Ontology-based Interpretation of Keywords for Semantic Search

1. Ontology-based Interpretation of Keywords for Semantic Search Author: Thanh Tran, Philipp Cimiano, Sebastian Rudolph and Rudi Source: ISWC2007 Reporter: Yong-Xiang Chen

2. Background • Semantic Web • understanding users’ information needs • retrieving a precise answer using semantic technologies • most web search engines are based on purely statistical techniques • not able to figure out the meaning of a query • Translation of natural language questions to formal queries using an ontology • Does User always need to specify a full natural language question?? • It is important to develop approaches which are able to interpret keywords • Impose a query syntax is more difficult to handle in semantic search

3. Purpose • This paper present an approach for translating keyword queries to DL conjunctive queries using background knowledge available in ontologies • For understanding users’ information needs

4. Answering User Queries in Ontology-based Systems • Process of ontology-based IR • The user poses a question to the system • The system answers the question • using knowledge formalized in a logical language • Consisting of • 4 models • 2 assumptions

5. Models Mental Model Ou • The mental model Ou corresponds to the information need that a user has in mind at the beginning • Ou be conceived as a set of (thought) entities • relevant for the current information need • embedded in an association structure • might be • related to real world objects • or to more abstract concepts • The entities in this association structure = what the user knows

6. Models The User Question Model Qu • Consists of elements constructed out of language primitives Pu of a language Lu (language of the user) • User translating elements in Ou to elements in Pu • So, elements in Qu explicitly denoting gaps • Between mentalmodel and language model

7. Models The System Resource Model Os • Consists of elements constructed out of • language primitives Ps of a formal KR language Ls (language of the system) • Independent from a concrete formal language used • These elements can be conceived as a set of entities of a given ontology • Entities and structure of Os are explicitly given and directly accessible

8. Models The System Query Model QS • Represents the final question processed by the formal query engine of the system • Consists of elements constructed out of • language primitives P’s of a query language L’s • A formal semantics for L’s (query language of the system), it must be compatible with the semantics of Ls • Some elements in Qs must correspond to elements in Os • the query language L’s may have primitives additional to the ones available in Ls • there must be primitives to specify the gaps, e.g. variables

9. The more the entities and structure in Ou match the entities and structure in Os , • the higher the chance that Os can be used by the system to fill the gaps • to answer the query • the more related the syntax and semantics of Lu and Ls , the more straightforward is the mapping from Qu to Qs • the interpretation of the user query

10. Translating the user question to the system query • Relies on the knowledge in the KB • Assumption (A1) — Ontology-Mental Correspondence • Assumption (A2) — Locality of Information Need

11. A1: Ontology-Mental Correspondence • Elements and the associative structure in Ou correspond to ontology entities and the structure in Os

12. A2: Locality of Information Need • Those ontology entities O’s include in Os that correspond to entities in the mental information need representation Ou • be connected over a maximum distance d • For any two ontology entities • a direct connection <a, b> • a sequence of xi such that a = x0 and <x0, x1>, <x1, x2>, . . . , <xn−1, xn>, and <xn, b> and n < d • Several such sequences that connect gaps with the two entities the user knows (a, b) • there is a maximum distance • connections over smaller distances are more likely to contain the gaps that the user looks for

13. Users can not be assumed to fully think in term of ontological structures or in any KR language • Assume that they think in some structures which can be mapped to an ontology • A2 helps to restrict attention to only a particular part of the ontology

14. Interpreting the User Question • Translate Qu to Qs which consists of three high level steps • The elements in the user question Qu are mapped to ontology elements from Os • Further ontology elements are explored • to better cover the initial information need in the mental model Ou • Qs then be derived

15. Step 1 • Not all elements of the user question can be mapped to corresponding ontology elements • Therefore, the identified ontology elements O’s yet do not account for the entire mental model • Need to “reconstruct” the mental model and find out the gaps

16. Step 2 • Explore connections among O’s identified in step one using further elements in Os • only elements within a specified range (maximum distance d) have to be considered • Discovered elements in Os combined with O’s can be assumed to approximate the user’s mental model

17. Step 3 • Elements need to be assembled into a formal query in the language L’s • Qs • formal queries are specified using ontology elements (the information part) • formal queries contain variables (the question part) • Os • not contain variables • The identified elements O’s map to the information part • The discovered elements may correspond to thought entities the user knows • but has not explicitly specified in the question • They might correspond to gaps • After above elements map to the information part • Entities the user does not know but corresponding to the answer the user looks for • map to variables of the question part

18. Illustrating Example • A user wants to retrieve all publicationsauthored by Philipp Cimiano which are associated to the projectX-Media • Qu = "Philipp Cimiano X-Media publications“ • Mapped to the ontology elements • Philipp Cimiano • X-Media • Publication • Not fully correspond to the information need of the user • Cannot be assembled into a system query

20. Find out missing elements • In the mental model the user knows but does not specify • the connection is-a between X-Media and project • Philipp Cimiano and publication connected in the ontology via the author relation • Missing elements = gaps • User does not make explicit the connection hasProject between X-Media and publication while for sure s/he was thinking of it • Some of these gaps correspond to the information the user is looking for

21. Exploration • Starts the KB exploration from the individual Philipp Cimiano and leads to the relations author and is-a • Assuming the exploration width is 2 • reach the elements Pub#1 • researcher from Philipp Cimiano • X-Media reach the relations hasProject, is-a as well as the elements project ,Pub#1 and Pub#2 • Publication reach the relations is-a as well as the elements Pub#1

22. Find subgraphs and rank queries • The (possibly many) subgraphs which connect these elements are computed • Subgraphs correspond to the different questions the user possibly has • A2 allows to rank queries • it postulates that connections over smaller distances are more likely to contain the answer the user looks for

23. Translation • The graph is translated into a corresponding query • Qs = <x,Philipp Cimiano>:nameΛ<x,y>:authorΛ<y,z>:hasProjectΛ<z,X-Media>:nameΛ<y:publication> • The mapping from the graph elements to the information part and question part of the system queries depends on the query syntax of Qs as well as the specific elements identified and explored in Os

24. Implementation • The approach is integrated in our system called XXploreKnow! • The user enters keywords which are processed by the Lucene search engine • Ontology entities returned by this engine enter the exploration process • As a result, the system visualizes a subgraph connecting the matched entities to the user • User clicking on the “search" or “xxplore" button, subsequent interactions consist of either further exploration of the graph or inspection of the search results

25. Evaluation • Real queries • 12 colleagues at the institute AIFB provide queries • Removed • Some queries which were obviously out of the scope of the knowledge base were removed • total of 42 different queries be used

26. Examples • "projects Blohm" • Retrieve all projects that Sebastian Blohm is working on • "phone Rudi Studer" • Retrieve the phone number of Rudi Studer • "publications SmartWeb Pascal Hitzler 2002“ • Retrieve all publications published by Pascal Hitzler within SmartWeb in 2002

27. One of the authors manually assigned conjunctive queries according to the natural language description • A query generated by our approach is regarded as correct if it retrieved the same answers as the hand crafted query • In case the query is selected by hand from the different queries generated, • P = 85%, R = 52% and a F-Measure F1 = 64% • In case automatically choose the highest ranked question • P = 69%, R = 43% and F1 = 53%

28. Discussion • The knowledge base from which our institute portal is automatically generated • The underlying ontology is the SWRC ontology for the representation of researchers, their publications, active projects • At least about half of the keyword queries can be mapped to appropriate ontology elements • The higher precision shows that the generated query is correct in most cases • Most of the errors in our approach are produced in step 1 • the Lucene engine does not return the appropriate ontology elements in some cases