based summarization:

Selection based summarization Three main phases: Analyzing the source text Determining its important points (units) Synthesizing an appropriate output Most methods adopt linear weighting model – each text unit (sentence) is assessed by the following formula: Weight(U) = LocationInText(U) + CuePhrase(U) + Statistics(U) + AdditionalPresence(U) …lot of heuristics and tuning of parameters (also with ML) …output consists from topmost text units (sentences)

Selection based summarization:

Selection based summarization Three main phases: Analyzing the source text Determining its important points (units) Synthesizing an appropriate output Most methods adopt linear weighting model – each text unit (sentence) is assessed by the following formula: Weight(U) = LocationInText(U) + CuePhrase(U) + Statistics(U) + AdditionalPresence(U) …lot of heuristics and tuning of parameters (also with Machine learning) …output consists from topmost text units (sentences)

Slide65:

Selected units Selection threshold Example of selection based approach from MS Word

Knowledge rich summarization:

Knowledge rich summarization To generate ‘true’ summary of a document we need to (at least partially) ‘understand’ the document text …the document is to small to count on statistics, we need to identify and use its linguistic and semantic structure On the next slides we show an approach from (Leskovec, Grobelnik, Milic-Frayling 2004) using 10 step procedure for extracting semantics from a document: …the approach was evaluated on “Document Understanding Conference” test set of documents and their summaries …the approach extracts semantic network from a document and tries to extract relevant part of the semantic network to represent summary Results achieved 70% recall of and 25% precision on extracted Subject-Predicate-Object triples

Knowledge Rich Summarization Example:

Knowledge Rich Summarization Example Input document is split into sentences Each sentence is deep-parsed Name-entities are disambiguated: Determining that ’George Bush’ == ‘Bush’ == ‘U.S. president’ Performing Anaphora resolution: Pronouns are connected with named-entities Extracting of Subject-Predicate-Object triples Constructing a graph from triples Each triple in the graph is described with features for learning Using machine learning train a model for classification of triples into the summary Generate a summary graph from selected triples From the summary graph generate textual summary document Tom went to town. In a bookstore he bought a large book. Tom went to town. In a bookstore he [Tom] bought a large book. Tom  go  town Tom  buy  book NLPWin WordNet

Training of summarization model:

Training of summarization model A model was trained deciding which Subject-Predicate-Object triple belongs into the target summary For training was used Support Vector Machine (SVM) on 400 statistic, linguistic and graph topological features Document Semantic network Summary semantic network

Example of summarization:

Cracks Appear in U.N. Trade Embargo Against Iraq. Cracks appeared Tuesday in the U.N. trade embargo against Iraq as Saddam Hussein sought to circumvent the economic noose around his country. Japan, meanwhile, announced it would increase its aid to countries hardest hit by enforcing the sanctions. Hoping to defuse criticism that it is not doing its share to oppose Baghdad, Japan said up to $2 billion in aid may be sent to nations most affected by the U.N. embargo on Iraq. President Bush on Tuesday night promised a joint session of Congress and a nationwide radio and television audience that ``Saddam Hussein will fail'' to make his conquest of Kuwait permanent. ``America must stand up to aggression, and we will,'' said Bush, who added that the U.S. military may remain in the Saudi Arabian desert indefinitely. ``I cannot predict just how long it will take to convince Iraq to withdraw from Kuwait,'' Bush said. More than 150,000 U.S. troops have been sent to the Persian Gulf region to deter a possible Iraqi invasion of Saudi Arabia. Bush's aides said the president would follow his address to Congress with a televised message for the Iraqi people, declaring the world is united against their government's invasion of Kuwait. Saddam had offered Bush time on Iraqi TV. The Philippines and Namibia, the first of the developing nations to respond to an offer Monday by Saddam of free oil _ in exchange for sending their own tankers to get it _ said no to the Iraqi leader. Saddam's offer was seen as a none-too-subtle attempt to bypass the U.N. embargo, in effect since four days after Iraq's Aug. 2 invasion of Kuwait, by getting poor countries to dock their tankers in Iraq. But according to a State Department survey, Cuba and Romania have struck oil deals with Iraq and companies elsewhere are trying to continue trade with Baghdad, all in defiance of U.N. sanctions. Romania denies the allegation. The report, made available to The Associated Press, said some Eastern European countries also are trying to maintain their military sales to Iraq. A well-informed source in Tehran told The Associated Press that Iran has agreed to an Iraqi request to exchange food and medicine for up to 200,000 barrels of refined oil a day and cash payments. There was no official comment from Tehran or Baghdad on the reported food-for-oil deal. But the source, who requested anonymity, said the deal was struck during Iraqi Foreign Minister Tariq Aziz's visit Sunday to Tehran, the first by a senior Iraqi official since the 1980-88 gulf war. After the visit, the two countries announced they would resume diplomatic relations. Well-informed oil industry sources in the region, contacted by The AP, said that although Iran is a major oil exporter itself, it currently has to import about 150,000 barrels of refined oil a day for domestic use because of damages to refineries in the gulf war. Along similar lines, ABC News reported that following Aziz's visit, Iraq is apparently prepared to give Iran all the oil it wants to make up for the damage Iraq inflicted on Iran during their conflict. Secretary of State James A. Baker III, meanwhile, met in Moscow with Soviet Foreign Minister Eduard Shevardnadze, two days after the U.S.-Soviet summit that produced a joint demand that Iraq withdraw from Kuwait. During the summit, Bush encouraged Mikhail Gorbachev to withdraw 190 Soviet military specialists from Iraq, where they remain to fulfill contracts. Shevardnadze told the Soviet parliament Tuesday the specialists had not reneged on those contracts for fear it would jeopardize the 5,800 Soviet citizens in Iraq. In his speech, Bush said his heart went out to the families of the hundreds of Americans held hostage by Iraq, but he declared, ``Our policy cannot change, and it will not change. America and the world will not be blackmailed.'' The president added: ``Vital issues of principle are at stake. Saddam Hussein is literally trying to wipe a country off the face of the Earth.'' In other developments: _A U.S. diplomat in Baghdad said Tuesday up to 800 Americans and Britons will fly out of Iraqi-occupied Kuwait this week, most of them women and children leaving their husbands behind. Saddam has said he is keeping foreign men as human shields against attack. On Monday, a planeload of 164 Westerners arrived in Baltimore from Iraq. Evacuees spoke of food shortages in Kuwait, nighttime gunfire and Iraqi roundups of young people suspected of involvement in the resistance. ``There is no law and order,'' said Thuraya, 19, who would not give her last name. ``A soldier can rape a father's daughter in front of him and he can't do anything about it.'' _The State Department said Iraq had told U.S. officials that American males residing in Iraq and Kuwait who were born in Arab countries will be allowed to leave. Iraq generally has not let American males leave. It was not known how many men the Iraqi move could affect. _A Pentagon spokesman said ``some increase in military activity'' had been detected inside Iraq near its borders with Turkey and Syria. He said there was little indication hostilities are imminent. Defense Secretary Dick Cheney said the cost of the U.S. military buildup in the Middle East was rising above the $1 billion-a-month estimate generally used by government officials. He said the total cost _ if no shooting war breaks out _ could total $15 billion in the next fiscal year beginning Oct. 1. Cheney promised disgruntled lawmakers ``a significant increase'' in help from Arab nations and other U.S. allies for Operation Desert Shield. Japan, which has been accused of responding too slowly to the crisis in the gulf, said Tuesday it may give $2 billion to Egypt, Jordan and Turkey, hit hardest by the U.N. prohibition on trade with Iraq. ``The pressure from abroad is getting so strong,'' said Hiroyasu Horio, an official with the Ministry of International Trade and Industry. Local news reports said the aid would be extended through the World Bank and International Monetary Fund, and $600 million would be sent as early as mid-September. On Friday, Treasury Secretary Nicholas Brady visited Tokyo on a world tour seeking $10.5 billion to help Egypt, Jordan and Turkey. Japan has already promised a $1 billion aid package for multinational peacekeeping forces in Saudi Arabia, including food, water, vehicles and prefabricated housing for non-military uses. But critics in the United States have said Japan should do more because its economy depends heavily on oil from the Middle East. Japan imports 99 percent of its oil. Japan's constitution bans the use of force in settling international disputes and Japanese law restricts the military to Japanese territory, except for ceremonial occasions. On Monday, Saddam offered developing nations free oil if they would send their tankers to pick it up. The first two countries to respond Tuesday _ the Philippines and Namibia _ said no. Manila said it had already fulfilled its oil requirements, and Namibia said it would not ``sell its sovereignty'' for Iraqi oil. Venezuelan President Carlos Andres Perez dismissed Saddam's offer of free oil as a ``propaganda ploy.'' Venezuela, an OPEC member, has led a drive among oil-producing nations to boost production to make up for the shortfall caused by the loss of Iraqi and Kuwaiti oil from the world market. Their oil makes up 20 percent of the world's oil reserves. Only Saudi Arabia has higher reserves. But according to the State Department, Cuba, which faces an oil deficit because of reduced Soviet deliveries, has received a shipment of Iraqi petroleum since U.N. sanctions were imposed five weeks ago. And Romania, it said, expects to receive oil indirectly from Iraq. Romania's ambassador to the United States, Virgil Constantinescu, denied that claim Tuesday, calling it ``absolutely false and without foundation.''. Example of summarization Cracks appeared in the U.N. trade embargo against Iraq. The State Department reports that Cuba and Romania have struck oil deals with Iraq as others attempt to trade with Baghdad in defiance of the sanctions. Iran has agreed to exchange food and medicine for Iraqi oil. Saddam has offered developing nations free oil if they send their tankers to pick it up. Thus far, none has accepted. Japan, accused of responding too slowly to the Gulf crisis, has promised $2 billion in aid to countries hit hardest by the Iraqi trade embargo. President Bush has promised that Saddam's aggression will not succeed. 7800 chars, 1300 words Human written summary

Slide70:

Automatically generated graph of summary triples

Text Segmentation:

Text Segmentation

Text Segmentation:

Text Segmentation Problem: divide text that has no given structure into segments with similar content Example applications: topic tracking in news (spoken news) identification of topics in large, unstructured text databases

Hearst Algorithm for Text Segmentation:

Hearst Algorithm for Text Segmentation Algorithm Initial segmentation Divide a text into equal blocks of k words Similarity Computation compute similarity between m blocks on the right and the left of the candidate boundary Boundary Detection place a boundary where similarity score reaches local minimum …the approach can be defined either as optimization problem or as sliding window

Supervised Learning:

Supervised Learning

Document Categorization Task:

Document Categorization Task Given: set of documents labeled with content categories The goal: to build a model which would automatically assign right content categories to new unlabeled documents. Content categories can be: unstructured (e.g., Reuters) or structured (e.g., Yahoo, DMoz, Medline)

Document categorization:

Document categorization labeled documents unlabeled document document category (label) ??? Machine learning

Algorithms for learning document classifiers:

Algorithms for learning document classifiers Popular algorithms for text categorization: Support Vector Machines Logistic Regression Perceptron algorithm Naive Bayesian classifier Winnow algorithm Nearest Neighbour ....

Example learning algorithm: Perceptron:

Example learning algorithm: Perceptron Input: set of documents D in the form of (e.g. TFIDF) numeric vectors each document has label +1 (positive class) or -1 (negative class) Output: linear model wi (one weight per word from the vocabulary) Algorithm: Initialize the model wi by setting word weights to 0 Iterate through documents N times For document d from D // Using current model wi classify the document d if sum(di *wi) >= 0 then classify document as positive else classify document as negative if document classification is wrong then // adjust weights of all words occurring in the document wt+1 = wt +sign(true-class) * Beta (input parameter Beta>0) // where sign(positive) = 1 and sign(negative) =-1

Measuring success – Model quality estimation :

Measuring success – Model quality estimation Classification accuracy Break-even point (precision=recall) F-measure (precision, recall) The truth, and ..the whole truth

Reuters dataset – Categorization to flat categories:

Reuters dataset – Categorization to flat categories Documents classified by editors into one or more categories Publicly available dataset of Reuters news mainly from 1987: 120 categories giving the document content, such as: earn, acquire, corn, rice, jobs, oilseeds, gold, coffee, housing, income,... …from 2000 is available new dataset of 830,000 Reuters documents available fo research

Distribution of documents (Reuters-21578):

Distribution of documents (Reuters-21578)

Example of Perceptron model for Reuters category “Acquisition”:

Example of Perceptron model for Reuters category “Acquisition” Feature Positive Class Weight ----------------------------- STAKE 11.5 MERGER 9.5 TAKEOVER 9 ACQUIRE 9 ACQUIRED 8 COMPLETES 7.5 OWNERSHIP 7.5 SALE 7.5 OWNERSHIP 7.5 BUYOUT 7 ACQUISITION 6.5 UNDISCLOSED 6.5 BUYS 6.5 ASSETS 6 BID 6 BP 6 DIVISION 5.5 …

SVM, Perceptron & Winnow text categorization performance on Reuters-21578 with different representations:

SVM, Perceptron & Winnow text categorization performance on Reuters-21578 with different representations

Text Categorization into hierarchy of categories:

Text Categorization into hierarchy of categories There are several hierarchies (taxonomies) of textual documents: Yahoo, DMoz, Medline, … Different people use different approaches: …series of hierarchically organized classifiers …set of independent classifiers just for leaves …set of independent classifiers for all nodes

Yahoo! hierarchy (taxonomy):

Yahoo! hierarchy (taxonomy) human constructed hierarchy of Web-documents exists in several languages (we use English) easy to access and regularly updated captures most of the Web topics English version includes over 2M pages categorized into 50,000 categories contains about 250Mb of HTML files

Slide86:

Document to categorize: CFP for CoNLL-2000

Slide87:

Some predicted categories

Slide88:

labeled documents (from Yahoo! hierarchy) Feature construction unlabeled document document category (label) ?? System architecture vectors of n-grams Document Classifier Subproblem definition Feature selection Classifier construction Web

Content categories:

Content categories For each content category generate a separate classifier that predicts probability for a new document to belong to its category

Considering promising categories only (classification by Naive Bayes):

Considering promising categories only (classification by Naive Bayes) Document is represented as a set of word sequences W Each classifier has two distributions: P(W|pos), P(W|neg) Promising category: calculated P(pos|Doc) is high meaning that the classifier has P(W|pos)>0 for at least some W from the document (otherwise, the prior probability is returned, P(neg) is about 0.90)

Summary of experimental results:

Summary of experimental results

Active Learning:

Active Learning

Active Learning:

Active Learning We use this methods whenever hand-labeled data are rare or expensive to obtain Interactive method Requests only labeling of “interesting” objects Much less human work needed for the same result compared to arbitrary labeling examples Teacher passive student Teacher active student Data & labels query label Passive student asking random questions Active student asking smart questions number of questions performance

Some approaches to Active Learning:

Some approaches to Active Learning Uncertainty sampling (efficient) select example closest to the decision hyperplane (or the one with classification probability closest to P=0.5) (Tong & Koller 2000 Stanford) Maximum margin ratio change select example with the largest predicted impact on the margin size if selected (Tong & Koller 2000 Stanford) Monte Carlo Estimation of Error Reduction select example that reinforces our current beliefs (Roy & McCallum 2001, CMU) Random sampling as baseline Experimental evaluation (using F1-measure) of the four listed approaches shown on three categories from Reuters-2000 dataset average over 10 random samples of 5000 training (out of 500k) and 10k testing (out of 300k) examples the last two methods are rather time consuming, thus we run them for including the first 50 unlabeled examples experiments show that active learning is especially useful for unbalanced data

Slide95:

Category with very unbalanced class distribution having 2.7% of positive examples Uncertainty seems to outperform MarginRatio

Illustration of Active learning:

Illustration of Active learning starting with one labeled example from each class (red and blue) select one example for labeling (green circle) request label and add re-generate the model using the extended labeled data Illustration of linear SVM model using arbitrary selection of unlabeled examples (random) active learning selecting the most uncertain examples (closest to the decision hyperplane)

Slide97:

Uncertainty sampling of unlabeled example

Unsupervised Learning:

Unsupervised Learning

Document Clustering:

Document Clustering Clustering is a process of finding natural groups in the data in a unsupervised way (no class labels are pre-assigned to documents) Key element is similarity measure In document clustering cosine similarity is most widely used Most popular clustering methods are: K-Means clustering (flat, hierarchical) Agglomerative hierarchical clustering EM (Gaussian Mixture) …

K-Means clustering algorithm:

K-Means clustering algorithm Given: set of documents (e.g. TFIDF vectors), distance measure (e.g. cosine) K (number of groups) For each of K groups initialize its centroid with a random document While not converging Each document is assigned to the nearest group (represented by its centroid) For each group calculate new centroid (group mass point, average document in the group)

Example of hierarchical clustering(bisecting k-means):

Example of hierarchical clustering (bisecting k-means) 3, 5, 8 0, 2, 4, 7, 10, 11 1, 6, 9 0, 2, 4, 7, 11 10 2, 4, 11 0, 7 0 7 2 4 2, 11 11 1, 9 9 6 1 3, 8 5 3 8 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 0, 1, 2, 4, 6, 7, 9, 10, 11

Latent Semantic Indexing:

Latent Semantic Indexing LSI is a statistical technique that attempts to estimate the hidden content structure within documents: …it uses linear algebra technique Singular-Value-Decomposition (SVD) …it discovers statistically most significant co-occurrences of terms

LSI Example:

LSI Example Correlation matrix Original document-term mantrix Rescaled document matrix, Reduced into two dimensions High correlation although d2 and d3 don’t share any word

Visualization:

Visualization

Why visualizing text?:

Why visualizing text? ...to have a top level view of the topics in the corpora ...to see relationships between the topics and objects in the corpora ...to understand better what’s going on in the corpora ...to show highly structured nature of textual contents in a simplified way ...to show main dimensions of highly dimensional space of textual documents ...because it’s fun!

Example: Visualization of PASCAL project research topics (based on published papers abstracts):

Example: Visualization of PASCAL project research topics (based on published papers abstracts) theory natural language processing kernel methods multimedia processing

…typical way of doing text visualization:

…typical way of doing text visualization By having text in the sparse vector Bag-of-Words representation we usually perform so kind of clustering algorithm identify structure which is then mapped into 2D or 3D space (e.g. using MDS) …other typical way of visualization of text is to find frequent co-occurrences of words and phrases which are visualized e.g. as graphs Typical visualization scenarios: Visualization of document collections Visualization of search results Visualization of document timeline

Graph based visualization:

Graph based visualization The sketch of the algorithm: Documents are transformed into the bag-of-words sparse-vectors representation Words in the vectors are weighted using TFIDF K-Means clustering algorithm splits the documents into K groups Each group consists from similar documents Documents are compared using cosine similarity K groups form a graph: Groups are nodes in graph; similar groups are linked Each group is represented by characteristic keywords Using simulated annealing draw a graph

Slide125:

Graph based visualization of 1700 IST project descriptions into 2 groups

Slide126:

Graph based visualization of 1700 IST project descriptions into 3 groups

Slide127:

Graph based visualization of 1700 IST project descriptions into 10 groups

Slide128:

Graph based visualization of 1700 IST project descriptions into 20 groups

Tiling based visualization:

Tiling based visualization The sketch of the algorithm: Documents are transformed into the bag-of-words sparse-vectors representation Words in the vectors are weighted using TFIDF Hierarchical top-down two-wise K-Means clustering algorithm builds a hierarchy of clusters The hierarchy is an artificial equivalent of hierarchical subject index (Yahoo like) The leaf nodes of the hierarchy (bottom level) are used to visualize the documents Each leaf is represented by characteristic keywords Each hierarchical binary split splits recursively the rectangular area into two sub-areas

Slide130:

Tiling based visualization of 1700 IST project descriptions into 2 groups

Slide131:

Tiling based visualization of 1700 IST project descriptions into 3 groups

Slide132:

Tiling based visualization of 1700 IST project descriptions into 4 groups

Slide133:

Tiling based visualization of 1700 IST project descriptions into 5 groups

Slide134:

Tiling visualization (up to 50 documents per group) of 1700 IST project descriptions (60 groups)

WebSOM:

WebSOM Self-Organizing Maps for Internet Exploration …algorithm that automatically organizes the documents onto a two-dimensional grid so that related documents appear close to each other … based on Kohonen’s Self-Organizing Maps Demo at http://websom.hut.fi/websom/

WebSOM visualization:

WebSOM visualization

ThemeScape:

ThemeScape Graphically displays images based on word similarities and themes in text Themes within the document spaces appear on the computer screen as a relief map of natural terrain The mountains in indicate where themes are dominant - valleys indicate weak themes Themes close in content will be close visually based on the many relationships within the text spaces Algorithm is based on K-means clustering http://www.pnl.gov/infoviz/technologies.html

ThemeScape Document visualization:

ThemeScape Document visualization

ThemeRiver topic stream visualization:

ThemeRiver topic stream visualization The ThemeRiver visualization helps users identify time-related patterns, trends, and relationships across a large collection of documents. The themes in the collection are represented by a "river" that flows left to right through time. The theme currents narrow or widen to indicate changes in individual theme strength at any point in time. http://www.pnl.gov/infoviz/technologies.html

Kartoo.com – visualization of search results:

Kartoo.com – visualization of search results http://kartoo.com/

SearchPoint – re-ranking of search results:

SearchPoint – re-ranking of search results

TextArc – visualization of word occurrences:

TextArc – visualization of word occurrences http://www.textarc.org/

NewsMap – visualization of news articles:

NewsMap – visualization of news articles http://www.marumushi.com/apps/newsmap/newsmap.cfm

Document Atlas – visualization of document collections and their structure:

Document Atlas – visualization of document collections and their structure http://docatlas.ijs.si

Information Extraction:

Information Extraction (slides borrowed from William Cohen’s Tutorial on IE)

Example: Extracting Job Openings from the Web :

Example: Extracting Job Openings from the Web

Slide147:

Example: IE from Research Papers

What is “Information Extraction”:

What is “Information Extraction” Filling slots in a database from sub-segments of text. As a task: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… NAME TITLE ORGANIZATION

What is “Information Extraction”:

What is “Information Extraction” Information Extraction = segmentation + classification + clustering + association As a family of techniques: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… Microsoft Corporation CEO Bill Gates Microsoft Gates Microsoft Bill Veghte Microsoft VP Richard Stallman founder Free Software Foundation aka “named entity extraction”

What is “Information Extraction”:

What is “Information Extraction” Information Extraction = segmentation + classification + association + clustering As a family of techniques: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… Microsoft Corporation CEO Bill Gates Microsoft Gates Microsoft Bill Veghte Microsoft VP Richard Stallman founder Free Software Foundation

What is “Information Extraction”:

IE in Context:

IE in Context Create ontology Segment Classify, Associate Cluster Load DB Spider Query, Search Data mine IE Document collection Database Filter by relevance Label training data Train extraction models

Typical approaches to IE:

Typical approaches to IE Hand-built rules/models for extraction …usually extended regexp rules …GATE system from U. Sheffield (http://gate.ac.uk/) Machine learning used on manually labelled data: Classification problem on sliding window …examples are taken from sliding window …models classify short segments of text such as title, name, institution, … …limitation of sliding window because it does not take into account sequential nature of text Training stochastic finite state machines (e.g. HMM) …probabilistic reconstruction of parsing sequence

Link-Analysis:

Link-Analysis How to analyze graphs in the Web context?

What is Link Analysis?:

What is Link Analysis? Link Analysis is exploring associations between the objects …most characteristic for the area is graph representation of the data Category of graphs which attract recently the most interest are the ones which are generated by some social process (social networks) – this would include web Synonyms for Link Analysis or at least very related areas are Graph Mining, Network Analysis, Social Network Analysis In the next slides we’ll present some of the typical definitions, ideas and algorithms

What is Power Law?:

What is Power Law? Power law describes relations between the objects in the network …it is very characteristic for the networks generated within some kind of social process …it describes scale invariance found in many natural phenomena (including physics, biology, sociology, economy and linguistics) In Link Analysis we usually deal with power law distributed graphs

Power-Law on the Web:

Power-Law on the Web In the context of Web the power-law appears in many cases: Web pages sizes Web page connectivity Web connected components’ size Web page access statistics Web Browsing behavior Formally, power law describing web page degrees are: (This property has been preserved as the Web has grown)

Small World Networks:

Small World Networks Empirical observation for the Web-Graph is that the diameter of the Web-Graph is small relative to the size of the network …this property is called “Small World” …formally, small-world networks have diameter exponentially smaller then the size By simulation it was shown that for the Web-size of 1B pages the diameter is approx. 19 steps …empirical studies confirmed the findings

Example of Small World: project collaboration network:

Example of Small World: project collaboration network The network represents collaboration between institutions on projects funded by European Union …there are 7886 organizations collaborating on 2786 projects …in the network, each node is an organization, two organizations are connected if they collaborate on at least one project Small world properties of the collaboration network: Main connected part of the network contains 94% of the nodes Max distance between any two organizations is 7 steps … meaning that any organization can be reached in up to 7 steps from any other organization Average distance between any two organizations is 3.15 steps (with standard deviation 0.38) 38% (2770) of organizations have avg. distance 3 or less

Connectedness of the most connected institution:

1856 collaborations avg. distance is 1.95 max. distance is 4 Connectedness of the most connected institution

Connectedness of semi connected institution:

179 collaborations avg. distance is 2.42 max. distance is 4 Connectedness of semi connected institution

Slide166:

Connectedness of min. connected institution 8 collaborations max. distance is 7

Structure of the Web – “Bow Tie” model:

Structure of the Web – “Bow Tie” model In November 1999 large scale study using AltaVista crawls in the size of over 200M nodes and 1.5B links reported “bow tie” structure of web links …we suspect, because of the scale free nature of the Web, this structure is still preserved

Slide168:

SCC - Strongly Connected component where pages can reach each other via directed paths IN – consisting from pages that can reach core via directed path, but cannot be reached from the core OUT – consisting from pages that can be reached from the core via directed path, but cannot reach core in a similar way TENDRILS – disconnected components reachable only via directed path from IN and OUT but not from and to core TENDRILS – disconnected components reachable only via directed path from IN and OUT but not from and to core

Modeling the Web Growth:

Modeling the Web Growth Links/Edges in the Web-Graph are not created at random …probability that a new page gets attached to one of the more popular pages is higher then to a one of the less popular pages Intuition: “rich gets richer” or “winners takes all” Simple algorithm “Preferential Attachment Model” (Barabasi, Albert) efficiently simulates Web-Growth

“Preferential Attachment Model” Algorithm:

“Preferential Attachment Model” Algorithm M0 vertices (pages) at time 0 At each time step new vertex (page) is generated with m≤ M0 edges to m random vertices …probability for selection a vertex for the edge is proportional to its degree …after t time steps, the network has M0+t vertices (pages) and mt edges …probability that a vertex has connectivity k follows the power-law

Estimating importance of the web pages:

Estimating importance of the web pages Two main approaches, both based on eigenvector decomposition of the graph adjacency matrix Hubs and Authorities (HITS) PageRank – used by Google

Hubs and Authorities:

Hubs and Authorities Intuition behind HITS is that each web page has two natures: …being good content page (authority weight) …being good hub (hub weight) …and the idea behind the algorithm: …good authority page is pointed to by good hub pages …good hub page is pointing to good authority pages

Hubs and Authorities(Kleinberg 1998):

“Hubs and authorities exhibit what could be called a mutually reinforcing relationship” Iterative relaxation: Hubs Authorities Hubs and Authorities (Kleinberg 1998)

PageRank:

PageRank PageRank was developed by the founders of the Google in 1998 …its basic intuition is to calculate primal eigenvector of the graph adjacency matrix …each page gets a value which corresponds to the importance of the node within the network PageRank can be computed effectively by an iterative procedure

Example Calculation:

Example Calculation http://iprcom.com/papers/pagerank/

Semantic-Web:

Semantic-Web How semantics fits into the picture?

What is Semantic Web? (informal):

What is Semantic Web? (informal) Informal statements: “…if the ordinary web is mainly for computer-to-human communication, then the semantic web aims primarily at computer-to-computer communication The idea is to establish infrastructure for dealing with common vocabularies The goal is to overcome surface syntax representation of the data and deal with the “semantics” of the data …as an example, one should be able to make a “semantic link” from a database column with the name “ZIP-Code” and a GUI form with a “ZIP” field since they actually mean the same – they both describe the same abstract concept Semantic Web is mainly about integration and standards!

What is Semantic Web? (formal):

What is Semantic Web? (formal) Formal statement (from http://www.w3.org/2001/sw/): “The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.” “It is a collaborative effort led by W3C with participation from a large number of researchers and industrial partners.”

What is the link between Text-Mining, Link Analysis and Semantic Web?:

What is the link between Text-Mining, Link Analysis and Semantic Web? Text-Mining, Link-Analysis and other analytic techniques deal mainly with extracting and aggregating the information from raw data …they maximize the quality of extracted information Semantic Web, on the other hand, deals mainly with the integration and representation of the given data …it maximizes reusability of the given information Both areas are very much complementary and necessary for operational information engineering

Semantic Web Ontologies (formalization of semantics):

Semantic Web Ontologies (formalization of semantics)

Ontologies – central objects in SW:

Ontologies – central objects in SW Ontologies are central formal objects within Semantic Web Ontologies have origin in philosophy, but within computer science they represent a data model that represents a domain and is used to reason about the objects in that domain and the relations between them …their main aim is to describe and represent an area of knowledge in a formal way Most of the Semantic Web standards/languages (XML, RDF, OWL) are concerned with some level of ontological representation of the knowledge

What is an ontology?:

What is an ontology? concepts, properties, relations, functions machine processable Consensual knowledge Abstract model of some domain Formal, explicit specification, of a shared conceptualisation. Frank.van Harmelen 2003: http://seminars.ijs.si/sekt

Which elements represent an ontology?:

Which elements represent an ontology? An ontology typically consists of the following elements: Instances – the basic or “ground level” objects Classes – sets, collections, or types of objects Attributes – properties, features, characteristics, or parameters that objects can have and share Relations – ways that objects can be related to one another Analogies between ontologies and relational databases: Instances correspond to records Classes correspond to tables Attributes correspond to record fields Relations correspond to relations between the tables

Semantic Web Semantic Web Languages (XML, RDF, OWL):

Semantic Web Semantic Web Languages (XML, RDF, OWL)

Which levels Semantic Web is dealing with?:

Which levels Semantic Web is dealing with? The famous “Semantic Web Layer Cake” shows representation levels and related technologies Addressing the information Character Level Encoding Different Levels of Semantic Abstraction Infrastructure Higher level of representation and reasoning

Stack of Semantic Web Languages:

Stack of Semantic Web Languages XML (eXtended Markup Language) Surface syntax, no semantics XML Schema Describes structure of XML documents RDF (Resource Description Framework) Datamodel for “relations” between “things” RDF Schema RDF Vocabulary Definition Language OWL (Web Ontology Language) A more expressive Vocabulary Definition Language Frank.van Harmelen 2003: http://seminars.ijs.si/sekt

Bluffer’s guide to RDF (1/2):

Bluffer’s guide to RDF (1/2) Object ->Attribute-> Value triples objects are web-resources Value is again an Object: triples can be linked data-model = graph Frank.van Harmelen 2003: http://seminars.ijs.si/sekt

Bluffer’s guide to RDF (2/2):

Bluffer’s guide to RDF (2/2) Every identifier is a URL = world-wide unique naming! Has XML syntax Any statement can be an object …graphs can be nested Frank.van Harmelen 2003: http://seminars.ijs.si/sekt

OWL – Web Ontology Language:

OWL – Web Ontology Language The OWL (Web Ontology Language) is a markup language for publishing and sharing data using ontologies on the Web …it is standardized at W3C (http://www.w3.org/TR/owl-guide/) One of the main goals was to achieve high machine interpretability …and higher expresivity then languages such as RDF and RDF Schema

OWL Layers:

OWL Layers OWL Lite: Classification hierarchy Simple constraints OWL DL: Maximal expressiveness While maintaining tractability Standard formalisation OWL Full: Very high expressiveness Loosing tractability Non-standard formalisation All syntactic freedom of RDF (self-modifying) Full DL Lite Frank.van Harmelen 2003: http://seminars.ijs.si/sekt

Semantic Web OntoGen system (example of ontology learning):

Semantic Web OntoGen system (example of ontology learning)

Ontology learning:

Ontology learning Ontology learning task aims at extracting structure in the given data and save the structure in the form of an ontology Two systems for ontology learning from documents: OntoGen (http://ontogen.ijs.si) …extracts the structure by using machine learning techniques (clustering, active learning, visualization, …) Text2Onto (http://ontoware.org/projects/text2onto/) …extracts the structure from text by using linguistic patterns

OntoGen – main scenarios using :

OntoGen – main scenarios using Given a corpus of documents a user can interactively… …construct new classes by …clustering of documents into topics and subtopics …active learning when user wants to extract structure …selecting data on visualized map of documents …mapping proposed concepts to existing ontologies …populate new documents into an ontology by …by categorization of documents into hierarchy …summarize ontology by …keyword extraction techniques …visualization of the structure …save constructed ontology as Semantic Web formalism (RDF, OWL, Prolog) statistical model

OntoGen – main scenario:

OntoGen – main scenario Given a text corpus, construct semi-automatically a taxonomic ontology where each of the documents belongs to a certain class Domain Text corpus Ontology Concept A Concept B Concept C 194 Blaz Fortuna et al, HCII2007

OntoGen – main screen:

OntoGen – main screen Concept hierarchy List of suggested sub-concepts Ontology visualization Selected concept 195 Blaz Fortuna et al, HCII2007

Ontology construction from content visualization:

Ontology construction from content visualization 196 Blaz Fortuna et al, HCII2007

Semantic Web Cyc system (example of deep reasoning):

Semantic Web Cyc system (example of deep reasoning)

Cyc …a little bit of historical context:

Cyc …a little bit of historical context Older AI-ers know about Cyc: …one of the boldest attempts in AI history to encode common sense knowledge in one KB The project started in 1984 at Stanford as US response to Japan’s project on “5th Generation Computer Systems” In 1994 the company Cycorp was established (in Austin, TX) In 2005 Cyc KB gets opened and available for research OpenCyc (http://www.opencyc.org/) ResearchCyc (http://research.cyc.com/) In 2006 Cyc-Europe was established (in Ljubljana, Slovenia) Till 2006 ~$80M was spent into the KB

The Cyc Ontology:

…part of Cyc Ontology on Human Beings:

…part of Cyc Ontology on Human Beings

Structure of Cyc Ontology:

Structure of Cyc Ontology Upper Ontology Core Theories Domain-Specific Theories Upper Ontology: Abstract Concepts Core Theories: Space, Time, Causality, … Domain-Specific Theories Facts: Instances Knowledge Base Layers

Structure of Cyc Ontology:

Structure of Cyc Ontology Upper Ontology Core Theories Domain-Specific Theories EVENT  TEMPORAL-THING  INDIVIDUAL  THING Upper Ontology: Abstract Concepts Knowledge Base Layers

Structure of Cyc Ontology:

Structure of Cyc Ontology Upper Ontology Core Theories Domain-Specific Theories EVENT  TEMPORAL-THING  INDIVIDUAL  THING For all events a and b, a causes b implies a precedes b For any mammal m and any anthrax bacteria a, m’s being exposed to a causes m to be infected by a. Upper Ontology: Abstract Concepts Core Theories: Space, Time, Causality, … Domain-Specific Theories Knowledge Base Layers

Structure of Cyc Ontology:

EVENT  TEMPORAL-THING  INDIVIDUAL  THING For all events a and b, a causes b implies a precedes b For any mammal m and any anthrax bacteria a, m’s being exposed to a causes m to be infected by a. John is a person infected by anthrax. Upper Ontology: Abstract Concepts Core Theories: Space, Time, Causality, … Domain-Specific Theories Facts: Instances Knowledge Base Layers Structure of Cyc Ontology

Cyc KB Extended w/Domain Knowledge:

An example of Psychoanalyst’s Cyc taxonomic context:

An example of Psychoanalyst’s Cyc taxonomic context #$Psychoanalyst (lexical representation: “psychoanalyst”, “psychoanalysts”) specialization-of #$MedicalCareProfessional | specialization-of #$HealthProfessional | specialization-of #$Professional-Adult | specialization-of #$Professional specialization-of #$Psychologist | specialization-of #$Scientist | specialization-of #$Researcher | | specialization-of #$PersonWithOccupation | | | specialization-of #$Person | | | | specialization-of #$HomoSapiens | | | | | instance-of #$BiologicalSpecies | | | | | | specialization-of #$BiologicalTaxon | | | | | instance-of #$SomeSampleKindsOfMammal-Biology-Topic | | specialization-of #$AdultAnimal | | | specialization-of #$Animal | | | | specialization-of #$SolidTangibleThing | | | | instance-of #$StatesOfMatter-Material-Topic | specialization-of (#$GraduateFn #$University) | specialization-of (#$Graduate #$DegreeGrantingHigherEducationInstitution) specialization-of #$Counselor-Psychological

Example Vocabulary: Senses of ‘In’ relation (1/3):

Example Vocabulary: Senses of ‘In’ relation (2/3):

Cycorp © 2006 Example Vocabulary: Senses of ‘In’ relation (2/3) Does part of the inner object stick out of the container? None of it. -- Try #$in-ContCompletely Yes -- Try #$in-ContPartially No -- Try #$in-ContClosed If the container were turned around could the contained object fall out? Yes -- Try #$in-ContOpen

Example Vocabulary: Senses of ‘In’ relation (3/3):

Slide212:

Text query Query (semi) automatically translated in the First Order Logic Answers to the query Cyc’s front-end: “Cyc Analytic Environment” – querying (1/2)

Slide213:

Query & Answer Justification Sources for Reasoning and Justification Cyc’s front-end: “Cyc Analytic Environment” – justification (2/2)

Semantic Web Web X.X versions (past and current trends):

Semantic Web Web X.X versions (past and current trends)

The beautiful world of Web X.X versions(…a trial to put all of them on one slide):

The beautiful world of Web X.X versions (…a trial to put all of them on one slide)

Web 2.0 –is there any new quality?:

Web 2.0 –is there any new quality? With “Web 2.0” the Web community became really aware of the importance of the global collaborative work …next step in the globalization of the Web Bottom-up “social networking” seems to nicely complement the traditional top-down schema design approaches Visualization of Web 2.0 typical vocabulary (http://en.wikipedia.org/wiki/Image:Web20_en.png)

Web 2.0 – the current hype!:

Web 2.0 – the current hype! Google search volume of “data mining” vs. “Web 2.0” vs. “semantic web” (http://www.google.com/trends?q=data+mining%2C+semantic+web%2C+web+2.0)

What about Web 4.0? :

What about Web 4.0?  Citation from some blog: “…Web 4.0 is the impending state at which all information converges into a great ball of benevolent self-aware light, and solves every problem from world peace to …” http://blogs.intel.com/it/2006/11/web_40_a_new_hype.html Ultimate stage in web development… …will prevent Web 5.0 to happen since everything will be resolved already by Web 4.0.

Wrap-up:

Wrap-up …what did we learn and where to continue?

References to some Text-Mining & Link Analysis Books:

References to some Text-Mining & Link Analysis Books

References to some Semantic Web Books:

References to some Semantic Web Books

References to the main conferences:

References to the main conferences Information Retrieval: SIGIR, ECIR Machine Learning/Data Mining: ICML, ECML/PKDD, KDD, ICDM, SDM Computational Linguistics: ACL, EACL, NAACL Semantic Web: ISWC, ESWS

References to some of the Text-Mining & Link Analysis workshops at KDD, ICDM, ICML and IJCAI conferences (available online):

References to some of the Text-Mining & Link Analysis workshops at KDD, ICDM, ICML and IJCAI conferences (available online) ICML-1999 Workshop on Machine Learning in Text Data Analysis (TextML-1999) (http://www-ai.ijs.si/DunjaMladenic/ICML99/TLWsh99.html), Bled 1999 KDD-2000 Workshop on Text Mining (TextKDD-2000) (http://www.cs.cmu.edu/~dunja/WshKDD2000.html), Boston 2000 ICDM-2001 Workshop on Text Mining (TextKDD-2001) (http://www-ai.ijs.si/DunjaMladenic/TextDM01/), San Jose 2001 ICML-2002 Workshop on Text Learning (TextML-2002) (http://www-ai.ijs.si/DunjaMladenic/TextML02/), Sydney 2002 IJCAI-2003 Workshop on Text-Mining and Link-Analysis (TextLink-2003) (http://www.cs.cmu.edu/~dunja/TextLink2003/), Acapulco 2003 KDD-2003 Workshop on Workshop on Link Analysis for Detecting Complex Behavior (LinkKDD2003) (http://www.cs.cmu.edu/~dunja/LinkKDD2003/), Washington DC 2003 KDD-2004 Workshop on Workshop on Link Analysis and Group Detection (LinkKDD2004) (http://www.cs.cmu.edu/~dunja/LinkKDD2004/), Seattle 2004 KDD-2005 Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD-2005) (http://www.isi.edu/LinkKDD-05/), Chicago 2005 KDD-2006 Workshop on Link Analysis: Dynamics and Statics of Large Networks (LinkKDD 2006) (http://kt.ijs.si/Dunja/LinkKDD2006/), Philadelphia 2006 IJCAI-2007 Workshop on Text-Mining & Link-Analysis (TextLink 2007) (http://kt.ijs.si/dunja/textlink2007/), Hyderabad 2007

References to video content:

References to video content Many scientific events are recorded and freely available from http://videolectures.net/ …videos categorized by a subject http://videolectures.net/Top/Computer_Science/

Some of the Products:

Some of the Products Authonomy ClearForest Megaputer SAS – Enterprise-Miner SPSS – Clementine, LexiQuest Oracle – ConText IBM - Intelligent Miner for Text, UIMA Microsoft – SQL Server

Major Databases & Text-Mining:

Major Databases & Text-Mining Oracle – includes some functionality within the database engine (e.g. classification with SVM, clustering, …) IBM DB2 – text mining appears as a database extender accessible through several SQL functions …a lot of functionality is included in WebFountain and UIMA environments Microsoft SQL Server – text processing is available as a preprocessing stage in Data-Transformation Services module

Final Remarks:

Final Remarks In the future we can expect stronger integration and bigger overlap between Text-Mining, Information-Retrieval, Natural-Language-Processing and Semantic-Web… …the technology and solutions will try to capture deeper semantics within the text …integration of various data sources (where text and graphs are just two of the modalities) is becoming increasingly important.