Hybrid acquisition of temporal scopes for rdf data
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Hybrid acquisition of temporal scopes for rdf data

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Information on the temporal interval of validity for facts described by RDF triples plays an important role in a large number of applications. Yet, most of the knowledge bases available on the Web of ...

Information on the temporal interval of validity for facts described by RDF triples plays an important role in a large number of applications. Yet, most of the knowledge bases available on the Web of Data do not provide such information in an explicit manner. In this paper, we present a generic approach which addresses this drawback by inserting temporal information into knowledge bases. Our approach combines two types of information to associate RDF triples with time intervals. First, it relies on temporal information gathered from the document Web by an extension of the fact validation framework DeFacto. Second, it harnesses the time information contained in knowledge bases. This knowledge is combined within a three-step approach which comprises the steps matching,
selection and merging. We evaluate our approach against a corpus of facts gathered from Yago2 by using DBpedia and Freebase as input and different parameter settings for the underlying algorithms. Our results suggest that we can detect temporal information for facts from DBpedia
with an F-measure of up to 70%.

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  • A temporal <br /> 1.8 Billion from http://www.worldwidewebsize.com/ <br /> <br /> Note: we also consider more temporal annotation per triple!
  • Temporally annotated RDF triples are useful for many reasons... <br /> -facts are usually considered as time invariant while in reality they dynamically change <br /> <br /> Large problem space (even at high temporal granularity levels, e.g., all possible time intervals at year granularity) <br /> <br /> <br /> Can be used as a dimensions along with facts can be organized, ranked or explored <br /> Relevancy ranking purposes <br />
  • 1.8 Billion from http://www.worldwidewebsize.com/
  • Finally we return a distribution of all dates and their number of occurrences in a given context. Hence, the output of temporal DeFacto for a fact f can be regarded as a vector DFV over all possible time points ti whose ith entry is the number of co-occurrences of s or o with ti
  • The links between the facts and the date are lost <br /> We assume that temporal triples contain relevant <br /> Dates are considered at year level
  • Each cell in the SM represents the significance of the interval identified by the cell for the given fact based on the distribution of time points acquired from the web <br /> <br /> - inject a time distribution vector into the entity-level RIM by producing a significance matrix SM <br /> Each cell of the matrix where i&lt;j is calculated as the number of time points included in the interval [i,j] (average of time points contained in the interval) <br /> For the diagonal we provide in alternative another formula to penalize intervals of 1 year by giving a weight to the number of time point in the diagonal
  • %
  • -macro precision as the average of all facts <br />
  • Difficulty of the task <br /> Sufficient relevant time points <br /> Macro averge <br /> Difficult task since it depends on the number of available time points
  • Molti fatti siamo molto precisi <br /> Altri meno <br /> Future: capire quelli che non sono precisi il perché, dare la confidence

Hybrid acquisition of temporal scopes for rdf data Presentation Transcript

  • 1. Hybrid Acquisition of Temporal Scopes for RDF Data Anisa Rula1, Matteo Palmonari1, Axel-Cyrille Ngonga Ngomo2, Daniel Gerber2, Jens Lehmann2, and Lorenz Bühmann2 1. University of Milano-Bicocca, SITI Lab 2. Universität Leipzig, Institut für Informatik, AKSW
  • 2. 2 Outline Anisa Rula 1. Introduction & Motivation 2. Approach Overview 3. Details of the Approach 4. Experimental Evaluation 5. Conclusions
  • 3. team team Temporally annotated RDF triples Alexandre Pato S.C. Corinthians Anisa Rula  Some facts are always valid while other facts are valid for a certain time interval (volatile facts)  Volatile facts are represented by triples whose validity is defined by a time interval i.e. the temporal scope Temporal Scoping of RDF triples 2007-2013 2013-2014 Temporal scopes, represented by time intervals A.C. Milan 3
  • 4. Motivation  World changes: relations represented in RDF triples may be valid only for a specific time interval [Gutierrez et al.,2005] o E.g. <Alexandre_Pato, team, A.C._Milan> [2007,2013]  Many applications have to use temporally annotated RDF triples o E.g. Temporal Query Answering, Question Answering over KBs, Temporal Reasoning, Timelines Challenges  Low availability and quality of temporal information in RDF data  NLP challenges for web-scale temporal information extraction (scalability, availability of corpus, conflicting information) [Derczynsk et al., 2013] Motivation & Challenges Anisa Rula 4 Temporally annotated RDF triples are largely unavailable or incomplete in the LOD (Rula et al., 2012)
  • 5. Anisa Rula Approach Overview: Use the Web as Source of Evidence Web of Data - RDF (61.9 Billion) World Wide Web (1.8 Billion) Source of evidence Temporally annotated RDF triples team team Alexandre Pato team team Alexandre Pato S.C. Corinthians A.C. Milan 2007-2013 2013-2014S.C. Corinthians A.C. Milan 5Anisa Rula  Use evidence from the Web for temporal scoping of RDF triples
  • 6. Web of Documents Mapping facts to time intervals Temporal Information Extraction fact t1 occ1 t2 occ2 t3 occ3 t4 occ4 Matching Selection Reasoning Approach Overview: Hybrid Acquisition of Time Scopes <s,p,o> Web of Data t1 t2 t3 … tn t1 t2 t3 … tn Temporally annotated RDF triples 6Anisa Rula Set of disconnected time intervals <s,p,o>[x1,y1],…,[xn,yn]
  • 7. Temporal Information Extraction - Web Documents Anisa Rula 7 DeFacto [Lehmann & al. 2012]  Retrieves a set of webpages that confirm the given RDF triple  The RDF triple issued to the search engine is verbalized by using natural language patterns Temporal Extension for DeFacto (TempDeFacto)  Apply Named Entity Tagger to extract the entities of type Date class  Observe the occurrences of the labels of the subject and object in less than 20 tokens  Analyze the context window of n characters before and after subject- object occurrences in order to retrieve the time points  Return a distribution vector of date and their number of occurrences
  • 8. Temporal Information Extraction - Web Documents Anisa Rula 8 <Alexandre_Pato,team, A.C._Milan> “Alexandre Pato” “played for” “A.C. Milan” “Pato” “’s striker” “Milan” “CR7” “Mi” Pato played for A.C. Milan from 2007 to 2013. A.C. Milan’s top striker Pato left in 2013. In 2013 Pato visited Milan for a short holiday. 2013 17 2007 11 2006 1 …. …. 2010 4 2009 4 1989 2 Occurrences of the labels of the subject and object Context window of n characters before and after subject-object occurrences NamedEntityTagger DeFacto Vector (dfv)
  • 9. Temporal Information Extraction - Web of Data <Alexandre_Pato> Content negotiation null null null null null null 0 null null null null null 0 0 null null null null 0 0 0 null null null 0 0 0 0 null null 0 0 0 0 0 null 1989 2000 2006 2007 2008 2013 1989 2000 2006 2007 2008 2013 Relevant Interval Matrix (RIM) Regular expressions TAlexandre_Pato= {1989, 2000, 2006, 2007, 2008, 2013} Relevant Time Points RDF document d Alexandre_Pato Anisa Rula  The set of time intervals for a given triple with starting and ending time points defined with the set of relevant time points ∀ 𝑟𝑖𝑚 𝑡𝑖 𝑡 𝑗 ∈ 𝑅𝐼𝑀 𝑒 𝑤𝑖𝑡ℎ 𝑖, 𝑗 > 0 𝑓𝑜𝑟 𝑖 ≤ 𝑗 𝑟𝑖𝑚 𝑡𝑖 𝑡 𝑗 = 𝑛𝑢𝑙𝑙 𝑓𝑜𝑟 𝑖 > 𝑗 𝑟𝑖𝑚 𝑡𝑖 𝑡 𝑗 = 0 9
  • 10. null null null null null null null null null null null null null null null null null null null null null 1989 2000 2006 2007 2008 2013 1989 2000 2006 2007 2008 2013 1. Matching temporal distribution (dfv) against the relevant time interval matrix 0.004 0.166 0.166 0.736 0.8 2.48 0 0 0.142 1.5 1.555 4.2 0 0 0.002 6 4.666 7.5 0 0 0 0.026 6.5 8.428 0 0 0 0 0.004 8 0 0 0 0 0 0.040 1989 2000 2006 2007 2008 2013 1989 2000 2006 2007 2008 2013 RIM Mapping Facts to Time Intervals - Matching Matching Selection Reasoning RDF data 2013 17 2007 11 2006 1 2011 6 2008 2 2016 3 2012 15 2010 4 2009 4 1989 2 𝑠𝑚2007:2008= 11 + 2 2 = 6.5 Significance Matrix (SM)dfv Anisa Rula 10
  • 11. 1989 2000 2006 2007 2008 2013 1989 2000 2006 2007 2008 2013 SM 0.004 0.166 0.166 0.736 0.8 2.48 0 0 0.142 1.5 1.555 4.2 0 0 0.002 6 4.666 7.5 0 0 0 0.026 6.5 8.428 0 0 0 0 0.004 8 0 0 0 0 0 0.040 Mapping Facts to Time Intervals - Selection 2. Mapping Selection:  top-k function: selects the k intervals that have highest scores in the SM  neighbor-x: selects a set of intervals whose significance score is close to the maximum significance score in the SM matrix, up to a certain threshold x  neighbor-k-x: selects the top-k intervals in the neighborhood of the interval with higher significance score neighbor, 𝑥 = 23 top-k , 𝑘 = 3 neighbor-k-x , 𝑘 = 2, 𝑥 = 23 [2007, 2013][2008, 2013] [2006,2013][2007, 2013][2008, 2013] [2007,2008][2006,2013][2007, 2013][2008, 2013] Matching Selection Reasoning 11Anisa Rula
  • 12. [2007, 2013][2008, 2013] [ 2007 2013] Mapping Facts to Time Intervals - Reasoning 3. Interval merging via reasoning based on Allen’s algebra relation <Alexander_Pato,playsFor, A.C._Milan> Matching Selection Reasoning 12Anisa Rula
  • 13. Experimental Setup - Dataset Dataset # facts Domain Property Equivalent Property Freebase Yago2 DBpedia 1000 Sport team team playsFor DBpedia 1000 Politicians office government_positions_held holdsPoliticalPosition DBpedia 500 Celebrities spouse spouse ismarriedTo Dataset: 2500 DBpedia triples with semantic equivalent triples in Freebase and Yago2 Gold standard: triples annotated with temporal scopes in Yago2  manually curated to correct missing or wrong values Anisa Rula 13
  • 14. Experimental Setup - Evaluation Measures The evaluation measures capture the degree of overlap between the retrieved intervals and the intervals in the gold standard  Precision (for a triple): number of time points in the temporal scope that fall into the time interval in the gold standard  Recall (for a triple): number of time points in the gold standard that are covered by the temporal scope  F1 measure (for a triple): the harmonic mean of precision and recall  Macro-averaged F1 (avgF-1): aggregated measure for a set of triples 14Anisa Rula 2007 2011 2008 2010 2007 2011 2006 2012 2007 2011 2007 2011 F1=1F1=0.83F1=0.75 Ref R
  • 15. Temp prop DBpedia Freebase TemporalDeFacto Config #facts avgF1 Config #facts avgF1 Config #facts avgF1 playsFor top-1 loc 264 0.505 top-1 loc 213 0.477 top-3 311 0.511 holdsPolitica lPosition neigh-10 702 0.699 neigh-10-2 242 0.549 top-3 709 0.586 ismarriedTo neigh-10 702 0.600 neigh-10 524 0.547 top-3 709 0.545  Good quality of the approach with an avgF1 of up to 70%  Using evidence from RDF documents the performance can be significantly improved (significantly better results for two properties and negligibly worst results for one property) Experimental Results - Accuracy of Best Configurations for all Properties  Different sources for the creation of the RIM  Setup different configurations in the selection and reasoning steps: o E.g. config top-3 refers to selection function top-3 and reasoning = yes 15Anisa Rula
  • 16. Temp prop Source Configuration With reasoning Without reasoning #fact avgF1 #fact avgF1 playsFor TempDeFacto top-3 311 0.511 505 0.467 holdsPoliticalPosition DBpedia neigh-10 702 0.699 822 0.667 ismarriedTo DBpedia neigh-10 705 0.600 977 0.563  The best results are obtained when reasoning is enabled Experimental Results - Accuracy with vs. without Reasoning for all Properties  The best configurations for the three properties 16Anisa Rula
  • 17. Conclusions & Future Work Summary  Temporal extension of the DeFacto framework  Modeling a space of relevant time intervals given an RDF triple  Mapping volatile facts to time intervals based on a three-phase algorithm  Unsupervised method Future work  Determine when to add or not to add the temporal scope based on the confidence of the acquisition process  Collect additional relevant time points to improve the overall results  Show the effectiveness of acquired temporal scopes in temporal query answering 17Anisa Rula
  • 18. Thank you for your attention Question? #eswc2014Rula 18Anisa Rula
  • 19. References  [Rula&2012] Anisa Rula, Matteo Palmonari, Andreas Harth, Steffen Stadtmüller, Andrea Maurino: On the Diversity and Availability of Temporal Information in Linked Open Data. International Semantic Web Conference (1) 2012: 492-507  [Gutiérrez&2005] C. Gutierrez, C. A. Hurtado, and A. A. Vaisman. Temporal RDF. In The 2ndESWC, pages 93-107, 2005  [Lehmann&2012] Jens Lehmann, Daniel Gerber, Mohamed Morsey, Axel-Cyrille Ngonga Ngomo: DeFacto - Deep Fact Validation. International Semantic Web Conference (1) 2012: 312-327  [Ling&2010] X. Ling and D. S. Weld. Temporal information extraction. In 25th AAAI, 2010.  [Derczynsk&2013] L. Derczynski and R. Gaizauskas. Information retrieval for temporal bounding. In 4th ICTIR, pages 29:129–29:130. ACM, 2013. 19Anisa Rula