国家重点研发计划(2018YFB1004300)
国家自然科学基金(61772264)
[1] Zhang L, Zhang Y, Chen Y. Summarizing highly structured documents for effective search interaction. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2012. 145--154. Google Scholar
[2] Yan J, Wang Y, Gao M, Zhou A. Context-aware entity summarization. In: Proceedings of the 17th Internal Conference on Web-Age Information Management, Part I. Switzerland: Springer, 2016. 517--529. Google Scholar
[3] Hasibi F, Balog K, Bratsberg S E. Dynamic factual summaries for entity cards. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2017. 773--782. Google Scholar
[4] Cheng G, Xu D, Qu Y. Summarizing entity descriptions for effective and efficient human-centered entity linking. In: Proceedings of the 24th International Conference on World Wide Web. New York: ACM, 2015. 184--194. Google Scholar
[5] Cheng G, Xu D, Qu Y. C3D+P: A summarization method for interactive entity resolution. J Web Semantics, 2015, 35: 203-213 CrossRef Google Scholar
[6] Cheng G, Tran T, Qu Y. RELIN: relatedness and informativeness-based centrality for entity summarization. In: Proceedings of the 10th International Semantic Web Conference, Part I. Berlin: Springer, 2011. 114--129. Google Scholar
[7] Sydow M, Piku?a M, Schenkel R. The notion of diversity in graphical entity summarisation on semantic knowledge graphs. J Intell Inf Syst, 2013, 41: 109-149 CrossRef Google Scholar
[8] Gunaratna K, Thirunarayan K, Sheth A P. FACES: diversity-aware entity summarization using incremental hierarchical conceptual clustering. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. California: AAAI Press, 2015. 116--122. Google Scholar
[9] Gunaratna K, Thirunarayan K, Sheth A P, et al. Gleaning types for literals in RDF triples with application to entity summarization. In: Proceedings of the 13th European Semantic Web Conference. Switzerland: Springer, 2016. 85--100. Google Scholar
[10] Xu D, Zheng L, Qu Y. CD at ENSEC 2016: generating characteristic and diverse entity summaries. In: Proceedings of the 2nd International Workshop on Summarizing and Presenting Entities and Ontologies. Ruzica Piskac: ESUR-WS.org, 2016. Google Scholar
[11] Thalhammer A, Lasierra N, Rettinger A. LinkSUM: using link analysis to summarize entity data. In: Proceedings of the 16th International Conference on Web Engineering. Switzerland: Springer, 2016. 244--261. Google Scholar
[12] Stoilos G, Stamou G B, Kollias S D. A string metric for ontology alignment. In: Proceedings of the 4th International Semantic Web Conference. Berlin: Springer, 2005. 624--637. Google Scholar
[13] Pisinger D. The quadratic knapsack problem-a survey. Discrete Appl Math, 2007, 155: 623-648 CrossRef Google Scholar
[14] Yang Z, Wang G, Chu F. An effective GRASP and tabu search for the 0-1 quadratic knapsack problem. Comput Operations Res, 2013, 40: 1176-1185 CrossRef Google Scholar
[15] Thalhammer A, Toma I, Roa-Valverde A J, et al. Leveraging usage data for Linked Data movie entity summarization. 2012,. arXiv Google Scholar
[16] Li Y, Zhao L. A common property and special property entity summarization approach based on statistical distribution. In: Proceedings of the 2nd International Workshop on Summarizing and Presenting Entities and Ontologies. Ruzica Piskac: ESUR-WS.org, 2016. Google Scholar
[17] EventKG - the hub of event knowledge on the web - and biographical timeline generation. SW, 2019, 10: 1039-1070 CrossRef Google Scholar
[18] Tonon A, Catasta M, Prokofyev R. Contextualized ranking of entity types based on knowledge graphs. J Web Semantics, 2016, 37-38: 170-183 CrossRef Google Scholar
[19] Thalhammer A, Rettinger A. Browsing DBpedia entities with summaries. In: Proceedings of the 11th European Semantic Web Conference, Switzerland: Springer, 2014. 511--515. Google Scholar
[20] Jones KS. Automatic summarising: The state of the art. Inf Processing Manage, 2007, 43: 1449-1481 CrossRef Google Scholar
[21] Mihalcea R, Tarau P. TextRank: Bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2004. 404--411. Google Scholar
[22] Zhou Q, Yang N, Wei F, et al. Neural document summarization by jointly learning to score and select sentences. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2018. 654--663. Google Scholar
[23] Carbonell J G, Goldstein J. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 1998. 335--336. Google Scholar
[24] Ganesan K, Zhai C, Viegas E. Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. In: Proceedings of the 21st World Wide Web Conference. New York: ACM, 2012. 869--878. Google Scholar
[25] Liu Y, Safavi T, Dighe A, et al. Graph summarization methods and applications: a survey. ACM Comput Surv, 2018, 51(3): 62:1-62:34. Google Scholar
Figure 1
An example of knowledge graph (ovals and rectangles represent entities/classes and literals, respectively)
Figure 2
(Color online) Cumulative distribution of Pearson correlation coefficient between weights and ideal importance scores. (a) $W_{\rm~struct}$; (b) $W_{\rm~text}$
Figure 3
Summaries generated by entity summarizers for entity Hagar Wilde along with their F-measure scores.
${\rm~ovlp}(t_i,~t_j)~\Leftarrow~1$;ELSIF${\rm~val}(t_i)={\rm~val}(t_j)$ and ($\texttt{subPropertyOf}({\rm~prop}(t_i),~{\rm~prop}(t_j))$ or $\texttt{subPropertyOf}({\rm~prop}(t_j),~{\rm~prop}(t_i))$) |
${\rm~ovlp}(t_i,~t_j)~\Leftarrow~1$; |
${\rm~sim}_p(t_i,~t_j)=\texttt{ISub}({\rm~prop}(t_i),~{\rm~prop}(t_j))$; |
|
|
${\rm~sim}_v(t_i,~t_j)~\Leftarrow~1$;ELSIF${\rm~val}(t_i)\cdot~{\rm~val}(t_j)\leq~0$ |
${\rm~sim}_v(t_i,~t_j)~\Leftarrow~-1$; |
|
${\rm~sim}_v(t_i,~t_j)~\Leftarrow~\frac{{\rm~min}\{\|{\rm~val}(t_i)\|,~\|{\rm~val}(t_j)\|\}}{{\rm~max}\{\|{\rm~val}(t_i)\|,~\|{\rm~val}(t_j)\|\}}$; |
|
|
${\rm~sim}_v(t_i,~t_j)~\Leftarrow~\texttt{ISub}({\rm~val}(t_i),~{\rm~val}(t_j))$; |
|
${\rm~ovlp}(t_i,~t_j)~\Leftarrow~\max\{{\rm~sim}_p(t_i,~t_j),~{\rm~sim}_v(t_i,~t_j),~0\}$; |
| ESBM-D | ESBM-L | FED | |
| RELIN | 0.242 ${(0.120)}$ | 0.203 ${(0.125)}$ | 0.127${(0.085)}$ |
| DIVERSUM | 0.249 ${(0.136)}$ | 0.207 ${(0.127)}$ | 0.112 ${(0.078)}$ |
| FACES | 0.270 ${(0.144)}$ | 0.169 ${(0.085)}$ | 0.145 ${(0.089)}$ |
| FACES-E | 0.280 ${(0.142)}$ | 0.313 ${(0.116)}$ | 0.145 ${(0.089)}$ |
| CD | 0.283 ${(0.134)}$ | 0.217 ${(0.101)}$ | 0.136 ${(0.076)}$ |
| LinkSUM | 0.287 ${(0.132)}$ | 0.140 ${(0.101)}$ | 0.239 ${(0.121)}$ |
| ESSTER | 0.305 ${(0.132)}$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\vartriangle$tiny$^\circ$tiny$^\circ$ | 0.347 ${(0.077)}$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\vartriangle$tiny$^\blacktriangle$tiny$^\blacktriangle$ | 0.229 ${(0.118)}$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\blacktriangle$tiny$^\circ$ |
a) Significant improvements of ESSTER over each baseline are indicated by $\blacktriangle$ ($p<0.01$) and $\vartriangle $ ($p<0.05$). Insignificant differences are indicated by $\circ$.
| ESBM-D | ESBM-L | FED | |||||||
| Mean | diff | $p$ | Mean | diff | $p$ | Mean | diff | $p$ | |
| ESSTER | 0.305 | – | – | 0.347 | – | – | 0.229 | – | – |
| ESSTER-S | 0.264 | $-$0.041 | 0.000 | 0.247 | $-$0.101 | 0.000 | 0.140 | $-$0.089 | 0.000 |
| ESSTER-T | 0.298 | $-$0.007 | 0.489 | 0.305 | $-$0.042 | 0.001 | 0.218 | $-$0.011 | 0.167 |
| ESSTER-R | 0.222 | $-$0.083 | 0.000 | 0.325 | $-$0.022 | 0.025 | 0.211 | $-$0.019 | 0.042 |
| ESBM-D | ESBM-L | FED | |||
| Highest | Lowest | Highest | Lowest | Highest | Lowest |
| time | draft year | made | link source | other | population blank1 title |
| long | debut team | subject | filmid | before | timezone DST |
| order | IMDB id | country | story contributor | after | computing media |
| number | type of tennis surface | date | film story contributor | years | demonym |
| course | siler medalist | language | music contributor | order | sovereignty type |
| name | UTC offset | type | director directorid | name | languages2 type |
| subject | NRHP reference number | page | director name | state | cctId |
| added | route type abbreviation | title | director nytimes id | parts | location country |
| result | serving railway line | writer | actor name | ground | państwo |
| position | bionomial authority | performance | actor Netflix id | country | flaglink |
| ESBM-D | ESBM-L | FED | |
| Desc | 203.69 | 431.99 | 140.78 |
| Ideal | 1.04 | 1.84 | 0.89 |
| RELIN | 3.04 | 3.45 | 2.22 |
| DIVERSUM | 0.39 | 1.29 | 1.64 |
| FACES | 0.75 | 0.30 | 1.05 |
| FACES-E | 1.29 | 0.76 | 1.05 |
| CD | 0.02 | 0.00 | 0.00 |
| LinkSUM | 2.45 | 4.72 | 1.47 |
| ESSTER | 0.02 | 1.17 | 1.69 |
Download PDF
