TechWatch. Technology and Market Observation powered by SMILA
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1 TechWatch Technology and Market Observation powered by SMILA PD Dr. Günter Neumann DFKI, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Juni 2011
2 Goal - Observation of Innovations and Trends» Technologies:» Combine methods from Bibliometrics, Information Wrapping, Text Mining, Information Extraction, Semantic Search and Knowledge Management.» Applications:» Search for publications and patents» The analysis of extracted relationships between themes, authors, time behavior and organizations.» Search for Key-Players of innovations.» Web-based search for trend statements.» Presentation of a knowledge domain as ontology, which can be used in further functionalities that improve the search and analysis
3 Partner: ThyssenKrupp Steel AG» Cooperation between DFKI and TKS since 2006» Ontology-based search for key players in the area of welding and joining technologies» Authors, inventors, companies, institutes, technologies» Advanced patent search (EPO)» Publications (Google Scholar)» Visualization of the created innovation network and of new trends Tech Watch» Software development TechWatchTool» In Theseus-Ordo» SMILA-fication of the system architecture» Ontology-based information extraction» Trend-Monitoring and analysis (new technologies, innovation pushers)
4 Important: Language Technology! Extraction of relations through linguistic analysis e.g., dependency analysis Information that change over time e.g., Increase of publications about lithium batteries Near-Synonyms Approach ; Method Collocations come to a decision, make a picture Co-reference dependency analysis, this method Hyponyms, Part-of Relationship is a, consists of Meta data is author of, is owner of the patent
5 Search and Analysis of Innovation Indicators» Relevant Key Phrases» Technology of tomorrow, Future, Development» Sentence patterns» Lithium-ion batteries will power cars in the future.» Lithium-ion technology is the future for hybrid and electric cars.» The laser will always be good for a surprise also in the future, predicts Peter Leibinger.
6 TechWatchTool
7 Patent Search
8 Patent Search
9 Patent Search
10 Trend analysis
11 Trend analysis
12 TechWatch SMILA Integration» The core components are integrated via wrapper methods» Patent search» Google Scholar» DepatisNet» Persistence» Trend search» Google Custom Search» Document processing» Trend patterns» Relation extraction» Named Entity recognition» Persistence
13 TechWatch - DFKI Language Technology software LanguageID Automatic language identification 23 languages Speed: ~6ms Accuracy: 99,8% Implementation: Java, OSGi NE-Hub Extraction of Named Entities: Persons, Organizations, Locations, etc. Integrates the result of different NE-recognizers (Sprout, OpenNLP, Stanford, LingPipe, etc.) Languages: DE, EN Implementation: Java, OSGi MDParser Extraction of the dependency relations of sentences Languages: DE, EN & more! Speed: 40 Sent./Sec. Accuracy: 86% - 88% Implementation: Java, OSGi DARE Semi-supervised extraction of relations Languages: DE, EN Implementation: Java, OSGi
14 Example - NE-Hub Selection and orchestration of the components Voting mechanism
15 Example - NE-Hub Selection and orchestration of the components Voting mechanism
16 Example - MDParser A fine-grained extraction and search for entities and relations demands for a structural analysis of free text. Recently, dependency relations between words are getting more and more important for semantic applications The dependency relationships uncover a flat semantic predicate argument structure. Our current R&D focus Automatic learning of dependency grammars and parsers from corpora Multilingual dependency parsing Solution: MDParser
17 Example - MDParser RTE-6: 3376/74326 Sent./Words A fine-grained extraction and search for entities and relations demands for a structural analysis of free text. Recently, dependency relations between words are getting more and more important for semantic applications The dependency relationships uncover a flat semantic predicate argument structure. Our current R&D focus Automatic learning of dependency grammars and parsers from corpora Multilingual dependency parsing Solution: MDParser
18 Example - DARE Extraction of relations of different complexities. Automatic learning of linguistic patterns for the extraction of new instances and their projections. Bootstrapping between pattern learning and relation extraction which is initialized by means of a small set of seed instances Integration into TechWatchTool relevant sentences seeds (Technologies, developments) Learning of linguistic patterns for the automatic recognition of new technologies and developments for a future trend analysis. Use of the found results (technologies & developments) of the trend analysis as new seeds linguistics patterns Search of relevant sentences by means of innovation indicators.
19 TechWatchTool DFKI advanced LT components ukeywe Unsupervised Machine Learning algorithm for the extraction of key phrases Languages: language independent Implementation: Java, OSGi TERA (Textual Entailment) Decides for two text fragments (phrases, sentences, paragraphs), whether the one semantically entails the other Languages: DE, EN Accuracy: best results at international competitions (cf. Text Analysis Conference - TAC) Implementation: Java, OSGi ConExt Extraction of concept hierarchies from free texts Languages: DE, EN Implementation: Java, OSGi
20 Example - ConExt Extraction of concept hierarchies (based on Cimiano, Hotho, Staab; 2005) a method for extracting domain specific concept hierarchies from free text which has been syntactically analysed Core idea: words, which occur with same syntactic functions and context are semantically related. Can also be used to determine the terminology of a domain. semantically related words can be easier identified. Information extraction can benefit (e.g., by means of Query Expansion, Clustering) The strategy has been tested with several corpora: business reports ( sentences) scientific publications ( sentences) Wikipedia, technical articles ( Sentences) Query expansion Clutsering
21 Example - ConExt Extraction of concept hierarchies (based on Cimiano, Hotho, Staab; 2005) a method for extracting domain specific concept hierarchies from free text which has been syntactically analysed Core idea: words, which occur with same syntactic functions and context are semantically related. Can also be used to determine the terminology of a domain. semantically related words can be easier identified. Information extraction can benefit (e.g., by means of Query Expansion, Clustering) The strategy has been tested with several corpora: business reports ( sentences) scientific publications ( sentences) Wikipedia, technical articles ( Sentences) Query expansion Clutsering
22 Example - Textual Inference Textual Inference Determine semantic inference relationships between texts (Recognition of text variants/paraphrases) Entailment: Does Text T entails Text H? Needed in many text analytics applications (e.g., information extraction, question answering, text summarization) Q: Wo acquired Overture? A: Googles buying of Overture supports... : X buys Y X acquire Y
23 Example - Textual Inference Textual Inference Determine semantic inference relationships between texts (Recognition of text variants/paraphrases) Entailment: Does Text T entails Text H? Needed in many text analytics applications (e.g., information extraction, question answering, text summarization) Q: Wo acquired Overture? A: Googles buying of Overture supports... : X buys Y X acquire Y
24 Example - Textual Inference Textual Inference Determine semantic inference relationships between texts (Recognition of text variants/paraphrases) Entailment: Does Text T entails Text H? Needed in many text analytics applications (e.g., information extraction, question answering, text summarization) Q: Wo acquired Overture? A: Googles buying of Overture supports... : X buys Y X acquire Y Textual inference in TechWatch Ordo semantic search concept extraction/ontology learning Textual inference (Entailment) is still a very young and dynamic R&D area! Progress through international competitions (NIST, USA) DFKI with very good results EU project Excitement -> prob. from > ~ 3.4 million EUR funding (DFKI is one of the 4 core scientific partners, 3 industrial partners; analysis of customer interaction data)
25 Example - Textual Inference Textual Inference Determine semantic inference relationships between texts (Recognition of text variants/paraphrases) Entailment: Does Text T entails Text H? Needed in many text analytics applications (e.g., information extraction, question answering, text summarization) Q: Wo acquired Overture? A: Googles buying of Overture supports... : X buys Y X acquire Y Textual inference in TechWatch Ordo semantic search concept extraction/ontology learning Textual inference (Entailment) is still a very young and dynamic R&D area! Progress through international competitions (NIST, USA) DFKI with very good results EU project Excitement -> prob. from > ~ 3.4 million EUR funding (DFKI is one of the 4 core scientific partners, 3 industrial partners; analysis of customer interaction data) Focus: - robustness, scalability - organized by NIST, USA : 19 Unis/Institute (world wide), DFKI, 4. best result in Novelty Subtask (with new team) (2009: 2. best result in Main Task.)
26 Eclipse Marketplace Current Implementation: SMILA Piplets (Java- OSGi web service) Direct integration into SMILA-process License: Research & commercial Planned NE-Hub Multilingual architecture for NE recognition
27 Summary Experience of our SMILA developers Pros Rapid entry point for End-User by means of: detailed documentation many good examples Out-of-the-box runnable system Parallelism of workflow through BPEL Fast composition of new apps by means of existing components and BPEL Rapid and competent support by means of the forum Cons Complicated data structures Version 0.7 solved in Version 0.8 Restricted extendability of the index algorithms in order to support several dynamically changing indices per user (a very specific requierement)
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