SemTag and Seeker: Bootstrapping the Semantic Web via Automated Semantic Annotation
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Transcript of SemTag and Seeker: Bootstrapping the Semantic Web via Automated Semantic Annotation
SemTag and Seeker: Bootstrapping the Semantic Web via Automated
Semantic Annotation
Presented by: Hussain Sattuwala
Stephen Dill, Nadav Eiron, David Gibson, Daniel Gruhl, R. Guha, Anant Jhingran, Tapas
Kanungo, Sridhar Rjagopalan, Andrew Tomkins, John A. Tomlin, Jason Y. Zien
IBM Almaden Research Centerhttp://www.almaden.ibm.com/webfountain/resources/semtag.pdf
Outline Motivation Goal SemTag
Architecture Phases TBD Results Methodology
Seeker Design Architecture Environment
Conclusion Related and Future work.
Motivation Natural language processing is the most
significant obstacle in building machine understandable web.
To allow for the Semantic Web to become a reality we need: Web-services to maintain & provide metadata. Annotated documents (OWL, RDF, XML, ...).
Annotations Current practice of annotation for knowledge
identification , extraction & other applications
is time consuming
needs annotation by experts
is complex
Reduce burden of text annotation for Knowledge Management
Goal To perform automated semantic tagging of large
corpora.
To introduce a new disambiguation algorithm to resolve ambiguities in a natural language corpus.
To introduce the platform which different tagging applications can share.
SemTag The goal is to automatic add semantic tags to the
existing HTML body of the web.
Example:“The Chicago Bulls announced that Michael Jordan will…”
Will be:The <resource ref = http://tap.stanford.edu/Basketball Team_Bulls>Chicago Bulls</resource> announced yesterday that <resource ref = “http://tap.stanford.edu/ AthleteJordan_Michael”> Michael Jordan</resource> will...’’
SemTag Uses TAP KB
TAP is a public broad, shallow knowledgebase. TAP contains lexical and taxonomical information about
popular objects like music, movies, sports, etc.
Problem: No write access to original documentHow do you annotate???
Uses the concept of Label Bureau from PICS (Platform for Internet Content Selection) HTTP server that can be queried for annotation
information Separate store of semantic annotation information
Example: Annotated Page
SemTag Architecture
Retrieve documents
Find Context
Tokenize
determine distribution of terms
Disambiguate windows
Add to DB
Spotting Learning
Tagging
Automatic
Manual
SemTag Phases 1. Spotting:
Retrieve documents from Seeker. Tokenize documents. Find contexts (10 words + label + 10 words) that
appears in TAP Taxonomy.
2. Learning: Scan the representative sample to determine
distribution of terms at each internal node of the taxonomy.
SemTag Phases, cont’d 3. Tagging
Disambiguate windows (using TBD). Add to the database.
Ambiguities types: Same label appears at multiple locations in TAP
ontology. Some entities have labels that occur in context that
have no representative in the taxonomy.
Training Data: Automatic metadata Manual metadata
TBD Methodology Each node has a set of labels.
E.g.: cats, football, cars all contain the label Jaguar.
Each label in the text is stored with a window of 20 words – the context
A spot(l,c) is a label in a context.
Each node has an associated similarity function mapping a context to a similarity Higher similarity more likely to contain a reference
TBD - Similarity Generate 200k dimensional vector corresponding
to context.
TF-IDF scheme Each entry of the vector is the frequency of the term
occurring at that node divide by corpus frequency of the term.
IR Algorithm – Cosine Similarity Vector product of sparse spot vector and dense node
vector
TBD - Algorithm Some internal nodes very popular:
Associate a measurement Mus of how accurate Sim is
likely to be at a node. Also Mu
a, how ambiguous the node is overall (consistency of human judgment)
TBD Algorithm: returns 1 or 0 to indicate whether a particular context c is on topic for a node v
82% accuracy on 434 million spots
The TBD Algorithm
SemTag Results Applied on 264 million pages
Produced 550 million labels.
Final set of 434 million spots with Accuracy 82%.
SemTag Methodology1. Lexicon generation:
Approximately 90 million total words. 1.4 million unique words . Most frequent 200,000 words.
2. Similarity functions: Estimated distribution of terms corresponding to 192
most common TAP nodes to derive fu.
SemTag Methodology, cont’d3. Measurement values:
Determined based on 750 relevant human judgments.
4. Full TBD Processing: Applied to 550m spots.
5. Evaluation: Compared TBD results with additional 378 human
judgments.
Seeker A platform used by SemTag and other increasingly
sophisticated text analytics applications.
Provides scalable, extensible knowledge extraction from erratic resources.
Erratic resources???
Seeker Design Goals Composability
Modularity
Extensibilty
Scalability
Robustness
Seeker Architecture
Indexing Tokens
Storage &CommunicationCrawls WEB
AnnotatorsQuery
Processing
SemTagComponents
n/w level APIs
Miners
Scalability & Robustness
Modular & Extensible
Seeker Design To achieve modularity and extensibility
SOA (service-oriented architecture) was used where communication among agents is done through a set of language-independent network-level APIs.
To achieve scalability and robustness Infrastructure components.
Infrastructure Components The Data Store
Central repository for all data storage. Communication medium.
The Indexer For indexing sequences of tokens.
The Joiner Query processing component.
Analysis Agents Annotators
Performs some local processing on each web page and write back results to the store in form of an annotation.
Miners Performs Intermediate processing Looks at the results of spots on many pages in order to
disambiguate them.
Observation Advantage
Other application can obtain semantic annotation from web-available database.
Use both human & computer judgments to solve ambiguous data in their TBD algorithm
Disadvantage The system require a large amount of storage space to
store data. Requires much larger and richer KB to build web scale
ontology.
Conclusion Automatic semantic tagging is essential to
bootstrap the Semantic Web.
It’s possible to achieve good accuracy with simple disambiguation approaches.
Future Work Develop more approaches and algorithms to
automated tagging.
Make annotated data public and seeker as a public service.
Related Work Systems built as a result of the Semantic Web are
divided among two types: Create ontologies – semi automated Page annotation. Examples: Protégé, OntoAnnotate, Anntea, SHOE, …
Some AI approaches were used, but, they need a lot of training. Principal tool:Wrapping
Some used other NL understanding techniques, example ALPHA.
Questions?