1. Type-directed Topic Segmentation of Entity Descriptions
龚赛赛

2. Contents
Background and Motivation
Related Work
Method Framework

3. Background
The descriptions of an entity in Linked Data are usually about more than one topic
e.g. family and academic info of a researcher
Topic segmentation: split entity descriptions into a sequence of topically coherent segments
Useful for many tasks, including entity browsing and entity summarization

4. Background
Manually segmentation spends humans large amount of time and energy
Several works leverage a variety of cues for automatic segmentation
e.g., Property name relatedness, property value overlap, relatedness derived from property axioms, distributional relatedness

5. Motivation
Existing works mainly follow the paradigm: firstly characterize relatedness between/among descriptions using various measures, and then derive segmentations by using clustering algorithms
The performances rely heavily on the setting of the clustering algorithms and remain far from perfect

6. Motivation Entity types contain important cues for segmentation
E.g. types of Arnold Schwarzenegger: person, politician, artist, body builder, etc.
In our work, we propose a new approach to text segmentation for entity browsing, which use the entity types to guide segmentation
Select a subset of entity types that are sensible, have a high coverage rate of descriptions, and allocate entity descriptions to these types to form the initial segmentations
Split the large segmentations in the initial ones

7. Related work Various property relatedness measures
Manually segmentation
Haystack, Marble, Fresnel
Spend users a lot of time and energy
Various property relatedness measures
E.g. Property name, wordnet based, wikipedia based, search engine based, value overlap, property axiom based, and so on
Cues to determine segmentation
Lloyd Rutledge et al1. mainly use property value overlap and formal concept analysis to generate
FACES2 use topic segmentation to improve entity summarization
Making RDF Presentable. www
FACES: Diversity-Aware Entity Summarization using Incremental Hierarchical Conceptual Clustering. aaai

8. Method Framework Entity description: a property with its value set
Segmentation: a set of descriptions
Given a set of entity descriptions of an entity , get a sequence of segmentations

9. Method Framework Cover: a property’s domain is superclass of a type
Sensible subset with high coverage
The size of subset is limited, at most k
A type covering suitable number of properties is preferred, i.e. 1/k
A type in the deeper position of the type hierarchy is preferred
Each property is allocated to a type covering it

10. Method Framework EBMC Property weight:1
The grade of membership: based on the distance of the property domain and the type

11. Method Framework Split large initial segmentations
Linear combination of measures and clustering
Measures: property name I-sub, wordnet relatedness, property value overlap, distributional relatedness
Clustering algorithm: DBSCAN