1. Semantic Search Spring 2007 Computer Engineering Department Sharif University of Technology

2. Semantic web - Computer Engineering Dept. - Spring 2007 2 Outline Traditional search concepts Semantic Search

3. Semantic web - Computer Engineering Dept. - Spring 2007 3 Traditional search Originated from Information Retrieval research Enhanced for the Web –Crawling and indexing –Web specific ranking An information need is represented by a set of keywords –Very simple interface –Users does not have to be experts Similarity of each document in the collection with the query is estimated A ranking is applied on the results to sort out the results and show them to the users

4. Semantic web - Computer Engineering Dept. - Spring 2007 4 Representation of documents structure Accents spacing stopwords Noun groups stemming Manual indexing Docs structure Full text Index terms

5. Semantic web - Computer Engineering Dept. - Spring 2007 5 Retrieval process User Interface Text Operations Query Operations Indexing Searching Ranking Index Text query user need user feedback ranked docs retrieved docs logical view inverted file DB Manager Module Text Database Text

6. Semantic web - Computer Engineering Dept. - Spring 2007 6 Indexing Tokenizer Token stream. Friends RomansCountrymen Linguistic modules Modified tokens. friend romancountryman Indexer Inverted index. friend roman countryman 24 2 13 16 1 Documents to be indexed. Friends, Romans, countrymen.

7. Semantic web - Computer Engineering Dept. - Spring 2007 7 Retrieval models A retrieval model specifies how the similarity of a document to a query is estimated. Three basic retrieval models: –Boolean model –Vector model –Probabilistic model

8. Semantic web - Computer Engineering Dept. - Spring 2007 8 Boolean model Query is specified using logical operators: AND, OR and NOT Merge of the posting lists is the basic operation Consider processing the query: Brutus AND Caesar –Locate Brutus in the Dictionary; Retrieve its postings. –Locate Caesar in the Dictionary; Retrieve its postings. –“Merge” the two postings: 128 34 248163264123581321 Brutus Caesar

9. Semantic web - Computer Engineering Dept. - Spring 2007 9 Boolean queries: Exact match The Boolean Retrieval model is being able to ask a query that is a Boolean expression: –Boolean Queries are queries using AND, OR and NOT to join query terms Views each document as a set of words Is precise: document matches condition or not. Primary commercial retrieval tool for 3 decades. Professional searchers (e.g., lawyers) still like Boolean queries: –You know exactly what you’re getting.

10. Semantic web - Computer Engineering Dept. - Spring 2007 10 Example: WestLaw http://www.westlaw.com/ Largest commercial (paying subscribers) legal search service (started 1975; ranking added 1992) Tens of terabytes of data; 700,000 users Majority of users still use boolean queries Example query: –What is the statute of limitations in cases involving the federal tort claims act? –LIMIT! /3 STATUTE ACTION /S FEDERAL /2 TORT /3 CLAIM /3 = within 3 words, /S = in same sentence

11. Semantic web - Computer Engineering Dept. - Spring 2007 11 Ranking search results Boolean queries give inclusion or exclusion of docs. Often we want to rank/group results –Need to measure proximity from query to each doc. –Need to decide whether docs presented to user are singletons, or a group of docs covering various aspects of the query.

12. Semantic web - Computer Engineering Dept. - Spring 2007 12 Spell correction Two principal uses –Correcting document(s) being indexed –Retrieve matching documents when query contains a spelling error Two main flavors: –Isolated word Check each word on its own for misspelling Will not catch typos resulting in correctly spelled words e.g., from  form –Context-sensitive Look at surrounding words, e.g., I flew form Heathrow to Narita.

13. Semantic web - Computer Engineering Dept. - Spring 2007 13 Isolated word correction Fundamental premise – there is a lexicon from which the correct spellings come Two basic choices for this –A standard lexicon such as Webster’s English Dictionary An “industry-specific” lexicon – hand-maintained –The lexicon of the indexed corpus E.g., all words on the web All names, acronyms etc. (Including the mis-spellings)

14. Semantic web - Computer Engineering Dept. - Spring 2007 14 Isolated word correction Given a lexicon and a character sequence Q, return the words in the lexicon closest to Q What’s “closest”? We have several alternatives –Edit distance –Weighted edit distance –n-gram overlap

15. Semantic web - Computer Engineering Dept. - Spring 2007 15 Edit distance Given two strings S 1 and S 2, the minimum number of basic operations to covert one to the other Basic operations are typically character-level –Insert –Delete –Replace E.g., the edit distance from cat to dog is 3. Generally found by dynamic programming.

16. Semantic web - Computer Engineering Dept. - Spring 2007 16 n-gram overlap Enumerate all the n-grams in the query string as well as in the lexicon Use the n-gram index (recall wild-card search) to retrieve all lexicon terms matching any of the query n-grams Threshold by number of matching n-grams

17. Semantic web - Computer Engineering Dept. - Spring 2007 17 Example with trigrams Suppose the text is november –Trigrams are nov, ove, vem, emb, mbe, ber. The query is december –Trigrams are dec, ece, cem, emb, mbe, ber. So 3 trigrams overlap (of 6 in each term) How can we turn this into a normalized measure of overlap?

18. Semantic web - Computer Engineering Dept. - Spring 2007 18 One option – Jaccard coefficient A commonly-used measure of overlap Let X and Y be two sets; then the J.C. is Equals 1 when X and Y have the same elements and zero when they are disjoint X and Y don’t have to be of the same size Always assigns a number between 0 and 1 –Now threshold to decide if you have a match –E.g., if J.C. > 0.8, declare a match

19. Semantic web - Computer Engineering Dept. - Spring 2007 19 Phrase queries Want to answer queries such as “stanford university” – as a phrase Thus the sentence “I went to university at Stanford” is not a match. –The concept of phrase queries has proven easily understood by users; about 10% of web queries are phrase queries No longer suffices to store only entries

20. Semantic web - Computer Engineering Dept. - Spring 2007 20 Biword indexes Index every consecutive pair of terms in the text as a phrase For example the text “Friends, Romans, Countrymen” would generate the biwords –friends romans –romans countrymen Each of these biwords is now a dictionary term Two-word phrase query-processing is now immediate.

21. Semantic web - Computer Engineering Dept. - Spring 2007 21 Longer phrase queries stanford university palo alto can be broken into the Boolean query on biwords: stanford university AND university palo AND palo alto Without the docs, we cannot verify that the docs matching the above Boolean query do contain the phrase. Can have false positives!

22. Semantic web - Computer Engineering Dept. - Spring 2007 22 Solution 2: Positional indexes Store, for each term, entries of the form: <number of docs containing term; doc1: position1, position2 … ; doc2: position1, position2 … ; etc.>

23. Semantic web - Computer Engineering Dept. - Spring 2007 23 Positional index example Can compress position values/offsets Nevertheless, this expands postings storage substantially <be: 993427; 1: 7, 18, 33, 72, 86, 231; 2: 3, 149; 4: 17, 191, 291, 430, 434; 5: 363, 367, …> Which of docs 1,2,4,5 could contain “to be or not to be”?

24. Semantic web - Computer Engineering Dept. - Spring 2007 24 Processing a phrase query Extract inverted index entries for each distinct term: to, be, or, not. Merge their doc:position lists to enumerate all positions with “to be or not to be”. –to: 2:1,17,74,222,551; 4:8,16,190,429,433; 7:13,23,191;... –be: 1:17,19; 4:17,191,291,430,434; 5:14,19,101;... Same general method for proximity searches

25. Semantic web - Computer Engineering Dept. - Spring 2007 25 Vector model of retrieval Documents are represented as vectors of terms In each entry a weight is considered. The weight is tfxidf: –term frequency (tf ) or wf, some measure of term density in a doc –inverse document frequency (idf ) measure of informativeness of a term: its rarity across the whole corpus could just be raw count of number of documents the term occurs in (idf i = 1/df i ) but by far the most commonly used version is:

26. Semantic web - Computer Engineering Dept. - Spring 2007 26 Why turn docs into vectors? First application: Query-by-example –Given a doc d, find others “like” it. Now that d is a vector, find vectors (docs) “near” it.

27. Semantic web - Computer Engineering Dept. - Spring 2007 27 Intuition Postulate: Documents that are “close together” in the vector space talk about the same things. t1t1 d2d2 d1d1 d3d3 d4d4 d5d5 t3t3 t2t2 θ φ

28. Semantic web - Computer Engineering Dept. - Spring 2007 28 Cosine similarity Distance between vectors d 1 and d 2 captured by the cosine of the angle x between them. Note – this is similarity, not distance –No triangle inequality for similarity. t 1 d 2 d 1 t 3 t 2 θ

29. Semantic web - Computer Engineering Dept. - Spring 2007 29 Cosine similarity Cosine of angle between two vectors The denominator involves the lengths of the vectors. Normalization

30. Semantic web - Computer Engineering Dept. - Spring 2007 30 Measures for a search engine How fast does it index –Number of documents/hour –(Average document size) How fast does it search –Latency as a function of index size Expressiveness of query language –Ability to express complex information needs –Speed on complex queries

31. Semantic web - Computer Engineering Dept. - Spring 2007 31 Measures for a search engine All of the preceding criteria are measurable: we can quantify speed/size; we can make expressiveness precise The key measure: user happiness –What is this? –Speed of response/size of index are factors –But blindingly fast, useless answers won’t make a user happy Need a way of quantifying user happiness

32. Semantic web - Computer Engineering Dept. - Spring 2007 32 Unranked retrieval evaluation: Precision and Recall Precision: fraction of retrieved docs that are relevant = P(relevant|retrieved) Recall: fraction of relevant docs that are retrieved = P(retrieved|relevant) Precision P = tp/(tp + fp) Recall R = tp/(tp + fn) RelevantNot Relevant Retrievedtpfp Not retrievedfntn

33. Semantic web - Computer Engineering Dept. - Spring 2007 33 Precision/Recall You can get high recall (but low precision) by retrieving all docs for all queries! Recall is a non-decreasing function of the number of docs retrieved In a good system, precision decreases as either number of docs retrieved or recall increases –A fact with strong empirical confirmation

34. Semantic web - Computer Engineering Dept. - Spring 2007 34 Typical (good) 11 point precisions

35. Semantic web - Computer Engineering Dept. - Spring 2007 35 Query expansion

36. Semantic web - Computer Engineering Dept. - Spring 2007 36 Relevance Feedback Relevance feedback: user feedback on relevance of docs in initial set of results –User issues a (short, simple) query –The user marks returned documents as relevant or non-relevant. –The system computes a better representation of the information need based on feedback. –Relevance feedback can go through one or more iterations. Idea: it may be difficult to formulate a good query when you don’t know the collection well, so iterate

37. Semantic web - Computer Engineering Dept. - Spring 2007 37 Relevance Feedback: Example Image search engine http://nayana.ece.ucsb.edu/imsearch/imsearch.ht ml

38. Semantic web - Computer Engineering Dept. - Spring 2007 38 Results for Initial Query

39. Semantic web - Computer Engineering Dept. - Spring 2007 39 Relevance Feedback

40. Semantic web - Computer Engineering Dept. - Spring 2007 40 Results after Relevance Feedback

41. Semantic web - Computer Engineering Dept. - Spring 2007 41 Rocchio Algorithm The Rocchio algorithm incorporates relevance feedback information into the vector space model. Want to maximize sim (Q, C r ) - sim (Q, C nr ) The optimal query vector for separating relevant and non-relevant documents (with cosine sim.): Q opt = optimal query; C r = set of rel. doc vectors; N = collection size Unrealistic: we don’t know relevant documents.

42. Semantic web - Computer Engineering Dept. - Spring 2007 42 Rocchio 1971 Algorithm (SMART) Used in practice: q m = modified query vector; q 0 = original query vector; α,β,γ: weights (hand-chosen or set empirically); D r = set of known relevant doc vectors; D nr = set of known irrelevant doc vectors New query moves toward relevant documents and away from irrelevant documents Tradeoff α vs. β/γ : If we have a lot of judged documents, we want a higher β/γ. Term weight can go negative –Negative term weights are ignored (set to 0)

43. Semantic web - Computer Engineering Dept. - Spring 2007 43 Types of Query Expansion Global Analysis: (static; of all documents in collection) –Controlled vocabulary Maintained by editors (e.g., medline) –Manual thesaurus E.g. MedLine: physician, syn: doc, doctor, MD, medico –Automatically derived thesaurus (co-occurrence statistics) –Refinements based on query log mining Common on the web Local Analysis: (dynamic) –Analysis of documents in result set

44. Semantic web - Computer Engineering Dept. - Spring 2007 44 Probabilistic relevance feedback Rather than reweighting in a vector space… If user has told us some relevant and some irrelevant documents, then we can proceed to build a probabilistic classifier, such as a Naive Bayes model: –P(t k |R) = |D rk | / |D r | –P(t k |NR) = |D nrk | / |D nr | t k is a term; D r is the set of known relevant documents; D rk is the subset that contain t k ; D nr is the set of known irrelevant documents; D nrk is the subset that contain t k.

45. Semantic web - Computer Engineering Dept. - Spring 2007 45 Binary Independence Model Since x i is either 0 or 1:

46. 46 Iteratively estimating p i 1.Assume that p i constant over all x i in query –p i = 0.5 (even odds) for any given doc 2.Determine guess of relevant document set: –V is fixed size set of highest ranked documents on this model (note: now a bit like tf.idf!) 3.We need to improve our guesses for p i and r i, so –Use distribution of x i in docs in V. Let V i be set of documents containing x i p i = |V i | / |V| –Assume if not retrieved then not relevant r i = (n i – |V i |) / (N – |V|) 4.Go to 2. until converges then return ranking

47. Semantic web - Computer Engineering Dept. - Spring 2007 47 Bayesian Networks for Text Retrieval (Turtle and Croft 1990) Standard probabilistic model assumes you can’t estimate P(R|D,Q) –Instead assume independence and use P(D|R) But maybe you can with a Bayesian network* What is a Bayesian network? –A directed acyclic graph –Nodes Events or Variables –Assume values. –For our purposes, all Boolean –Links model direct dependencies between nodes

48. Semantic web - Computer Engineering Dept. - Spring 2007 48 Bayesian Networks ab c a,b,c - propositions (events). p(c|ab) for all values for a,b,c p(a) p(b) Bayesian networks model causal relations between events Inference in Bayesian Nets: Given probability distributions for roots and conditional probabilities can compute apriori probability of any instance Fixing assumptions (e.g., b was observed) will cause recomputation of probabilities Conditional dependence

49. Semantic web - Computer Engineering Dept. - Spring 2007 49 Bayesian Nets for IR: Idea Document Network Query Network Large, but Compute once for each document collection Small, compute once for every query d1 dndn d2 t1t2 tntn r1r2 r3 rkrk di -documents ti - document representations ri - “concepts” I q2 q1 cmcm c2c1 ci - query concepts qi - high-level concepts I - goal node

50. Semantic web - Computer Engineering Dept. - Spring 2007 50 Web search basics The Web Ad indexes Web spider Indexer Indexes Search User

51. Semantic Search

52. Semantic web - Computer Engineering Dept. - Spring 2007 52 Ontology Meta Search Engines This group do retrieval by putting a system on top of a current search engine There are two types of this systems Using Filetype feature of search engines Swangling

53. Semantic web - Computer Engineering Dept. - Spring 2007 53 Filetype Feature Google started indexing RDF documents some time in late 2003 In the first type, there is a search engine that only searches specific file types (e.g. RSS, RDF, OWL) In fact we just forward the keywords of the queries with filetype feature to Google The main concern of such systems is on the visualization and browsing of results

54. Semantic web - Computer Engineering Dept. - Spring 2007 54 OntoSearch A basis system with Google as its “heart” Abilities: –The ability to specify the types of file(s) to be returned (OWL, RDFS, all) –The ability to specify the types of entities to be matched by each keyword (concept, attribute, values, comments, all) –The ability to specify partial or exact matches on entities. –Sub-graph matching eg concept animal with concept pig within 3 links; concepts with particular attributes

55. Semantic web - Computer Engineering Dept. - Spring 2007 55 Ontology Meta Search Engines In the second type we use traditional search engines again But since semantic tags are ignored by the underlying search engine, an intermediate format for documents and user queries are used A technique named Swangle is used for this purpose With this technique RDF triples are translated into strings suitable for underlying search engine

56. Semantic web - Computer Engineering Dept. - Spring 2007 56 Swangling Swangling turns a SW triple into 7 word like terms –One for each non-empty subset of the three components with the missing elements replaced by the special “don’t care” URI –Terms generated by a hashing function (e.g., SHA1) Swangling an RDF document means adding in triples with swangle terms. –This can be indexed and retrieved via conventional search engines like Google Allows one to search for a SWD with a triple that claims “Ossama bin Laden is located at X”

57. Semantic web - Computer Engineering Dept. - Spring 2007 57 A Swangled Triple <rdf:RDF xmlns:s="http://swoogle.umbc.edu/ontologies/swangle.owl#" Swangled text for [http://www.xfront.com/owl/ontologies/camera/#Camera, http://www.w3.org/2000/01/rdf-schema#subClassOf, http://www.xfront.com/owl/ontologies/camera/#PurchaseableItem] N656WNTZ36KQ5PX6RFUGVKQ63A M6IMWPWIH4YQI4IMGZYBGPYKEI HO2H3FOPAEM53AQIZ6YVPFQ2XI 2AQEUJOYPMXWKHZTENIJS6PQ6M IIVQRXOAYRH6GGRZDFXKEEB4PY 75Q5Z3BYAKRPLZDLFNS5KKMTOY 2FQ2YI7SNJ7OMXOXIDEEE2WOZU

58. Semantic web - Computer Engineering Dept. - Spring 2007 58 Swangler Architecture Web Search Engine Filters Semantic Markup Inference Engine Local KB Semantic Markup Semantic Markup Extractor Encoder (“swangler”) Ranked Pages Encoded Markup Semantic Web Query

59. Semantic web - Computer Engineering Dept. - Spring 2007 59 What’s the point? We’d like to get our documents into Google –Swangle terms look like words to Google and other search engines. On the other side, this translation is done for user queries too. –Add rules to the web server so that, when a search spider asks for document X the document swangled(X) is returned We could also use Swanglish – hashing each triple into N of the 50K most common English words

60. Semantic web - Computer Engineering Dept. - Spring 2007 60 Crawler Based Search Engines  They have a crawler and ranking of their own

61. Semantic web - Computer Engineering Dept. - Spring 2007 61

62. Semantic web - Computer Engineering Dept. - Spring 2007 62 Swoogle Architecture metadata creation data analysis interface SWD discovery SWD Metadata Web Service Web Server SWD Cache The Web Candidate URLs Web Crawler SWD Reader IR analyzerSWD analyzer Agent Service Swoogle 2: 340K SWDs, 48M triples, 5K SWOs, 97K classes, 55K properties, 7M individuals (4/05) Swoogle 3: 700K SWDs, 135M triples, 7.7K SWOs, (11/05)

63. Semantic web - Computer Engineering Dept. - Spring 2007 63 Crawler Based Ontology Search Engines  Discovery  Crawling of SW documents is different from html documents  In SW we express knowledge using URI in RDF triples. Unlike html hyperlinks, URIs in RDF may point to a non existing entity  Also RDF may be embedded in html documents or be stored in a separate file.

64. Semantic web - Computer Engineering Dept. - Spring 2007 64 Semantic Web Crawler Such crawlers should have the following properties  Should crawl on heterogeneous web resources (owl, oil, daml, rdf, xml, html)  Avoid circular links  Completing RDF holes  Aggregating RDF chunks

65. Semantic web - Computer Engineering Dept. - Spring 2007 65 Metadata Creation Web document metadata –When/how discovered/fetched –Suffix of URL –Last modified time –Document size SSWD metadata –Language features OWL species RDF encoding –Statistical features Defined/used terms Declared/used namespaces Ontology Ratio –Ontology Rank Ontology annotation –Label –Version –Comment Related Relational Metadata –Links to other SWDs Imported SWDs Referenced SWDs Extended SWDs Prior version –Links to terms Classes/Properties defined/used

66. Semantic web - Computer Engineering Dept. - Spring 2007 66 Digesting Digest –But the main point is that count, type and meaning of relations in SW is more complete than the current web

67. Semantic web - Computer Engineering Dept. - Spring 2007 67 RDF graph Resource Web SWT SWD uses populates defines officialOnto isDefinedBy owl:imports … rdfs:seeAlso rdfs:isDefinedBy SWO isUsedBy isPopulatedBy rdfs:subClassOf sameNamespace, sameLocalname Extends class-property bond 1 2 3 4 5 67 Term Search Document Search literal Semantic Web Navigation Model Navigating the HTML web is simple; there’s just one kind of link. The SW has more kinds of links and hence more navigation paths.

68. Semantic web - Computer Engineering Dept. - Spring 2007 68 foaf:Personfoaf:Agent rdfs:subClassOf foaf:mbox foaf:Person rdf:type mailto:finin@umbc.edu foaf:mbox rdfs:domain owl:InverseFunctionalProperty owl:Class rdfs:range owl:Thing rdf:type foaf:Person rdf:type http://www.cs.umbc.edu/~finin/foaf.rdf rdfs:seeAlso http://www.cs.umbc.edu/~finin/foaf.rdfhttp://www.cs.umbc.edu/~dingli1/foaf.rdf http://xmlns.com/foaf/0.1/index.rdf http://www.w3.org/2002/07/owl owl:imports An Example We navigate the Semantic Web via links in the physical layer of RDF documents and also via links in the “logical” layer defined by the semantics of RDF and OWL.

69. Semantic web - Computer Engineering Dept. - Spring 2007 69 Rank has its privilege Google introduced a new approach to ranking query results using a simple “popularity” metric. –It was a big improvement! Swoogle ranks its query results also –When searching for an ontology, class or property, wouldn’t one want to see the most used ones first? Ranking SW content requires different algorithms for different kinds of SW objects –For SWDs, SWTs, individuals, “assertions”, molecules, etc…

70. Semantic web - Computer Engineering Dept. - Spring 2007 70 Ranking SWDs For offline ranking it is possible to use the references idea of PageRank. In OntoRank values for each ontology is calculated very similar to PageRank in traditional search engines like google Ranking based on “Referencing” identify and rank of referrer Number of citation by others Distance of reference from origin to target Types of links: Import Extend Instantiate Prior version..

71. Semantic web - Computer Engineering Dept. - Spring 2007 71 An Example http://www.cs.umbc.edu/~finin/foaf.rdf http://xmlns.com/wordnet/1.6/ http://xmlns.com/foaf/1.0/ EX TM http://www.w3.org/2000/01/rdf-schema wPR =0.2 wPR =100 wPR =3 wPR =300 OntoRank =0.2 OntoRank =100 OntoRank =103 OntoRank =403

72. Semantic web - Computer Engineering Dept. - Spring 2007 72 Crawler Based Ontology Search Engines Service –User interface – Services to application systems

73. Semantic web - Computer Engineering Dept. - Spring 2007 73 Find “Time” Ontology We can use a set of keywords to search ontology. For example, “time, before, after” are basic concepts for a “Time” ontology. Demo 1

74. Semantic web - Computer Engineering Dept. - Spring 2007 74 Digest “Time” Ontology (document view) Demo 2(a)

75. Semantic web - Computer Engineering Dept. - Spring 2007 75 Summary Swoogle (Mar, 2004) Swoogle2 (Sep, 2004) Swoogle3 (July 2005)  Automated SWD discovery  SWD metadata creation and search  Ontology rank (rational surfer model)  Swoogle watch  Web Interface  Ontology dictionary  Swoogle statistics  Web service interface (WSDL)  Bag of URIref IR search  Triple shopping cart  Better (re-)crawling strategies  Better navigation models  Index instance data  More metadata (ontology mapping and OWL-S services )  Better web service interfaces  IR component for string literals 2005 2004

76. Semantic web - Computer Engineering Dept. - Spring 2007 76 Applications and use cases Supporting Semantic Web developers, e.g., –Ontology designers –Vocabulary discovery –Who’s using my ontologies or data? –Etc. Searching specialized collections, e.g., –Proofs in Inference Web –Text Meaning Representations of news stories in SemNews Supporting SW tools, e.g., –Discovering mappings between ontologies

77. Semantic web - Computer Engineering Dept. - Spring 2007 77 Semantic Search Engines There are some restrictions for current search engines One interesting example : ”Matrix” Another example is java Semantic web is introduced to overcome this problem. The most important tool in semantic web for improving search results is context concept and its correspondence with Ontologies. This type of search engines uses such ontological definitions

78. Semantic web - Computer Engineering Dept. - Spring 2007 78 Two Levels of the Semantic Web Deep Semantic Web: –Intelligent agents performing inference –Semantic Web as distributed AI –Small problem … the AI problem is not yet solved Shallow Semantic Web: using SW/Knowledge Representation techniques for –Data integration –Search –Is starting to see traction in industry

79. Semantic web - Computer Engineering Dept. - Spring 2007 79 Problems with current search engines Current search engines = keywords: –high recall, low precision –sensitive to vocabulary –insensitive to implicit content

80. Semantic web - Computer Engineering Dept. - Spring 2007 80 Semantic Search Engines It is possible to categorize this type of search engines to three groups. –Context Based Search Engines They are the largest one, aim is to add semantic operations for better results. –Evolutionary Search Engines Use facilities of semantic web to accumulate information on a topic we are researching on. –Semantic Association Discovery Engines They try to find semantic relations between two or more terms.

81. Semantic web - Computer Engineering Dept. - Spring 2007 81 Context Based Search Engines

82. Semantic web - Computer Engineering Dept. - Spring 2007 82 Context Based Search Engines 1) Crawling the semantic web: –There is not much difference between these crawlers and ordinary web crawlers –many of the implemented systems uses an existing web crawler as underlying system. –Its better to develop a crawler that understands special semantic tags. –One of the important features of theses crawlers should be the exploration of ontologies that are referred from existing web pages

83. Semantic web - Computer Engineering Dept. - Spring 2007 83 Annotation Methods Annotation is perquisite of Search in semantic web. There are different approaches which spawn in a broad spectrum from complete manual to full automatic methods. Selection of an appropriate method depends on the domain of interest In general meta-data generation for structured data is simpler

84. Semantic web - Computer Engineering Dept. - Spring 2007 84 Annotation Methods Annotations can be categorized based on following aspects:  Type of meta-data Structural : non contextual information about content is expressed (e.g. language and format) Semantic: The main concern is on the detailed content of information and usually is stored as RDF triples

85. Semantic web - Computer Engineering Dept. - Spring 2007 85 Annotation Methods Generation approach –A simple approach is to generate meta-data without considering the overall theme of the page. (Without Ontology) –Better approach is to use an ontology in the generation process. Using a previously specified ontology for that type, generate meta-data that instantiates concepts and relations of ontology for that page The main advantage of this method is the usage of contextual information.

86. Semantic web - Computer Engineering Dept. - Spring 2007 86 Annotation Methods Source of generation –The ordinary source of meta-data generation is a page itself –Sometimes it is beneficial to use other complementary sources, like using network available resources for accumulating more information for a page For example for a movie it might be possible to use IMDB to extract additional information like director, genre, etc.

87. Semantic web - Computer Engineering Dept. - Spring 2007 87 Evolutionary Search Engines The advanced type of search is some thing like research Here we aim at gathering some information about specific topic It can be something like search by Teoma search engine For example if we give the name of a singer to the search engine it should be able to find some related data to this singer like biography, posters, albums and so on.

88. Semantic web - Computer Engineering Dept. - Spring 2007 88 Evolutionary Search Engines These engines usually use on of the commercial search engines as their base component for searching and they augment returned result by these base engines. This augmented information is gathered from some data- insensitive web resources.

89. Semantic web - Computer Engineering Dept. - Spring 2007 89 Evolutionary Search Engines Architecture

90. Semantic web - Computer Engineering Dept. - Spring 2007 90 Evolutionary Search Engines It has some similarities with previous category’s architecture Here we crawl and generate annotation just for some well know informational web pages i.e. CDNow, Amazon, IMDB After this phase we collect annotations in a repository.

91. Semantic web - Computer Engineering Dept. - Spring 2007 91 Evolutionary Search Engines Whenever a sample user posed a query two processes must be performed:  first, we should give this query to a usual search engine (usually Google) to obtaining raw results.  Second, system will attempt to detect the context and its corresponding ontology for the user’s request in order to extract some key concepts.  Later we use these concepts to fetch some information from our metadata repository.  The last step in this architecture is combining and displaying results.

92. Semantic web - Computer Engineering Dept. - Spring 2007 92 Evolutionary Search Engines Main problems and challenge in these types of engines are :  Concept extraction from user’s request  Selecting proper annotation to show and their order

93. Semantic web - Computer Engineering Dept. - Spring 2007 93 Evolutionary Search Engines Concept extraction from user’s request there are some problems that lead to misunderstanding of input query by system; – Inherent ambiguity in query specified by user – Complex terms that must be decomposed to understand.

94. Semantic web - Computer Engineering Dept. - Spring 2007 94 Evolutionary Search Engines Selecting proper annotation to show and their order: –often we find a huge number of potential metadata related to the initial request and we should choose those ones that are more useful for user. –A simple approach is using other concepts around our core concept (which we extracted it before) in base ontology –if we have more than one core concept we must focus on those concepts that are on the path between these concepts.

95. Semantic web - Computer Engineering Dept. - Spring 2007 95 Displaying the Results Results are displayed using a set of templates Each class of object has an associated set of templates The templates specify the class and the properties and a HTML template A template is identified for each node in the ordered list and the HTML is generated The HTML is included in the results page

96. Semantic web - Computer Engineering Dept. - Spring 2007 96 W3C Search W3C Semantic Search has five different data sources: People, Activities, Working Groups, Documents, and News Both ABS and W3C Semantic Search have a basic ontology about people, places, events, organizations, vocabulary terms, etc. The plan is to augment a traditional search with data from the Semantic Web

97. Semantic web - Computer Engineering Dept. - Spring 2007 97 Base Ontology A segment of the Semantic Web pertaining to Eric Miller

98. Semantic web - Computer Engineering Dept. - Spring 2007 98 Sample Applications-W3C Search

99. Semantic web - Computer Engineering Dept. - Spring 2007 99 Activity Based Search ABS contains data from many sites, such as AllMusic, Ebay, Amazon, AOL Shopping, TicketMaster, Weather.com and Mapquest There are millions of triples in the ABS Semantic Web TAP knowledge base has a broad range of domains including people, places, organizations, and products Resources have a rdf:type and rdfs:label

100. Semantic web - Computer Engineering Dept. - Spring 2007 100 Sample Applications-ABS

101. Semantic web - Computer Engineering Dept. - Spring 2007 101 Sample Applications-ABS

102. Semantic web - Computer Engineering Dept. - Spring 2007 102 References T. Finin, J. Mayfield, C. Fink, A. Joshi, and R. S. Cost, “Information retrieval and the semantic web,” in Proceedings of the 38th International Conference on System Sciences, Hawaii, United States of America, 2005. T. Finin, L. Ding, R. Pan, A. Joshi, P. Kolari, A. Java, and Y. Peng, “Swoogle: Searching for knowledge on the semantic web,” in Proceedings of the AAAI 05, 2005. R. Guha, R. McCool, and E. Miller, “Semantic search,” in Proc. of the12th international conference on World Wide Web, New Orleans, 2003, pp. 700–709. Y. Zhang, W. Vasconcelos, and D. Sleeman, “OntoSearch: An ontology search engine,” in The Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, 2004.