A Graph Database Model for Knowledge Extracted from Place Descriptions
Abstract
:1. Introduction
 The identification of eight types of information that are either embedded in place descriptions or in external context and have not been captured by the original place graph model.
 An extended place graph model that represents such information and enables future tracing as well as querying.
 The implementation of the extended place graph into a graph database management system, which allows operations including querying, visualizing, and mapping.
 The demonstration of how the extended model overcomes limitations of the original place graph in georeferencing, reasoning, and querying tasks based on three experiments.
2. Related Work
2.1. Place as a Cognitive Concept
2.2. Place Models from an Information System Perspective
2.3. Modelling Place Descriptions
3. Extending the Place Graph Model
3.1. Information not Captured in the Original Place Graph Model
3.1.1. Place Semantics and Characteristics
3.1.2. Places and Relationships from Discourse and Their Sequential Order of Appearance
3.1.3. Reference Frame and Direction
3.1.4. NonBinary Relationships
3.1.5. Number of Occurrences of Place References and Spatial Relationships
3.1.6. Conceptualization of Places
3.1.7. Route and Accessibility
3.1.8. Description Context and Source Context
3.2. The Extended Place Graph Database Model
3.2.1. Place Reference Node
3.2.2. NPlet Node
“… coming from the Main South Entry, the Baillieu Library will be on the left hand side of the South Lawn …”
3.2.3. Place Node
3.2.4. Route Node
3.2.5. Spatial Relation Node
3.2.6. Description Node
3.2.7. User Node
3.3. Summary
4. Implementation and Experiments
4.1. Data Overview and Construction of the Test Place Graph Database
“… If you go into the Old Quad, you will reach a square courtyard and at the back of the courtyard. You can either turn left to go to the Arts Faculty Building, or turn right into the John Medley Building and Wilson Hall. Raymond Priestly Building is the open aired ground area which is in front of Wilson Hall that is adjacent to it. Towards North, which is when you turn left when exiting the Old Quad, you will see Union House where there are shops selling foods. If you continue walk along the road on the right side where you’re facing Union House, you can see the Beaurepaire and Swimming Pool. There will also be a sport tracks and university oval behind it …”
{"descriptions": [ … {"did": 1, "nplets": [{"nid": 2, "locatum␣reference": "Baillieu Library", "spatial␣relation␣expression": "on the left hand side", "relatum␣reference": "South Lawn", "reference␣frame": "relative", "reference␣direction": ["Main South Entry", "back"], "relation␣map": ["left"], …}, {…}, …] }, {…}, … ]}
4.2. Experiment I: Locating Places Without Gazetteered References
4.3. Experiment II: Relational Consistency Reasoning Using Reference Direction Information
Algorithm 1 Consistency reasoning of directional relationships. 
Input:$\mathit{place}\_\mathit{graph}$ Output:$\mathit{inconsistent}\_\mathit{pairs}$

4.4. Experiment III: Spatial Knowledge Querying
 Find the most frequently referred to relatum (landmarks).
 Find places that are most frequently linked to a specific place by spatial relations (place relevance by cooccurrence).
 Find the most frequent paths of length three, consisting of only directional relationships, i.e., place Arelation a>place Brelation b>place C (prominent routes)
MATCH (p:place)>(:place_reference)[r {as: ’relatum’}]>(:n_plet), RETURN p, count(r) AS c ORDER BY c DESC
MATCH (p:place)[*2]>(n:n_plet)<({place_reference: ’Alice Hoy’})<(:place) RETURN p, count(n) AS c ORDER BY c DESC
MATCH path=(p1:place)[*2]>(n1:n_plet)<[*2](p2:place)[*2]>(n2:n_plet)<[*2](p3:place), (n1)>(r1:spatial_relation), (n2)>(r2:spatial_relation), (n1)>(:description)<(n2) WHERE r1.family in [’cardinal_direction’, ’relative_direction’] and r2.family in [’cardinal_direction’, ’relative_direction’] RETURN p1, r1, p2, r2, p3, count(path) AS c ORDER BY c DESC
5. Results and Discussion
 The size of the ALR is reduced compared to the one from the baseline, but both ALRs capture the groundtruth location of the place (Case 1).
 There is no change in the ALR’s size (Case 2).
 The groundtruth location is not captured in the either ALR (Case 3).
 The groundtruth location is captured by the ALR of the baseline method, but not in the reducedsize ALR (Case 4).
“… You’re now in the Old Quad … Pass through the Old Arts building and immediately look to your left—the tall building is the Babel building that, somewhat ironically, houses the languages and linguistics departments …”
“… From the Old Quad, you can go through the Old Arts building, and then turn right and walk until you come to a building called the Babel building (a 1970s yellow brick monolith) …”
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A. Graph Construction Example
“South Lawn is the major reference point which is situated in about the middle of the campus. Coming from the Main South Entry, the Baillieu Library will be on the left hand side of the South Lawn. To the north of this you have the Old Quad (really old English style building). If you want food and are currently on South Lawn go through the Old Quad to the north and keeping heading north until you get to a Union House.”
{"descriptions": [ {"did": 1, "theme": ["University of Melbourne campus description"], "transportation␣mode": "walking", "source": "collected from students", "descriptor": {"uid": 1, "identity": "university student"}, "recipient": {"uid": 2, "identity": "university student"}, "n␣plets": [{"nid": 1, "locatum␣reference": "South Lawn", "locatum␣characteristic": "the major reference point", "locatum␣conceptualization": "node", "spatial␣relation␣expression": "about the middle of", "relatum␣reference": "campus", "relatum␣conceptualization": "district", "relation␣map": ["inside"]}, {"nid": 2, "locatum␣reference": "Baillieu Library", "locatum␣conceptualization": "node", "spatial␣relation␣expression": "on the left hand side", "relatum␣reference": "South Lawn", "relatum␣conceptualization": "node", "reference␣frame": "relative", "reference␣direction": ["Main South Entry", "back"], "relation␣map": ["left"]}, {"nid": 3, "locatum␣reference": "Old Quad", "locatum␣type": "building", "locatum␣characteristic": ["old", "English style"], "locatum␣conceptualization": "node", "spatial␣relation␣expression": "to the north", "relatum␣reference": "South Lawn", "relatum␣conceptualization": "node", "relation␣map": ["north"]}, {"nid": 4, "route_id": 1, "locatum␣reference": "Union House", "locatum␣equipment": "food", "locatum␣conceptualization": "node", "spatial␣relation␣expression": "go through", "relatum␣reference": ["Old Quad", "South Lawn"], "relatum␣conceptualization": ["node", "node"], "relation␣map": ["through"]}, {"nid": 5, "route_id": 1, "locatum␣reference": "Union House", "locatum␣conceptualization": "node", "spatial␣relation␣expression": "to the north and keeping heading north", "relatum␣reference": "Old Quad", "relatum␣conceptualization": "node", "relation␣map": ["north"]}] } ]}
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Spatial Relation Family  Spatial Relation Type 

Cardinal direction  north, south, east, west, northeast, southeast, northwest, southwest 
Qualitative distance  near 
Relative direction  front, back, left, right, left front, right front, left back, right back 
Topological  inside, covered by, overlap, meet, disjoint, cover, contain, equal 
Rank  Most Frequent Relata  Places Most Frequently CoOccurring with Alice Hoy  Most Frequent Length3 Paths 

1  University Of Melbourne  Monash Road  <Old Arts, right, Baillieu Library, left, South Lawn> 
2  Union Building  Entrance  <Baillieu Library, left, South Lawn, front, John Medley> 
3  Grattan Street  University of Melbourne  <Royal Parade, left, Baillieu Library, left, South Lawn> 
4  South Lawn  Wilson Hall  <Medical Building, left, Baillieu Library, left, South Lawn> 
5  Swanston Street  Peter Hall Building  <Baillieu Library, left, South Lawn, left, Wilson Hall> 
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Chen, H.; Vasardani, M.; Winter, S.; Tomko, M. A Graph Database Model for Knowledge Extracted from Place Descriptions. ISPRS Int. J. GeoInf. 2018, 7, 221. https://doi.org/10.3390/ijgi7060221
Chen H, Vasardani M, Winter S, Tomko M. A Graph Database Model for Knowledge Extracted from Place Descriptions. ISPRS International Journal of GeoInformation. 2018; 7(6):221. https://doi.org/10.3390/ijgi7060221
Chicago/Turabian StyleChen, Hao, Maria Vasardani, Stephan Winter, and Martin Tomko. 2018. "A Graph Database Model for Knowledge Extracted from Place Descriptions" ISPRS International Journal of GeoInformation 7, no. 6: 221. https://doi.org/10.3390/ijgi7060221