Abstract
Semantic associations are direct or indirect linkages between two entities that are construed from existing associations among entities. In this paper we extend our previous query language approach for discovering semantic associations with an ability to retrieve semantic associations that, besides explicitly stated (base) associations, may contain associations derived using logic-based derivation rules. As will be shown, this makes it possible to find semantic associations that are both compact and intuitive. To implement this new feature, we introduce a rewriting principle that utilizes derived associations to reduce resulting semantic associations if possible. Other proposed means to assist the interpretation of query results include answer expansion and the ordering of answers. The incorporated answer expansion feature lets the user investigate rewritten semantic associations in a query result at the desired level of detail. The ordering of answers is based on the lengths of the resulting semantic associations, whereby priority is given to shorter semantic associations which often express close and relevant relationships.
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Adibi, J., Cohen, P. R., & Morrison, C. T. (2004). Measuring confidence intervals in link discovery: A bootstrap approach. In Proceedings of the 10th ACM special interest group on knowledge discovery and data mining. New York: ACM.
Aleman-Meza, B., Halaschek, C., Arpinar, I. B., & Sheth, A. (2003). Context-aware semantic association ranking. In Proceedings of 1st international workshop on semantic web and databases (pp. 33–50).
Aleman-Meza, B., Arpinar, I. B., Ramakrishanan, C., Halaschek, C., Anyanwu, K., & Kochut, K. (2005). Semantic association identification and knowledge discovery for national security applications. Journal of Database Management, 16, 33–53.
Anyanwu, K., & Sheth, A. (2002). The ρ operator: Discovering and ranking associations on the semantic web. SIGMOD Record, 31, 42–47.
Anyanwu, K., & Sheth, A. (2003). P-queries: Enabling querying for semantic associations on the semantic web. In Proceedings of the 12th international world wide web conference (pp. 690–699). New York: ACM.
Anyanwu, K., Maduko, A., & Sheth, A. (2005). SemRank: Ranking complex relationship search results on the semantic web. In Proceedings of the 14th international conference on world wide web (pp. 117–127). New York: ACM.
Boley, H. (2000). Relationships between logic programming and RDF. Lecture Notes In Computer Science, 2112, 201–218.
Boley, H., Tabet, S., & Wagner, G. (2001). Design rationale of RuleML: A markup language for semantic web rules. In Semantic web working symposium (pp. 381–401).
Borodin, A., Roberts, G. O., Rosenthal, J. S., & Tsaparas, P. (2001). Finding authorities and hubs from link structures on the world wide web. In Proceedings of the 10th international conference on world wide web (pp. 415–429). New York: ACM.
Ceri, S., Gottlob, G., & Tanca, L. (1990). Logic programming and databases. New York: Springer.
Chen, Q., & Chu, W. (1989). HILOG: A high-order logic programming language for non-1NF deductive databases. In Proceedings of the international conference on deductive and object-oriented databases (pp. 431–452). Amsterdam: North-Holland/Elsevier.
Christophides, V., Abiteboul, S., Cluet, S., & Scholl, M. (1994). From structured documents to novel query facilities. In Proceedings of the 1994 ACM SIGMOD international conference on management of data (pp. 313–324). New York: ACM.
Christophides, V., Cluet, S., & Moerkotte, G. (1996). Evaluating queries with generalized path expressions. In Proceedings of the 1996 ACM SIGMOD international conference on management of data (pp. 413–422). New York: ACM.
Colmerauer, A. (1985). Prolog in 10 figures. Communications of the ACM, 28, 1296–1310.
Dijkstra, E. W. (1959). A note on two problems in connection with graphs. Numerische Mathematik, 1, 269–271.
Earley, J. (1970). An efficient context-free parsing algorithm. Communications of the ACM, 13, 94–102.
Egghe, L. (Ed.) (2006). Special issue on informetrics. Information Processing and Management, 42, 1405–1656.
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.
Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying online social networks. Journal of Computer-Mediated Communication, 3, 1–27.
Hammer, M., & McLeod, D. (1981). Database description with SDM: A semantic database model. ACM Transactions on Database Systems, 6, 351–386.
Harrison, M. A. (1978). Introduction to formal language theory. Boston: Addison-Wesley.
Hristidis, V., & Papakonstantinou, Y. (2002). DISCOVER: Keyword search in relational databases. In Proceedings of the 28th VLDB conference (pp. 670–681). San Francisco: Kaufmann.
Hull, R., & King, R. (1987). Semantic database modeling: Survey, applications, and research issues. ACM Computing Surveys, 19, 201–260.
Ioannidis, Y. E., & Lashkari, Y. (1994). Incomplete path expressions and their disambiguation. In Proceedings of the 1994 ACM SIGMOD international conference on management of data (pp. 138–149). New York: ACM.
Järvelin, K., Ingwersen, P., & Niemi, T. (2000). A user-oriented interface for generalised informetric analysis based on applying advanced data modelling techniques. Journal of Documentation, 56, 250–278.
Kasami, T. (1967). A note on computing time for recognition of languages generated by linear grammars. Information and Control, 10, 209–214.
Knuth, D. E. (1965). On the translation of languages from left to right. Information Control, 8, 607–639.
Kovalerchuk, B., & Vityaev, E. (2003). Detecting patterns of fraudulent behavior in forensic accounting. In Proceedings of 7th international conference on knowledge-based intelligent information and engineering systems (pp. 502–509). Amsterdam: IOS.
Lewis, P. M., & Stearns, R. (1968). Syntax-directed transduction. Journal of the ACM, 15, 465–488.
Lin, S., & Chalupsky, H. (2003a). Unsupervised link discovery in multi-relational data via rarity analysis. In Proceedings of the 3rd IEEE international conference on data mining (pp. 171–178). Los Alamitos: IEEE Computer Society.
Lin, S., & Chalupsky, H. (2003b). Using unsupervised link discovery methods to find interesting facts and connections in a bibliography dataset. SIGKDD Explorations, 5, 173–178.
Linz, P. (1990). An introduction to formal languages and automata. Lexington: Heath.
Liu, M. (1995). Relationlog: A typed extension to datalog with sets and tuples (extended abstract). In Proceedings of the international symposium on logic programming (pp. 83–97). Cambridge: MIT.
Liu, M. (1998). Relationlog: A typed extension to datalog with sets and tuples. The Journal of Logic Programming, 36, 271–299.
Liu, M. (1999). Deductive database languages: Problems and solutions. ACM Computing Surveys, 31, 27–62.
Maier, D., Ullman, J. D., & Vardi, M. Y. (1984). On the foundations of the universal relation model. ACM Transactions on Database Systems, 9, 283–308.
Manola, F., & Miller, E. (2004). RDF primer, W3C recommendation. http://www.w3.org/TR/rdf-primer/ (current May 3, 2006).
Mitchell, J. C. (1969). The concept and use of social networks. In J. C. Mitchell (Ed.), Social networks in urban situations. Manchester: Bobbs-Merrill.
Mooney, R. J., Melville, P., Tang, L. R., Shavlik, J., de Castro Dutra, I., Page, D., et al. (2002). Relational data mining with inductive logic programming for link discovery. In Proceedings of the national science foundation workshop on next generation data mining. Menlo Park: AAAI/MIT Press.
Naqvi, S., & Tsur, S. (1989). A logical language for data and knowledge bases. New York: Computer Science Press.
Niemi, T., & Jämsen, J. (2007). A query language for discovering semantic associations, Parts I and II. Journal of the American Society for Information Science and Technology, 58, 1559–1568, 1686–1700.
Niemi, T., Junkkari, M., & Järvelin, K. (2002). Relational deductive object-oriented modeling (RDOOM) approach for finding, representing and integrating application-specific concepts. International Journal of Software Engineering and Knowledge Engineering, 12, 415–451.
Niemi, T., Hirvonen, L., & Järvelin, K. (2003). Multidimensional data model and query language for informetrics. Journal of the American Society for Information Science and Technology, 54, 939–951.
Olivé, A., & Teniente, E. (2002). Derived types and taxonomic constraints in conceptual modeling. Information Systems, 27, 391–409.
Sterling, L., & Shapiro, E. (1986). The art of Prolog: Advanced programming techniques. Cambridge: MIT Press.
Tsur, S., & Zaniolo, C. (1986). LDL: A logic-based data language. In Proceedings of the 12th international conference on very large data bases (pp. 33–41). San Francisco: Kaufmann.
Ullman, J. D. (1989). Principles of database and knowledge-base systems. Rockville: Computer Science Press.
Ullman, J. D. (1991). The interface between language theory and database theory. In Theoretical studies in computer science (pp. 133–151). San Diego: Academic.
Wang, J., Chen, Z., Tao, L., Ma, W. Y., & Wenyin, L. (2002). Ranking user’s relevance to a topic through link analysis on web logs. In Proceedings of the 4th international workshop on web information and data management (pp. 49–54). New York: ACM.
Xu, J., & Chen, H. (2004). Fighting organized crimes: Using shortest-path algorithms to identify associations in criminal networks. Decision Support Systems, 38, 473–487.
Younger, D. (1967). Recognition and parsing of context free languages in time n3. Information and Control, 10, 189–208.
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Appendices
Appendix 1: Entity and relationship types in the sample context
base entity types:
person, enterprise, crime, apartment
base association types:
-
motherhood (Mother<female>, Child<person>)
-
fatherhood (Father<male>, Child<person>)
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employment_excl_management (Employee<person>, Enterprise <enterprise>,
-
Salary (number))
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management (Manager<person>, Enterprise<enterprise>, Salary (number),
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RightOfOptions<{yes, no}>)
-
commitment (Person<person>, Crime<crime>)
-
own_apartment (Owner<person>, Apartment<apartment>)
-
rented_apartment (Tenant<person>, Lessor<person>,
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Apartment<apartment>, rent<number>)
base relationship types (other than association types):
-
person_info (Person<person>, FirstName<string>,LastName<string>,
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Sex<{m, f}>)
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crime_type (Crime<crime>, Type<{theft, robbery, homicide,
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white_collar_crime}>)
derived entity types:
-
male, female, suspicious_person
derived association types:
-
parenthood (Parent<person>, Child<person>).
-
co_parenthood (Mother <female>, Father<male>)
-
co_worker_parenthood (Mother<female>, Father<male>, Child<person>)
-
ancestorhood (Ancestor<person>, Descendant<person>)
-
grandparenthood (Grandparent< person>, Grandchild<person>)
-
grandfatherhood (Grandfather<male>, Grandchild<person>)
-
grandmotherhood (Grandmother<female>, Grandchild<person>)
-
employment (Employee<person>, Enterprise<enterprise>)
-
self_employed_entrepreneurship (Entrepreneur<person>, Enterprise<enterprise>)
-
co_workership (CoWorker1<person>, CoWorker2<person>)
-
superiorship (Superior<person>, Employee<person>)
-
complicity (Accomplice1<person>, Accomplice2<person>)
-
common_apartment (Person1<person>, Person2<person>)
-
living (Person< person>, Apartment< apartment>)
Appendix 2: Sample rules
- r 9 :
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male(Person) :- person_info (Person, Firstname, Lastname, m).
- r 10 :
-
female (Person) :- person_info (Person, Firstname, Lastname, f).
- r 11 :
-
suspicious_person (Person) :-
commitment (Person, Crime), crime_type (Crime,robbery).
- r 12 :
-
suspicious_person (Person) :-
commitment (Person,Crime), crime_type (Crime, homicide).
- r 13 :
-
parenthood (Parent,Child) :- motherhood (Parent, Child).
- r 14 :
-
parenthood (Parent, Child) :- fatherhood (Parent, Child).
- r 15 :
-
co_parenthood (Mother, Father) :-
motherhood (Mother, Child), fatherhood (Father, Child).
- r 16 :
-
co_worker_parenthood (Mother, Father, Child) :-
motherhood (Mother, Child), fatherhood (Father, Child),
co_workership (Mother, Father).
- r 17 :
-
ancestorhood (Ancestor, Descendant):-
parenthood (Ancestor, Child), ancestorhood (Child, Descendant).
- r 18 :
-
ancestorhood (Ancestor, Descendant) :- parenthood (Ancestor, Descendant).
- r 19 :
-
grandparenthood (Grandparent, Grandchild) :-
parenthood (Grandparent, Parent), parenthood (Parent, Grandchild).
- r 20 :
-
grandfatherhood (Grandfather, Grandchild):-
fatherhood (Grandfather, Parent), parenthood (Parent, Grandchild).
- r 21 :
-
grandmotherhood (Grandmother, Grandchild) :-
parenthood (Grandmother, Parent), female (Grandmother), parenthood (Parent, Grandchild).
- r 22 :
-
employment (Employee, Enterprise, Salary) :-
employment_excl_management (Employee, Enterprise, Salary).
- r 23 :
-
employment (Employee, Enterprise, Salary) :-
management (Employee, Enterprise, Salary, RightOfOptions).
- r 24 :
-
self_employed_entrepreneurship (Entrepreneur, Enterprise) :-
management (Entrepreneur, Enterprise, Salary1),
\(\neg\) employment_excl_management (Employee, Enterprise, Salary2).
- r 25 :
-
co_workership (CoWorker1, CoWorker2) :-
employment (CoWorker1, Enterprise, Salary1),
employment (CoWorker2, Enterprise, Salary2),
\(\neg\) CoWorker1 = CoWorker2.
- r 26 :
-
superiorship (Superior, Employee) :-
management (Superior, Enterprise, Salary1, RightOfOptions),
employment_excl_management (Employee, Enterprise, Salary2).
- r 27 :
-
complicity (Accomplice1, Accomplice2) :-
commitment (Accomplice1, Crime), commitment (Accomplice2, Crime),
\(\neg\) Accomplice1 = Accomplice2.
- r 28 :
-
common_apartment (Person1, Person2) :-
living (Person1, Apartment), living (Person2, Apartment),
\(\neg\) Person1 = Person2.
- r 29 :
-
living (Person, Apartment) :- own_apartment (Person, Apartment).
- r 30 :
-
living (Person, Apartment) :-
rented_apartment (Person, Lessor, Apartment, Rent).
Appendix 3: Base facts in the sample context
entity facts:
-
person (#p9),..., person (#p23)
-
enterprise (#e1)
-
crime (#c1),..., crime (#c5)
-
apartment (#a1),..., apartment (#a5)
association facts:
-
motherhood (#p10, #p11)
-
motherhood (#p10, #p12)
-
motherhood (#p13, #p9)
-
motherhood (#p13, #p14)
-
motherhood (#p16, #p10)
-
motherhood (#p23, #p22)
-
fatherhood (#p9, #p11)
-
fatherhood (#p9, #p12)
-
fatherhood (#p15, #p13)
-
fatherhood (#p19, #p20)
-
employment_excl_management (#p16, #e1, 2000)
-
employment_excl_management (#p17, #e1, 2500)
-
management (#p15, #e1,4000,yes)
-
commitment (#p9, #c1)
-
commitment (#p18, #c1)
-
commitment (#p18, #c2)
-
commitment (#p19, #c3)
-
commitment (#p21, #c4)
-
commitment (#p21, #c5)
-
commitment (#p22, #c4)
-
commitment (#p22, #c5)
-
own_apartment (#p9, #a1)
-
own_apartment (#p13, #a2)
-
own_apartment (#p14, #a2)
-
own_apartment (#p15, #a4)
-
own_apartment (#p17, #a5)
-
own_apartment (#p18, #a5)
-
rented_apartment (#p19, #p10, #a3, 200)
base relationship facts (other than association facts):
-
person_info (#p9, thomas, selsor, m)
-
person_info (#p10, sonya, selsor, f)
-
person_info (#p11, tina, selsor, f)
-
person_info (#p12, tim, selsor, m)
-
person_info (#p13, ann, selsor, f)
-
person_info (#p14, maria, macpattern, f)
-
person_info (#p15, john, cassius, m)
-
person_info (#p16, cathrine, freeman, f)
-
person_info (#p17, lisa, curley, f)
-
person_info (#p18, sam, suspicious, m)
-
person_info (#p19, richard, risk, m)
-
person_info (#p20, tony, risk, m)
-
person_info (#p21, lars, leery, m)
-
person_info (#p22, robbie, robber, m)
-
person_info (#p23, margareth, robber, f)
-
crime_type (#c1, theft)
-
crime_type (#c2, robbery)
-
crime_type (#c3, homicide)
-
crime_type (#c4, robbery)
-
crime_type (#c5, theft)
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Jämsen, J., Niemi, T. & Järvelin, K. Derived types in semantic association discovery. J Intell Inf Syst 35, 213–244 (2010). https://doi.org/10.1007/s10844-009-0094-7
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DOI: https://doi.org/10.1007/s10844-009-0094-7