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Navi 下一步工作的设想

Navi 下一步工作的设想. 郑 亮 6.6. LOD Cloud. Knowledge Graph. Motivation. When browsing an entity or a set of entities in the Semantic Web, it is important to improve the efficiency of human navigation and help people find the information they need as fast as possible.

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Navi 下一步工作的设想

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  1. Navi 下一步工作的设想 郑 亮 6.6

  2. LOD Cloud

  3. Knowledge Graph

  4. Motivation • When browsing an entity or a set of entities in the Semantic Web, it is important to improve the efficiency of human navigation and help people find the information they need as fast as possible. • Our study aims to not only recommending the related entities, but also understanding how people reach these entities by navigating through the Semantic Web.

  5. Relate d Work • Navigating in Information Networks • User click-trail analysis • Network structure analysis

  6. Recommended Papers • West R, Paranjape A, Leskovec J. Mining missing hyperlinks from human navigation traces: A case study of wikipedia. www2015: 1242-1252. • West R, Leskovec J. Human wayfinding in information networks. www2012: 619-628. • Blanco R, Cambazoglu B B, Mika P, et al. Entity Recommendations in Web Search. ISWC2013. • Antikacioglu A, Ravi R, Sridhar S. Recommendation Subgraphs for Web Discovery. www2015.

  7. 1. Human Wayfinding in Information Networks [West 2012] Stanford University • Task：How do humans navigate information networks? • How to do? • Understand how humans navigate Wikipedia Get an idea of how people connect concepts. • Study more than 30,000 goal-directed human search paths and identify strategies people use when navigating information spaces. • Apply the lessons learned, in order to design a learning algorithm for predicting an information seeker’s target, given only a prefix of a few clicks.

  8. Example path

  9. Data collection via a game:Wikispeedia.net http://cs.mcgill.ca/~rwest/wikispeedia/ More than 30,000 instances (the data came from around 9,400 distinct IP addresses).

  10. How good are humans at finding such short chains?

  11. Elements of Human Wayfinding • Anatomy of typical paths • Making progress is easiest far from and close to the target. • Hubs are crucial in the opening. • Conceptual distance to the target decreases steadily • Big leaps first, followed by smaller steps. • Clicks are most predictable far from and close to the target • Two main strategic elements • Degree-based: Navigate to hub • Similarity-based: Get ever closer to target in terms of semantic distance

  12. Target Prediction • Our next goal is to apply the lessons learned, in order to design a learning algorithm for predicting an information seeker’s target, given only a prefix of a few clicks. • Our method explicitly takes the characteristic features of human search into account and is trained on real human trajectories. • We cast our task as a ranking problem. Given the observed path prefix q, rank all articles t according to how plausible they are as targets of the current search.

  13. Learning-to-rank model

  14. 2. Mining Missing Hyperlinks from Human Navigation Traces[West 2015]

  15. Task • Navigation logs for mining missing links. • If we often observe users going through page s and ending up in page t, although s does not directly link to t, then it might be a good idea to introduce a ‘shortcut’ link from s to t.

  16. Ranking by relatedness • It seems reasonable to rank source candidates s by their relatedness to t, since clearly a link is more relevant between articles with topical connections • Ranking by path frequency

  17. 3. Entity Recommendations in Web Search [Blanco, 2013] • Task • Given the large number of related entities in the knowledge base, we need to select the most relevant ones to show based on the current query (entity) of the user. • Approach • Entity Recommendation task  Ranking task • For every triple in the knowledge base, Spark extracts over 100 features (co-occurrence, popularity, and graph-theoretic features(PageRank),…).

  18. 3. Entity Recommendations in Web Search [Blanco, 2013]

  19. 4. Recommendation Subgraphs for Web Discovery [Antikacioglu 2015] Carnegie Mellon University … … L: the set of discovered items R: the set of undiscovered

  20. Our Approach T : the related entities path • User click-trail  Meta-Paths • Learning to rank … s : current entity l: the length of path

  21. Thanks! • Q&A

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