Difference between revisions of "Domain Specific Graph Selection From Linked Open Data"
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==Abstract== | ==Abstract== | ||
| − | With the Linked Open Data (LOD) initiative, there has been a great deal of work in developing a variety of open knowledge graphs freely accessible on the Web such as DBPedia, Yago and Freebase. Lately, the focus is moving greatly towards in leveraging the knowledge graphs as a background source for various applications. Knowledge graphs can provide a valuable source of information to improve tasks such as recommendations, (semantic) similarity calculation and named entity disambiguation. One of the main usage of a knowledge graph for these applications is to find out how entities are related to each other. For example, a movie recommendation system is interested in finding out how movies are related to provide | + | With the Linked Open Data (LOD) initiative, there has been a great deal of work in developing a variety of open knowledge graphs freely accessible on the Web such as DBPedia, Yago and Freebase. Lately, the focus is moving greatly towards in leveraging the knowledge graphs as a background source for various applications. Knowledge graphs can provide a valuable source of information to improve tasks such as recommendations, (semantic) similarity calculation and named entity disambiguation. One of the main usage of a knowledge graph for these applications is to find out how entities are related to each other. For example, a movie recommendation system is interested in finding out how movies are related to provide recommendations based on the implicit feedback. Existing approaches consider the neighborhood graph within predefined number of hops for this purpose. But, given a particular domain not all properties and entities are relevant. Given the movie domain, Military Unit and Death Place of a movie director will have a significantly less importance compared to his Movies and Awards. In this work, we propose a schema driven approach to select the domain specific sub graph by taking PMI as the degree of association to rank the properties and classes of the neighbourhood graph in the context of the domain (e.g movie). We evaluate our approach in the movie domain with DBPedia knowledge graph to show the effectiveness our approach for movie recommendation. |
==Contribution== | ==Contribution== | ||
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==Approach== | ==Approach== | ||
| + | Proposed approach exploits the schema of the knowledge graph with the instance | ||
| + | We use the PMI as a measure to rank the properties in the context of the domain of interest. find the co occurrence of properties with the type (eg:- movie) | ||
| + | |||
| + | ==Other== | ||
| + | * As of now, we consider P(p<sub>2i</sub>, t<sub>1</sub> p<sub>1</sub> t<sub></sub>) and need to analyze whether we should consider P(p<sub>2i</sub>, p<sub>1</sub>) | ||
| + | * We need to justify our approach versus semantic similarity | ||
Revision as of 21:08, 6 January 2015
Contents
Abstract
With the Linked Open Data (LOD) initiative, there has been a great deal of work in developing a variety of open knowledge graphs freely accessible on the Web such as DBPedia, Yago and Freebase. Lately, the focus is moving greatly towards in leveraging the knowledge graphs as a background source for various applications. Knowledge graphs can provide a valuable source of information to improve tasks such as recommendations, (semantic) similarity calculation and named entity disambiguation. One of the main usage of a knowledge graph for these applications is to find out how entities are related to each other. For example, a movie recommendation system is interested in finding out how movies are related to provide recommendations based on the implicit feedback. Existing approaches consider the neighborhood graph within predefined number of hops for this purpose. But, given a particular domain not all properties and entities are relevant. Given the movie domain, Military Unit and Death Place of a movie director will have a significantly less importance compared to his Movies and Awards. In this work, we propose a schema driven approach to select the domain specific sub graph by taking PMI as the degree of association to rank the properties and classes of the neighbourhood graph in the context of the domain (e.g movie). We evaluate our approach in the movie domain with DBPedia knowledge graph to show the effectiveness our approach for movie recommendation.
Contribution
- Propose a method to create a domain specific subgraph given the DBpedia type to represent the domain
- Evaluate the domain specific subgraph in the movie domain, to see its effectiveness for movie recommendation
Approach
Proposed approach exploits the schema of the knowledge graph with the instance We use the PMI as a measure to rank the properties in the context of the domain of interest. find the co occurrence of properties with the type (eg:- movie)
Other
- As of now, we consider P(p2i, t1 p1 t) and need to analyze whether we should consider P(p2i, p1)
- We need to justify our approach versus semantic similarity
